CN113382683A - 改善睡眠的系统和方法 - Google Patents

改善睡眠的系统和方法 Download PDF

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CN113382683A
CN113382683A CN201980075272.9A CN201980075272A CN113382683A CN 113382683 A CN113382683 A CN 113382683A CN 201980075272 A CN201980075272 A CN 201980075272A CN 113382683 A CN113382683 A CN 113382683A
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A·波尔托拉金
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Newrock Hengsimont Experiment Co ltd
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Abstract

一种向第二受试者(受者)移植第一受试者(供者)的睡眠状态的方法，所述方法包括：捕获所述第一受试者的由脑活动模式表示的睡眠状态；以及通过在所述第二受试者体内诱导所述脑活动模式来在所述第二受试者体内移植所述第一受试者的所述睡眠状态。

Description

改善睡眠的系统和方法
技术领域
本发明总体上涉及神经调制和神经增强(NE)的领域，并且更具体地涉及用于改善人或动物的睡眠状态的系统和方法。
背景技术
本文引用的每个参考文献和文档出于所有目的，均通过引用以其整体明确地并入本文。
生物方式的时间：生物学中的几乎所有事物都会随着时间的推移而变化。这些变化发生在许多不同时间尺度上，其差异很大。例如，会有随着时间的推移的影响整个群体，而不是单个生物体的演进性变化。演进性变化通常比跨越多年(通常是人类一生)的人类时间尺度慢。活生物体的生物活动的定时和持续时间的更快变化发生在例如，日常生活中的许多重要生物过程中：在人类和动物中，这些变化发生在例如进食、睡眠、交配、冬眠、迁徙、细胞再生中。其它快速变化可以包含神经信号例如，通过突触的传输，所述突触如calyx ofheld，哺乳动物的听觉中枢神经系统中的可以达到高达50Hz的传输频率的特别大的突触。利用募集调制，有效频率可以更高。单个神经冲动可以达到高达每秒一百米(0.06英里)的速度(Kraus,David,《现代生物学概念(Concepts in Modern Biology)》,纽约：Globe Book公司(New York:Globe Book Company),1969:170)。轴突的髓鞘化可以通过分割膜去极化过程来增加传输的速度。
随时间推移的这些变化中的许多变化是重复性的或节律性的并且被描述为某种频率或振荡。时间生物学领域研究了例如活生物体中的此周期性(周期)现象以及其对例如太阳和月球有关的节律的适应性[DeCoursey等人,(2003)]。这些周期也被称为生物节律。在一些情况下已使用了相关术语时变组学和时计组学来描述时间生物学现象中涉及的分子机制或时间生物学的更多定量方面，尤其是需要比较生物体之间的周期的情况下。时间生物学研究包含但不限于比较性解剖学、生理学、遗传学、分子生物学和在生物节律力学内生物体的行为[DeCoursey等人,(2003)]。其它方面包含表观遗传学、发育、繁殖、生态学和进化。
时间生物学中最重要的节律是昼夜节律，在所有这些生物体中通过生理过程示出的大略24小时周期。其由昼夜钟调节。昼夜节律可以被进一步分解成24小时一天期间的常规周期[Nelson RJ.,2005,《行为内分泌学介绍(An Introduction to BehavioralEndocrinology)》马萨诸塞州Sinauer Associates有限公司(Sinauer Associates,Inc.:Massachusetts.)第587页]。所有动物可以根据其活动周期分类：昼行性，其描述了白天期间活跃的生物体；夜行性，其描述了夜晚活跃的生物体；以及黄昏行性，其描述了在黄昏和傍晚时刻期间活跃的动物(例如：白尾鹿、一些蝙蝠)。
虽然昼夜节律被定义为通过内源性过程调节，但其它生物周期可以受外源性信号调节。在一些情况下，多营养系统可以展现出由成员之一的昼夜钟驱动的节律(其也可能受到外部因素的影响或通过外部因素重置)。
还研究了许多其它重要周期，包含：周期长于一天的红外线节律。实例包含：年度或每年周期，所述年度或每年周期支配许多植物和动物的迁移或繁殖周期或人类月经周期；亚昼夜节律，所述亚昼夜节律是短于24小时的周期，如90分钟REM周期、4小时鼻周期或3小时的生长激素产生周期；通常在海洋生活中观察到的潮汐节律，所述潮汐节律遵循从高潮到低潮并且返回的大略12.4小时的转变；农历节律，所述农历节律遵循农历月(29.5天)，其与例如海洋生活有关，因为潮汐的水平是跨农历周期调制的；以及基因振荡–一些基因在一天的某些小时期间比其它时间期间表达更多。
在每个周期内，过程更活跃的时间段被称为峰相[Refinetti,Roberto(2006)《昼夜生理学(Circadian Physiology)》CRC出版社/泰勒和弗朗西斯集团(CRC Press/Taylor&Francis Group.)ISBN 0-8493-2233-2.简明摘要]。当过程不太活跃时，周期处于其槽相或谷相。最高活跃度的特定时刻是峰值或最大；最低点是天底。过程达到多高(或多低)通过幅度测量。
睡眠周期和亚昼夜节律：睡眠和清醒的正常周期表明在具体时间，各个神经系统被激活而其它断开。因此，睡眠神经生物学的关键是了解睡眠的各个阶段。1953年，Nathaniel Kleitman和Eugene Aserinksy使用来自正常人受试者的脑电图(EEG)记录示出了睡眠包括以特征顺序发生的不同阶段。
人类在就寝之后的第一小时内等彼此接替的阶段中陷入睡眠中。这些特征阶段主要由脑电图标准定义。最初，在“困倦”期间，脑电图(EEG)的频谱朝较低值偏移，并且皮层波的幅度略微增加。此困倦阶段被称为阶段I睡眠，最终屈服于轻度睡眠或阶段II睡眠，所述轻度睡眠或阶段II睡眠的特征在于EEG波的频率进一步降低并且其幅度增加，伴有被称为睡眠锭的间歇性高频率尖峰聚类。睡眠锭是约10–12Hz的周期性活动爆发，其通常持续1秒或2秒并且由于丘脑神经元与皮层神经元之间的相互作用而产生。在表示中度至深度睡眠的阶段III睡眠中，锭的数量减少，而低频率波的幅度仍更多地增加。在最深层的睡眠(阶段IV睡眠)中，主要EEG活动由被称为δ波(此相位的睡眠所称为的特征慢波)的低频率(1-4Hz)、高幅度波动组成。从困倦到深阶段IV睡眠的整个顺序通常需要约一个小时。
这四个SS(SS)称为非快速眼睛移动(非-REM)睡眠，并且其最突出的特征是慢波(阶段IV)睡眠。使人们从慢波睡眠中苏醒是最困难的，因此，其被视为是最深睡眠阶段。然而，在一段时间的慢波睡眠之后，EEG记录示出，睡眠阶段逆转以达到相当不同的状态，其被称为快速眼睛移动或REM睡眠。在REM睡眠中，EEG记录与清醒状态的EEG记录非常相似。此模式很奇特：做梦者的大脑变得高度活跃，而身体的肌肉无力，并且呼吸速率和心脏速率变得无定。REM睡眠约10分钟之后，大脑典型地循环回到非REM SS。慢波睡眠通常再次出现在此连续循环的第二周期中而不是夜晚的其余部分期间。平均而言，出现了REM睡眠的四个另外的阶段，每个周期都比之前的周期持续时间更长。每个晚上经历的典型8小时睡眠实际包括在非REM睡眠与REM睡眠之间交替的几个周期，脑在大多数此假设的休眠安稳时间期间非常活跃。由于不清楚的原因，每天REM睡眠的量从出生时的约8小时减少到20岁时的2小时，到70岁时的仅约45分钟。
入睡：当入睡时，一系列高度统筹的事件使大脑在上述阶段中睡眠。从技术上讲，睡眠开始于产生慢波睡眠(SWS)的脑区。已经示出，两个细胞组-下丘脑中的腹侧视前核和脑干中的面旁分区-参与促进SWS。当这些细胞被激活时，其会触发神志丧失。SWS之后，REM睡眠开始。尽管对其生物化学和神经生物学的了解不断提高，但REM睡眠的目的仍是生物学谜团。已经示出，脑干中的被称为蓝藻核的小细胞组控制REM睡眠。当这些细胞变得受伤或患病时，人们不会经历与REM睡眠相关联的肌肉无力，这可能导致REM睡眠行为障碍，这是患病者剧烈地做梦的严重病状。
神经相关性：睡眠状态的神经相关性是电神经生物学状态或脑的一些生物物理子系统所设想的状态，所述状态的存在必定且有规律地与此特定睡眠状态相关。归于思想的所有性质，包含神志、情感和欲望都被视为具有直接神经相关性。出于目的，睡眠状态的神经相关性可以被定义为对应于给定SS的神经元振荡的最小集合。神经科学家使用经验方法来发现SS的神经相关性。
精神状态：精神状态是受试者所处的思想状态。一些精神状态是纯粹且明确的，而人类能够具有精神表示的组合的复杂状态，所述状态在其纯粹状态下可能具有矛盾特性。存在受试者所具有的几种范式思想状态：爱、恨、愉悦、恐惧和痛苦。精神状态还可以包含清醒状态、睡眠状态、心流(或处于“分区”)和情绪(精神状态)。精神状态是与思维和感觉相对应的假设状态并且由精神表示的聚集组成。精神状态与情感有关，尽管它也与认知过程有关。由于精神状态本身是复杂的并且潜在地具有不一致的属性，因此很难或不可能通过外部分析(除了自我报告之外)对精神状态进行清晰的解释。但是，一些研究报告了，精神状态或思维过程的某些属性实际上可以通过被动监测来确定，如具有某种程度的统计可靠性的EEG或fMRI。在大多数研究中，表征精神状态是终点，并且在统计分类或语义标记之后，原始信号被取代。剩余的信号能量被视为噪声。当前技术不准许基于精神状态的神经相关性对整个范围的精神状态进行精确的抽象编码或表征。
脑：脑是中枢神经系统的关键部分，封闭在头骨中。在人类和更通常的哺乳动物中，脑控制自主过程以及认知过程两者。脑(以及在较小程度上脊髓)控制身体的所有意志功能并且解释来自外界的信息。脑控制智力、记忆、情感、言语、思维、移动和创造力。中枢神经系统还控制自主功能以及许多稳态和反射动作，如呼吸、心律等。人脑由大脑、小脑和脑干组成。脑干包含中脑、桥脑和延髓。有时包含间脑，前脑的尾部部分。
脑由神经元、神经胶质细胞(又名胶质细胞)和连接网络中的其它细胞类型构成，所述连接网络整合感觉输入、控制移动、促进学习和记忆、激活和表达情感并且控制所有其它行为和认知功能。神经元主要通过电化学脉冲通信，所述电化学脉冲在脑区内的连接细胞之间以及脑区之间传输信号。因此，非侵入地捕获和复制与认知状态相关联的神经活动的期望已成为行为和认知神经科学家关注的主题。
现在，技术进步允许以多种空间和时间尺度来非侵入性地记录来自脑的大量信息。实例包含使用放置在头皮或脑内的多通道电极阵列的脑电图(“EEG”)数据、脑磁图(“MEG”)、磁共振成像(“MRI”)、使用功能性磁共振成像(“fMRI”)的功能性数据、正电子发射层析成像(“PET”)、近红外光谱(“NIRS”)、单光子发射计算机层析成像(“SPECT”)等。
也已经开发了非侵入性神经调制技术，所述非侵入性神经调制技术可以调制神经活动的模式并且由此引起行为、认知状态、感知和运动输出的改变。整合非侵入性测量和神经调制技术以从神经活动中标识和移植脑状态对于临床疗法经是非常有价值的，如脑刺激和通常尝试治疗认知障碍的相关技术。
脑干通过颅神经提供对面部和颈部的主要运动和感觉神经支配。在十二对颅神经中，十对来自脑干。这是脑的极为重要的部分，因为从脑的主要部分到身体的其余部分的运动和感觉系统的神经连接都经过脑干。这包含皮层脊髓束(运动)、后柱-内侧丘系通路(精细触摸、振动感觉和本体感受)和脊髓丘脑束(疼痛、温度、瘙痒和粗触摸)。脑干还在心脏和呼吸功能的调节方面起重要作用。其也调节中枢神经系统并且在维持神志和调制睡眠周期中是关键的。脑干具有许多基本功能，包含控制心脏速率、呼吸、睡眠和进食。
头骨的功能是保护脆弱的脑组织免受损伤。头骨由八块融合的骨骼组成：额骨、两个顶骨、两个颞骨、蝶骨、枕骨和筛骨。面部由14对骨骼形成，包含上颌骨、颧骨、鼻骨、腭骨、泪骨、下鼻骨、下颌骨和犁骨。骨质头骨通过硬脑膜(膜状器官)与大脑分开，所述硬脑膜进而含有脑脊髓液。典型地，脑的皮层表面不会经受来自头骨的局部压力。因此，头骨为电访问脑功能产生了障碍，并且在健康人类中，突破硬脑膜以进入脑是高度不赞成的。结果是通过硬脑膜、脑脊髓液、头骨、头皮、皮肤附件(例如头发)对脑活动的电读数进行滤波，从而导致潜在空间分辨率和从脑发出的信号的幅度损失。尽管由脑电活动产生的磁场是可访问的，但使用可行传感器的空间分辨率也受到限制。
大脑是脑的最大部分并且由左半球和右半球构成。其执行更高功能，如解释来自感官的输入以及言语、推理、情感、学习和对移动的精细控制。大脑的表面具有折叠的外观，其被称为皮层。人皮层含有约70％的神经细胞(神经元)并且呈现灰颜色的外观(灰质)。皮层下方是构成白质的神经元之间的长连接纤维，其被称为轴突。
小脑位于大脑和脑干后面。其协调肌肉移动，帮助维持平衡和姿势。小脑还可能涉及一些认知功能，如关注和语言以及调节恐惧和愉悦响应。有大量证据表明，小脑在一些类型的运动学习中起着至关重要的作用。小脑最明显起作用的任务是需要对动作执行的方式进行精细调整的那些。关于学习是否发生在小脑自身内，或者其是否仅用于提供促进其它脑结构中的学习的信号，存在争议。小脑还在睡眠和长期记忆形成中起重要作用。
脑通过脊髓和十二对颅神经与身体通信。十二对颅神经中控制听觉、眼睛移动、面部感觉、味道、吞咽以及面部、颈部、肩膀和舌头肌肉的移动的十对起源于脑干。气味和视觉的颅神经源于大脑。神经元是神经系统的基本单元，所述神经系统包括自主神经系统和中枢神经系统。
脑的右半球和左半球通过由被称为胼胝体的纤维组成的结构连接。每个半球控制身体的相反侧。右眼向左半球发送视觉信号，并且反之亦然。然而，右耳向右半球发送信号，并且左耳向左半球发送信号。并非半球的所有功能都是共享的。例如，言语仅仅在左半球中处理。大脑半球具有不同的结构，所述结构将脑分为叶。每个半球具有四个叶：额叶、颞叶、顶叶和枕叶。脑的叶之间以及左半球与右半球之间存在非常复杂的关系：额叶控制判断、计划、问题解决、行为、情感、性格、言语、自我意识、专注力、智力、身体移动；颞叶控制对语言的理解、记忆、组织和听力；顶叶控制：对语言的解释；来自视觉、听觉、感觉和运动的输入；温度、疼痛、触觉信号、记忆、空间和视觉感知；并且枕叶解释视觉输入(移动、光、颜色)。
脑结构和脑结构内的特定区包含但不限于菱脑结构(例如，末脑结构(例如，延髓、延髓锥体、橄榄体、下橄榄核、呼吸中枢、楔束核、薄束核、插层核、髓脑神经核、下泌涎核、疑核、迷走神经背核、舌下核、孤束核等)、后脑结构(例如，桥脑、桥脑脑神经核、三叉神经感觉核(V)的主核或脑桥核、三叉神经(V)的运动核、外旋神经核(VI)、面神经核(VII)、前庭耳蜗核(前庭核和耳蜗核)(VIII)、上唾液核、桥脑盖骨、呼吸中枢、呼吸调整中枢、长吸中枢、桥脑排尿中枢(巴灵顿核(Barrington's nucleus))、蓝斑核、脚桥核、背外侧被盖核、被盖桥脑网状核、上橄榄核复合体、中线旁桥脑网状形成、小脑脚、小脑上脚、小脑中脚、小脑下脚、第四脑室、小脑、小脑蚓部、小脑半球、前叶、后叶、绒球小结叶、小脑核、顶核、间位核、球状核、拴状核、齿状核等))、中脑结构(例如，顶盖、四叠体、下丘、上丘、前顶盖、被盖、水管周灰质、臂旁区、内侧臂旁核、外侧臂旁核、臂旁下核(Kolliker-Fuse核)、内侧纵束的吻侧间隙核、中脑网状形成、背侧缝核、红核、腹侧被盖区、黑质、致密部、网状部、脚间核、大脑脚(Cerebral peduncle)、大脑脚(Cms cerebri)、中脑脑神经核、动眼神经核(III)、滑车神经核(IV)、中脑水管(大脑导水管、中脑导水管)等)、前脑结构(例如，间脑、上丘脑结构(例如，松果体、缰核、髓纹、视丘带等)、第三脑室、丘脑结构(例如，前核群、前腹侧核(也就是腹前核)、前背侧核、前内侧核、内侧核群、内侧背核、中线核群、带旁核、连接核、菱形核、板内核群、中央内侧核(Centromedial nucleus)、束旁核、中央旁核、中央外侧核、中央内侧核(Central medial nucleus)、外侧核群、外侧背核、外侧后核、枕、腹侧核群、腹侧前核、腹侧外侧核、腹侧后核、腹侧后外侧核、后丘脑、内侧膝状体、外侧膝状体、丘脑网状核等)、下丘脑结构(例如，前、中侧区、视前区的部分、中侧视前核、视交叉上核、室旁核、视上核(主)、下丘脑前核、外侧区、视前区的部分、外侧视前核、外侧核的前部部分、视上核的部分、视前区的其它核、内侧視前核、视前室周核、结节、中侧区、背内侧下丘脑核、腹内侧核、弓状核、外侧区、外侧核的结节部分、外侧隆起核、后中侧区、乳状核(乳状体的部分)、视交叉、穹窿下器官、室周核、腦垂腺柄、灰结节、结节核、结节乳头核、结节区域、乳状核等)、底丘脑结构(例如，丘脑核、未定带等)、垂腺体结构(例如，神经垂体、中间部(垂体中叶)、腺垂体等)、端脑结构、白质结构(例如，放射冠、内囊、外囊、最外囊、弓状束、钩束、穿通纤维等)、皮层下结构(例如，海马结构(内侧颞叶)、齿状回、海马角(CA场)、海马角区1、海马角区2、海马角区3、海马角区4、杏仁体(边缘系统)(边缘叶)、中央核(自住神经系统)、内侧核(辅助嗅觉系统)、皮层核和基底内侧核(主嗅觉系统)、外侧[消歧需要]和基侧核(额颞叶皮层系统)、屏状体、基底神经节、纹状体、背侧纹状体(亦为新纹状体)、壳核、尾状核、腹侧纹状体、伏核、嗅结节、苍白球(与壳核形成豆状核)、丘脑下核、基底前脑、前穿质、无名质、基底核、Broca斜角带、内侧中隔核等)、嗅脑结构(例如，嗅球、梨状皮层、前嗅核、嗅束、前连合、钩回等)、大脑皮层结构(例如，前叶、皮层、初级运动皮层(中央前回M1)、辅助运动皮层、前运动皮层、前额叶皮层、脑回、额上回、额中回、额下回、布罗德曼区(Brodmann areas)：4、6、8、9、10、11、12、24、25、32、33、44、45、46、47、顶叶、皮层、初级体感皮层(S1)、次级体感皮层(S2)、后顶叶皮层、脑回、后中央回(主要体觉区)、其它、楔前叶、布罗德曼区1、2、3(主要体觉区)；5、7、23、26、29、31、39、40、枕叶、皮层、初级视觉皮层(V1)、V2、V3、V4、V5/MT、脑回、外侧枕回、楔片、布罗德曼区17(V1、初级视觉皮层)；18、19、颞叶、皮层、初级听觉皮层(A1)、次级听觉皮层(A2)、下颞皮层、后下颞皮层、上颞回、中颞回、下颞回、内嗅皮层、鼻周皮层、海马旁回、梭状回、布罗德曼区：9、20、21、22、27、34、35、36、37、38、41、42、内侧上颞区(MST)、岛叶皮层、扣带皮层、前扣带、后扣带、后皮层皮层、灰被、膝下区25、布罗德曼区23、24、26、29、30(后皮层区)、31、32等))。
神经元：神经元是接收、处理和传输信息并且基于所述信息通过电信号和化学信号向其它神经元、肌肉或腺体发送信号的可电激励细胞。神经元之间的这些信号通过被称为突触的专门连接发生。神经元可以彼此连接以形成神经网络。神经元的基本目的是接收进入信息并且基于所述信息向其它神经元、肌肉或腺体发送信号。神经元被设计成跨生理上长的距离快速发送信号。其使用被称为神经冲动或动作电位的电信号进行此操作。当神经冲动达到神经元末端时，其会触发化学品或神经递质的释放。神经递质跨细胞之间的短间隙(突触)快速行进并且起作用与向邻近细胞发信号。参见www.biologyreference.com/Mo-Nu/Neuron.html#ixzz5AVxCuM5a.
神经元可以通过突触接收来自其它神经元的数千个输入。突触整合是神经元借此在产生神经冲动或动作电位之前整合这些输入的机制。突触输入影响神经元输出的能力通过多个因素确定：突触输入所产生的电位的大小、形状和相对定时；目标神经元的几何结构；突触输入在所述结构内的物理位置；以及电压-门控的通道在神经元膜的不同区域中的表达。
神经元在被称为突触的专门接合点处从许多其它细胞中接收信息，并且向许多其它细胞发送信息。突触整合是单独神经元处理其突触输入并且将所述突触输入转化成输出信号的计算过程。突触电位在神经递质与树突状膜结合并且打开树突状膜中的配体-操作的通道时发生，从而允许离子根据其电化学梯度往来于细胞移动。突触电位可以是激励性的或抑制性的，这取决于离子移动的方向和电荷。动作电位在神经元的总突触输入达到去极化的阈值水平并且触发电压-门控的离子通道的再生打开时发生。突触电位通常是短暂且小的幅度，因此通常需要时间上的输入的总和(时间总和)或来自多个突触输入的总和(空间总和)才能达到动作电位激发阈值。
存在两种类型的突触：电突触和化学突触。电突触是由间隙接合点介导的两个细胞之间的直接电偶联，所述间隙接合点是由连接蛋白构建的孔，所述电突触本质上导致在两个细胞之间传递梯度电位(可以是去极化的或超极化的)。电突触非常迅速(无突触延迟)。其是被动过程，其中信号可能随着距离而降级并且可能不会产生大到足以启动突触后细胞中的动作电位的去极化。电突触是双向的，即，突触后细胞实际上可以向突触前细胞发送消息。
化学突触是两个细胞之间通过神经递质、配体或电压门控的通道、受体的偶联。其受膜的任一侧上的离子的浓度和类型的影响。在神经递质之中，谷氨酸、钠、钾和钙是带正电的。GABA和氯化物是带负电的。神经递质接合点为药理干预提供了机会，并且许多不同的药物，包含非法药物在突触处起作用。
激励性突触后电位(EPSP)是使突触后神经元更有可能激发动作电位的突触后电位。突触后神经元的膜中的电荷(超极化)是由抑制性神经递质从突触前细胞到突触后受体的结合引起的。这使得突触后神经元更难以生成动作电位。突触后神经元的膜中的电变化(去极化)是由激励性神经递质从突触前细胞到突触后受体的结合引起的。这使得突触后神经元更可能生成动作电位。例如在使用谷氨酸作为受体的神经元突触中，受体打开可非选择性地渗透阳离子的离子通道。当这些谷氨酸受体被激活时，Na+和K+两者都跨突触后膜流动。突触后电流的反向电位(Erev)为大约0mV。神经元的静息电位为大约-60mV。产生的EPSP将对突触后膜电位去极化，使其朝向0mV。
抑制性突触后电位(IPSP)是使突触后神经元不太可能生成动作电位的一种类型的突触电位。抑制性突触后动作的实例是使用γ-氨基丁酸(GABA)作为其递质的神经元突触。在此类突触处，GABA受体典型地打开可选择性地渗透Cl-的通道。当这些通道打开时，带负电的氯离子可以跨膜流动。突触后神经元的静息电位为-60mV，并且动作电位阈值为-40mV。此突触处的递质释放将抑制突触后细胞。由于ECI比动作电位阈值更具负性，例如，-70mV，因此其降低了突触后细胞将激发动作电位的可能性。
一些类型的神经递质，如谷氨酸持续地产生EPSP。其它神经递质，如GABA，持续地产生IPSP。动作电位持续约一毫秒(1msec)。相反，EPSP和IPSP可以持续长达5到10msec。这允许一种突触后电位的作用建立在下一个之上，依此类推。
膜渗漏以及较小程度上的电位本身可能会受外部电场和磁场的影响。这些场可以如通过植入的电极，或者不太具体地如通过经颅刺激局灶地生成。经颅刺激可以是阈下的或阈上的。在前一种情况下，外部刺激起到调制静息膜电位的作用，使得神经更多或更少地为可激励的。此刺激可以是直流电或交流电。在后一种情况下，这将倾向于将神经元去极化与信号同步。超阈值刺激可以是疼痛的(至少因为刺激会直接激励疼痛的神经元)并且必须进行脉冲。由于这与电惊厥疗法具有对应性，因此很少使用超阈值经颅刺激。
许多神经递质是已知的，影响这些化合物的药物干预和疗法也是已知的。典型地，主要神经递质是小单胺分子，如多巴胺、肾上腺素、去甲肾上腺素、血清素、GABA、组胺等以及乙酰胆碱。另外，神经递质还包含氨基酸、如一氧化氮、一氧化碳、二氧化碳和硫化氢等气体分子以及肽。这些分子的存在、代谢和调制可能影响学习和记忆。神经递质前体的供应、氧化和精神压力状况的控制以及对学习和记忆相关的脑化学的其它影响可以用于促进记忆、学习和学习适应性转移。
神经肽以及其相应受体广泛分布于整个哺乳动物中枢神经系统。在学习和记忆过程期间，除了结构突触重塑之外，还观察到分子和代谢水平的变化连同神经递质以及神经肽合成和释放的改变。尽管普遍认为脑胆碱能神经传递在与学习和记忆有关的过程中起着至关重要的作用，但众所周知的是，这些功能受巨大数量的神经肽和非肽分子的影响。精氨酸加压素(AVP)、催产素、血管紧张素II、胰岛素、生长因子、血清素(5-HT)、黑色素浓集激素、组胺、蛙皮素和胃泌素释放肽(GRP)、胰高血糖素样肽1(GLP-1)、胆囊收缩素(CCK)、多巴胺、促肾上腺皮层激素释放因子(CRF)对学习和记忆具有调制作用。在这些肽之中，CCK、5-HT和CRF在应激条件下的记忆过程调制中起关键作用。CRF被视为参与物理和情感应力两者的主要神经肽，其在应力期间的保护作用可能通过激活下丘脑-垂体(HPA)。已经提出了肽CCK来促进记忆处理，并且在应力暴露下观察到下丘脑中的CCK样免疫反应性，这表明CCK可以参与应激反应和应力诱导的记忆功能障碍的中央控制。另一方面，5-HT似乎在涉及高认知需求的行为中起作用，并且应力暴露会激活各个脑区域中的血清素能系统。
精神状态：一些研究报告了，精神状态或思维过程的某些属性实际上可以通过被动监测来确定，如具有某种程度的统计可靠性的EEG。在大多数研究中，表征精神状态是终点，并且在统计学上分类或语义标记之后，原始信号被取代，并且剩余信号能量被视为噪声。
神经相关性：精神状态的神经相关性是电神经生物学状态或脑的一些生物物理子系统所设想的状态，所述状态的存在必定且有规律地与此特定精神状态相关。归于en.wikipedia.org/wiki/Mind的所有性质，包含神志、情感和欲望被视为具有直接神经相关性。精神状态的神经相关性可以被视为对应于给定精神状态的神经元振荡的最小集合。神经科学家使用经验方法来发现客观精神状态的神经相关性。
脑波：所有思维、情感和行为的根源是脑内的神经元之间的通信，即中枢神经系统的节律性或重复性神经活动。振荡可以由单个神经元或者由来自彼此传达的神经元群的同步电脉冲产生。神经元之间的相互作用可能引起与单独神经元的激发频率不同的频率的振荡。大量神经元的同步活动会产生宏观振荡，这可以在脑电图中观察到。所述振荡被分为用于描述其声称的功能或功能关系的带宽。观察到脑中的振荡活动广泛在组织的不同水平上，并且所述振荡活动被视为在处理神经信息中起关键作用。许多实验研究支持神经振荡的功能作用。然而，仍未确定统一的解释。神经振荡和同步与许多认知功能相联系，如信息转移、感知、运动控制和记忆。脑电图(EEG)信号相对容易且安全地获取，具有长的分析历史，并且可以具有高维度，例如高达128个或256个单独记录电极。尽管每个电极中表示的信息并不彼此独立，并且信号中的噪声很高，但迄今为止，仍存在可通过尚未完全表征的此类信号获得的许多信息。
已在通过大神经元组(主要通过EEG)生成的神经活动中广泛研究了脑波。通常，EEG信号显示出特定频带中的振荡活动(神经元组定期同步激活)：α(7.5–12.5Hz)，其可以在放松的清醒期间从枕叶中检测到，并且在眼睛关闭时会增加；δ(1-4Hz)、θ(4-8Hz)、β(13-30Hz)、低γ(30-70Hz)和高γ(70-150Hz)频带，其中更快节律，如γ活动与认知处理相联系。较高频率意味着多个神经元组以并联或串联或两者的方式协调激发，因为单独神经元不以100Hz的速率激发。特定特性的神经振荡与认知状态，如意识和神志以及不同的SS相联系。
奈奎斯特定理(Nyquist Theorem)指出，可以准确地表示的最高频率是采样速率的一半。实际地，采样速率应比信号的最高频率高十倍。参见www.slideshare.net/ertvk/eeg-examples)。虽然EEG信号是很大程度上带受限的，但叠加的噪声可以不是。进一步地，EEG信号本身表示来自独立地激发的大量神经元的组分。因此，大带宽信号采集可能具有效用。
有用的类比是将脑波视为音符。如交响乐一样，较高和较低频率通过谐波彼此联系且相干，尤其是当认为神经元不仅可以基于转变还可以基于相位延迟来协调时也是如此。在组织的所有水平上，在整个中枢神经系统中都观察到振荡活动。主导神经元振荡频率与相应精神状态相关联。
脑波的功能是宽范围的并且针对不同类型的振荡活动而变化。神经振荡还在许多神经病症中也起着重要作用。
在标准EEG记录实践中，将19个记录电极均匀地放置在头皮上(国际10-20系统)。另外，需要一个或两个参考电极(通常放置在耳垂上)和接地电极(通常放置在鼻子上以为放大器提供参考电压)。然而，除非通过计算机算法进行补充以将原始EEG数据减少为可管理形式，否则另外的电极可能会添加最少的有用信息。当采用大量电极时，可以相对于所有电位的平均(公共平均参考)测量每个位置处的电位，这通常在无限远处提供对电位的良好的估计。当电极覆盖稀疏(可能少于64个电极)时，公共平均参考不合适。参见Paul L.Nunez和Ramesh Srinivasan(2007)“脑电图(Electroencephalogram)”《学者百科(Scholarpedia)》2(2):1348,scholarpedia.org/article/Electroencephalogram。偶极子定位算法可用于确定EEG中的空间发射模式。
头皮电位可以表示为整个大脑之上每单位体积的偶极矩的体积积分，前提是P(r,t)是一般定义的而不是用柱状术语定义的。对于主导皮层源的重要情况，头皮电位可以通过对皮层体积的以下积分来近似Θ,VS(r,t)＝∫∫∫ΘG(r,r')·P(r',t)dΘ(r')。如果体积元素dΘ(r')是根据皮层柱定义的，则体积积分可以减小到折叠的皮层表面之上的积分。头皮电位的时间相关性是脑中所有偶极子时间变化的加权总和，但是深偶极子体积的贡献通常可忽略不计。向量格林函数(vector Green's function)G(r,r')含有关于头部体积导体的所有几何和导电信息以及因此对积分的加权。因此，格林函数的每个标量分量实质上是每个源分量与头皮位置之间的逆电距离。对于源在恒定电导率的无限介质中的理想情况，电距离等于几何距离。格林函数解释了组织的有限空间范围以及其不均匀性和各向异性。EEG的正向问题由选择头部模型以提供G(r,r')以及对某些设想的源分布执行积分组成。逆问题由使用记录的头皮电位分布VS(r,t)加上对P(r,t)的一些约束(通常的设想)来找到最佳拟合的源分布P(r,t)组成。由于逆问题没有唯一的解，因此任何逆解都严格取决于所选约束，例如，仅一个或两个隔离的源、局限于皮层的分布的源或空间和时间平滑度标准。高分辨率EEG使用实验性头皮电位VS(r,t)来预测硬脑膜表面(围绕大脑皮层的未折叠的膜)上的电位VD(r,t)。这可以使用头部模型格林函数G(r,r')或通过利用球形或3D样条线估计表面拉普拉斯(surface Laplacian)。这两种方法典型地提供非常相似的硬脑膜电位VD(r,t)；对硬脑膜电位分布的估计唯一地受头部模型、电极密度和噪声问题的影响。
在EEG记录系统中，每个电极都连接到差分放大器的一个输入(每对电极一个放大器)；公共系统参考电极(或合成参考)连接到每个差分放大器的另一个输入。这些放大器放大有源电极与参考之间的电压(典型地1,000-100,000倍或60-100dB的电压增益)。经放大的信号在经过抗混叠滤波器之后，通过模数转化器被数字化。在临床头皮EEG中，模数采样通常发生在256-512Hz处；在某些研究应用中，使用高达20kHz的采样速率。EEG信号可以利用如OpenBCI等开源硬件来捕获，并且所述信号可以通过免费可用的EEG软件，如EEGLAB或神经生理生物标志物工具箱来处理。典型成人EEG信号的振幅在从头皮测量时为约10μV到100μV并且在从硬脑膜下电极测量时为约10-20mV。
δ波(en.wikipedia.org/wiki/Delta_wave)是高达4Hz的频率范围。其振幅往往最高并且波最慢。在NREM中，其通常存在于成人中(en.wikipedia.org/wiki/NREM)。其通常还存在于婴儿中。其可以与皮层下病变一起局灶地发生并且与弥漫性病变、代谢性脑病、脑积水或深中线病变一起以普遍分布发生。其通常在成人中在前部最突出(例如FIRDA-额间歇性节律性δ)并且在儿童中在后部最突出(例如OIRDA-枕间歇性节律性δ)。
θ是从4Hz到7Hz的频率范围。θ通常存在于幼儿中。这可能存在于较大儿童和成人的困倦或醒觉时；其还可以存在于冥想时。所述年龄的过多θ表示异常活动。其可以被视为局灶性皮层下病变的局灶性病变；其可能在弥漫性病症或代谢性脑病或深中线病症或脑积水的一些情况下以普遍分布存在。相反，此范围与放松、冥想和创造性状态的报告相关联。
α是从7Hz到14Hz的频率范围。这是在“后部基本节律”(也被称为“后部主导节律”或“后部α节律”)，存在于头部两侧的后部区域中，其幅度在主导侧更高。其随着眼睛闭合以及随着放松出现并且随着眼睛睁开或精神努勉而衰减。在幼儿中，后部基本节律实际上慢于8Hz(因此在技术上处于θ范围内)。除了后部基本节律之外，还存在如感觉运动等其它正常α节律或当手和手臂空闲时出现的μ节律(对侧感觉和运动皮层区的α活动)以及“第三节律”(颞叶或额叶中的α活动)。α可能是异常的；例如，具有在昏迷中出现的弥漫性α并且对外部刺激无反应的EEG被称为“α昏迷”。
β是从15Hz到约30Hz的频率范围。其通常在两侧以对称分布存在并且在前部最明显。β活动与运动行为密切联系并且常在活跃移动期间衰减。具有多个且不同频率的低幅度β通常与活跃、忙碌或焦虑的思维和活跃的注意力相关联。主导组的频率的节律性β与各种病理学相关联，如Dup15q综合征和药物作用，尤其是苯二氮卓类。在皮层损伤的区中，其可能不存在或减少。其是警觉的或焦虑的或其眼睛睁开的患者的主导节律。
γ是大约30-100Hz的频率范围。Γ节律被视为表示不同神经元群体一起结合成用于执行某种认知或运动功能的网络。
μ范围是8-13Hz并且与其它频率部分地重叠。其反映了运动神经元在静息状态下的同步激发。μ抑制被视为反映运动镜像神经元系统，因为当观察到动作时，模式消失，这可能是因为正常神经元系统和镜像神经元系统“不同步”并且彼此干扰。(en.wikipedia.org/wiki/Electroencephalography)。
表1
Figure BDA0003065971300000131
Figure BDA0003065971300000141
EEG和qEEG：EEG电极将主要检测仅在其之下的脑区域中的神经元活动。然而，电极接收来自数千个神经元的活动。例如，一平方毫米的皮层表面具有多于100,000个神经元。只有当区域的输入与同时发生的电活动同步时，EEG中的简单周期性波形才变得可区别。可以将与特定脑波相关联的时间模式数字化并且编码在非瞬态存储器中，并且体现在计算机软件中或由计算机软件引用。
EEG(脑电图)和MEG(脑磁图)是用于监测脑电活动的可用技术。每一个通常具有足够的时间分辨率来跟踪脑电活动的动态改变。脑电图(EEG)和定量脑电图(qEEG)是电生理监测方法，其分析脑的电活动以测量并显示与认知状态和/或诊断信息相对应的模式。其典型地是非侵入性的，其中电极放置在头皮上，但是在一些情况下也使用侵入性电极。EEG信号可以由通常被称为“脑可穿戴设备”的移动装置捕获和分析。当今市场易于获得各种“大脑可穿戴设备”。EEG可以利用非侵入性方法获得，在所述非侵入性方法中，利用附接到人的头皮的各种电极记录了脑电位的聚合振荡。大多数EEG信号源于脑的外层(大脑皮层)，被视为在很大程度上负责的思维、情感和行为。皮层突触动作生成在10到100毫秒范围内变化的电信号。经皮EEG信号受以下的限制：围绕脑的头骨的相对绝缘性质、脑脊髓液和脑组织的电导率、单个细胞电活动的相对低的幅度以及细胞电流与电极之间的距离。EEG的特征在于：(1)电压；(2)频率；(3)空间位置；(4)半球间对称性；(5)反应性(与状态变化的反应)；(6)波形发生的特性(随机、连续、持续)；以及(7)瞬时时间的形态。EEG可以分为两个主要类别。在特定感觉刺激不存在的情况下发生的自发性EEG和与如反复闪光灯、听觉音调、手指压力或轻度电击等感觉刺激相关联的诱发电位(EP)。后者通过例如时间平均来记录，以去除自发性EEG的影响。非感觉触发电位也是已知的。EP典型地是与触发器时间同步的并且因此具有组织原则。事件相关电位(ERP)提供了在广泛的认知范例中认知事件与脑电活动之间直接联系的证据。通常认为，ERP是一组离散刺激诱发的脑事件的结果。事件相关电位(ERP)以与EP相同的方式记录，但发生在离刺激较长的潜伏期内，并且更多地与内源性脑状态相关联。
通常，对单独细胞去极化或小组具有足够灵敏度的磁传感器是超导量子干扰装置(SQIUD)，其需要在液氮温度(高温超导体，HTS)或液氦温度(低温超导体(LTS))下进行低温温度操作。然而，目前研究示出了室温超导体(20C)的可能可行性。磁感测的优势是，由于源的偶极子性质，具有更好的潜在体积局部化；然而，由于此增加的信息，信号分析的复杂性增加。
通常，检测到的电磁信号表示动作电位，即神经细胞对去极化的超过阈值的自动响应，所述自动响应短暂地打开传导通道。细胞具有试图维持去极化状态的离子泵。一旦被触发，动作电位就沿二维的膜传播，从而引起短暂高水平的去极化离子流。去极化之后存在沉寂期，所述沉寂期通常防止单个细胞内的振荡。由于外显子从神经元的主体延伸，因此动作电位典型地将沿轴突的长度行进，所述轴突终止于与另一细胞的突触。在细胞之间的直接电连接发生的同时，通常轴突将神经递质化合物释放到突触中，这导致靶细胞的去极化或超极化。实际上，结果还可能是激素或肽的释放，这可能具有局部或更远的作用。
外部可检测到的电场往往不包含作为低频信号的信号，如静态水平的极化或动作电位之间的累积去极化或超极化作用。在有髓束中，在分段处流动的电流往往较小，并且因此来自单独细胞的信号小。因此，最大信号分量来自突触和细胞体。在大脑和小脑中，这些结构主要在皮层中，所述皮层在很大程度上接近头骨，使得脑电图可用，因为其提供了基于电极位置的空间辨别。然而，深信号衰减并且被不良地定位。脑磁图检测从电流而不是电压变化得到的偶极子。在短距离内径向或球形对称电流流动的情况下，偶极子将趋于抵消，而净电流流动长轴突将增强。因此，脑电图读取不同于脑磁图的信号。
对单电极水平下的情感特异性的基于EEG的研究证明，额部位处，尤其是α(8-12Hz)带中的不对称活动与情感有相关联。享受的自愿面部表情微笑产生较高的左额激活。在自愿面部表情恐惧中观察到左额活动减少。除了α带活动外，发现额中线(Fm)的θ带功率也与情感状态有关。愉悦的(与令人不快的相反)情感与额中线θ功率的增加相关联。许多研究试图利用模式分类，如神经网络、统计分类器、聚类算法等来在EEG中反映的各种情感状态之间进行区分。
对单电极水平下的情感特异性的基于EEG的研究证明，额部位处，尤其是α(8-12Hz)带中的不对称活动与情感有相关联。Ekman和Davidson发现，愉悦的自愿面部表情微笑产生较高的左额激活(Ekman P、Davidson RJ(1993)“自愿微笑改变区域性脑活动(Voluntary Smiling Changes Regional Brain Activity)”,《心理科学(Psychol Sci)》4:342-345)。Coan等人的另一项研究发现在自愿面部表情恐惧中左额活动减少(Coan JA、Allen JJ、Harmon-Jones E(2001)“额皮层之上的自愿面部表情和半球非对称性(Voluntary facial expression and hemispheric asymmetry over the frontalcortex)”,《心理生理学(Psychophysiology)》,38:912–925)。除了α带活动外，发现额中线(Fm)的θ带功率也与情感状态有关。例如，Sammler和其同事示出，愉悦的(与令人不快的相反)情感与额中线θ功率的增加相关联(Sammler D、Grigutsch M、Fritz T,Koelsch S(2007),“音乐和情感：对愉悦和令人不快的音乐的处理的电生理相关性(Music andemotion:Electrophysiological correlates of the processing of pleasant andunpleasant music)”,《心理生理学》44:293-304)。为了进一步证明这些情感特定的EEG特性是否强到足以在各种情感状态之间进行区分，一些研究利用了模式分类分析方法。
通过EEG检测不同的情感状态使用基于EEG的功能连接性可以变得更合适。存在各种方法来估计基于EEG的功能性脑连接性：已经使用了每对EEG电极之间的相关性、相干性和相位同步指标。设想是越高的相关图指示两个信号之间的关系更强。(Brazier MA、CasbyJU(1952)“电脑电图电位的交叉相关性和自相关性研究(Cross-correlation andautocorrelation studies of electroencephalographic potentials)”,《脑电图与临床神经生理学(Electroen clin neuro)》4:201–211)。相干性给出了与相关性类似的信息，但还包含两个信号之间的协变性，所述协变性是频率的函数。(Cantero JL、Atienza M、SalasRM、Gomez CM(1999)“不同脑状态的αEEG相干性：人类受试者的醒觉水平的电生理指标(Alpha EEG coherence in different brain states:an electrophysiological indexof the arousal level in human subjects)”,《神经科学通讯快报(Neurosci lett)》271:167–70)。设想是越高的相关性越指示两个信号之间的关系越强。(Guevara MA、Corsi-Cabrera M(1996)“EEG相干性或EEG相关性？(EEG coherence or EEG correlation？)”,《国际心理生理学杂志(Int J Psychophysiology)》23:145–153；(Cantero JL、Atienza M、Salas RM、Gomez CM(1999)“不同脑状态的αEEG相干性：人类受试者的醒觉水平的电生理指标”,《神经科学通讯快报》271:167–70；Adler G、Brassen S、Jajcevic A(2003)“阿尔茨海默氏症中的EEG相干性(EEG coherence in Alzheimer's dementia)”《神经传输杂志(JNeural Transm)》110:1051–1058；Deeny SP、Hillman CH、Janelle CM、Hatfield BD(2003)“皮层-皮层通信和熟练射手的优越表现：EEG相干性分析(Cortico-corticalcommunication and superior performance in skilled marksmen:An EEG coherenceanalysis)”《运动和运动心理学杂志(J Sport Exercise Psy)》25:188–204)。基于两个信号之间的相位差估计的神经元组之中的相位同步是估计脑区之间的基于EEG的功能连接性的另一种方法。所述方法为(Franaszczuk PJ、Bergey GK(1999)“用于测量发作间和发作期EEG信号的同步性的自回归方法(An autoregressive method for the measurement ofsynchronization of interictal and ictal EEG signals)”《生物控制论(BiolCybern)》81:3-9)。
许多组使用基于EEG的功能性脑连接性检查了情感特异性。例如，Shin和Park示出，当情感状态在高室温下变得更加消极时，颞部位与枕部位的通道之间的相关系数增加(Shin J-H、Park D-H.(2011)“根据情感变化对脑电图(EEG)的特性以及环境因素的影响的分析(Analysis for Characteristics of Electroencephalogram(EEG)and Influenceof Environmental Factors According to Emotional Changes)”于Lee G、Howard D、
Figure BDA0003065971300000171
D编辑者,《收敛与混合信息技术(Convergence and Hybrid InformationTechnology)》施普林格出版社柏林海德堡(Springer Berlin Heidelberg),488–500中)。Hinrichs和Machleidt证明，相比于幸福，悲伤期间α带中的相干性降低(Hinrichs H,Machleidt W(1992)“以EEG相干性反映的基本情感(Basic emotions reflected in EEG-coherences)”《国际心理生理学杂志》13:225–232)。Miskovic和Schmidt发现，相比于查看中性图像，前额皮层和后皮层之间的EEG相干性在查看高度情感唤醒(即威胁)图像时增加(Miskovic V,Schmidt LA(2010)“情感作用图像查看期间的跨区域皮层同步(Cross-regional cortical synchronization during affective image viewing)”《脑研究(Brain Res)》1362:102–111)。Costa和同事应用了同步指标来检测不同脑部位在不同情感状态下的相互作用(Costa T、Rognoni E、Galati D(2006)“对积极和消极电影刺激的情感响应期间的EEG相位同步(EEG phase synchronization during emotional response topositive and negative film stimuli)”,《神经科学通讯快报》406：159–164)。Costa的结果示出了在情感刺激期间，尤其是在消极情感(即悲伤)期间，额通道之中的同步指标的总体增加。此外，发现相位同步模式在积极情感与消极情感之间不同。Costa还发现，在每个频带上，悲伤比幸福更同步，并且与在左额部位与右额部位之间以及在左半球内的更广泛的同步相关联。相反，幸福与额部位与枕部位之间的更广泛的同步相关联。
不同连接性指标对EEG信号的不同特性敏感。相关性对相位和极性敏感，但与幅度无关。幅度和相位两者的变化导致相干性的变化(Guevara MA、Corsi-Cabrera M(1996)“EEG相干性或EEG相关性？”《国际心理生理学杂志》23:145-153)。相位同步指标仅对相位的变化敏感(Lachaux JP、Rodriguez E、Martinerie J、Varela FJ(1999)“测量脑信号中的相位同步性(Measuring phase synchrony in brain signals)”,《人脑映射(Hum BrainMapp)》8:194-208)。
许多研究尝试通过记录和在统计学上分析来自中枢神经系统的EEG信号的手段来对情感状态进行分类。
可以使用认为存在中性、积极和消极情感状态的情感的维度论来对情感状态进行分类，因为大量研究表明中枢神经系统的响应与情感效价和醒觉度相关。(参见例如Davidson RJ(1993)“大脑不对称和情感-概念和方法难题(Cerebral Asymmetry andEmotion-Conceptual and Methodological Conundrums)”,《认知情感(CognitionEmotion)》7:115-138；Jones NA、Fox NA(1992)“情感唤起电影期间的脑电图不对称性以及其与积极情感作用和消极情感作用的关系(Electroencephalogram asymmetry duringemotionally evocative films and its relation to positive and negativeaffectivity)”《脑认知((Brain Cogn)》20:280-299；Schmidt LA、Trainor LJ(2001)“额脑电活动(EEG)区别音乐情感的效价和强度(Frontal brain electrical activity(EEG)distinguishes valence and intensity of musical emotions)”,《认知情感(CognitionEmotion)》15:487-500；Tomarken AJ、Davidson RJ、Henriques JB(1990)“静息额脑不对称预测对电影的情感响应(Resting frontal brain asymmetry predicts affectiveresponses to films)”,《人格与社会心理学杂志(J Pers Soc Psychol)》59:791-801)。如Mauss和Robins(2009),“对情感响应的度量似乎是沿维度(例如，效价、醒觉度)而不是离散情感状态(例如，悲伤、恐惧、愤怒)构建的(measures of emotional responding appearto be structured along dimensions(e.g.,valence,arousal)rather than discreteemotional states(e.g.,sadness,fear,anger))”所表明的。
发现基于EEG的功能连接性在中性、积极或消极的情感状态之间显著不同。Lee Y-Y、Hsieh S(2014)“通过基于EEG的功能连接性模式的手段对不同情感状态进行分类(Classifying Different Emotional States by Means of EEG-Based FunctionalConnectivity Patterns)”,《公共科学图书馆：综合(PLos ONE)》9(4):e95415.doi.org/10.1371/journal.pone.0095415。连接性模式可以通过使用二次判别分析的模式分类分析来检测。结果指示，分类速率优于偶然性。其得出结论，估计基于EEG的功能连接性为研究脑活动与情感状态之间的关系提供了有用的工具。
情感影响学习。最初由认知模块构成的智能辅导系统(ITS)学习者模型已延伸到包含心理模块和情感模块。Alicia Heraz等人引入了情感剂。情感剂与ITS相互作用，以基于学习者的精神状态传达其情感状态。从学习者的脑波获得精神状态。药剂通过使用机器学习技术学习，以预测学习者的情感。(Alicia Heraz、Ryad Razaki、Claude Frasson,“使用机器学习以根据脑波来预测学习者的情感状态(Using machine learning to predictlearner emotional state from brainwaves)”,《高级学习技术(Advanced LearningTechnologies)》,2007,ICALT 2007。第七届IEEE高级学习技术国际会议(Seventh IEEEInternational Conference on Advanced Learning Technologies)(ICALT 2007)。
使用EEG来评估情感状态具有许多实际应用。第一此类应用之一是新加坡旅游集团(Singapore tourism group)通过测量脑波来开发基于情感的旅行指南。“通过研究度假的一家人的脑波，研究人员写出了为未来的游客提供其在不同景点处可能期望经历的情感的建议的《新加坡情感旅行指南(By studying the brainwaves of a family onvacation,the researchers drew up the Singapore Emotion Travel Guide,whichadvises future visitors of the emotions they can expect to experience atdifferent attractions)》”(www.lonelyplanet.com/news/2017/04/12/singapore-emotion-travel-guide)新南威尔士大学(University of New South Wales)的JoelPearson和其小组开发了使用EEG测量旅行者的脑波并且解码特定情感状态的方案。
另一个最近发布的应用涉及虚拟现实(VR)技术。Looxid Labs公司推出了利用了来自佩戴VR头戴装置的受试者的EEG的技术。Looxid Labs公司的意图是将脑波纳入VR应用中，以便准确地推断情感。如MindMaze以及甚至Samsung等其它产品尝试创建通过面部肌肉识别的类似应用。(scottamyx.com/2017/10/13/looxid-labs-vr-brain-waves-human-emotions/)。根据其网站(looxidlabs.com/device-2/)，Looxid Labs开发套件提供了嵌入有微型眼和脑传感器的VR头戴装置。其使用6个EEG通道：国际10-20系统中的Fp1、Fp2、AF7、AF8、AF3、AF4。
为了评估用户的思想状态，可以使用计算机来分析由用户的脑产生的EEG信号。然而，脑的情感状态是复杂的，并且与特定情感相关联的脑波似乎会随着时间的推移而变化。Wei-Long Zheng(上海交通大学(Shanghai Jiao Tong University))使用机器学习来标识情感脑状态并且可靠地重复情感脑状态。机器学习算法发现了一组清晰地区别积极、消极和中性情感的模式，所述模式适用于不同的受试者以及随着时间的推移的相同受试者，其准确度为约80％。(参见Wei-Long Zheng、Jia-Yi Zhu、Bao-Liang Lu,“根据EEG标识随时间推移的稳定模式以进行情感标识(Identifying Stable Patterns over Time forEmotion Recognition from EEG)”,arxiv.org/abs/1601.02197；另请参见“一台智能机器如何学习来识别人类情感(How One Intelligent Machine Learned to Recognize HumanEmotions)”《MIT技术评论(MIT Technology Review)》,2016年1月23日)。
MEG：脑磁图(MEG)是功能性神经成像技术，其用于通过使用非常灵敏的磁力计记录由在脑中自然产生的电流产生的磁场来映射脑活动。SQUID(超导量子干涉装置)阵列是目前最常见的磁力计，同时正在研究SERF(无自旋交换弛豫)磁力计(
Figure BDA0003065971300000201
Matti、Hari,Riitta、Ilmoniemi,Risto J、Knuutila,Jukka、Lounasmaa,Olli V.(1993)。“脑磁图学理论、仪器以及在对运作的人脑的非侵入性研究中的应用(Magnetoencephalography-theory,instrumentation,and applications to noninvasive studies of the workinghuman brain)”,《现代物理评论(Reviews of Modern Physics)》65(2):413-497。ISSN0034-6861.doi:10.1103/RevModPhys.65.413)。众所周知，“神经元活动造成大脑血流量、血容量和血液氧合的局部变化(neuronal activity causes local changes in cerebralblood flow,blood volume,and blood oxygenation)”(初级感觉刺激期间人脑活动的动态磁共振成像(Dynamic magneticresonance imaging of human brain activity duringprimary sensory stimulation.),K.K.Kwong、J.W.Belliveau、D.A.Chesler、I.E.Goldberg、R.M.Weisskoff、B.P.Poncelet、D.N.Kennedy、B.E.Hoppel、M.S.Cohen和R.Turner)。使用“具有覆盖受试者的头部的头盔形状的检测器阵列的122通道D.C.SQUID磁力计”示出，“系统允许同时记录整个头部的磁活动(system allows simultaneousrecording of magnetic activity all over the head)”(用于研究来自人脑的磁信号的122通道squid仪器(122-channel squid instrument for investigating the magneticsignals fromthe human brain.))A.I.Ahonen、M.S.
Figure BDA0003065971300000202
M.J.Kajola、J.E.T.Knuutila、P.P.Laine、O.V.Lounasmaa、L.T.Parkkonen、J.T.Simola和C.D.TeschePhysica Scripta,第1993卷,T49A)。
在一些情况下，磁场会抵消，并且因此可检测的电活动可能与通过EEG获得的可检测的电活动从根本上不同。然而，脑节律的主要不同可通过两种方法检测。
MEG试图检测来自细胞中的放电的磁偶极子发射，例如，神经动作电位。MEG的典型传感器是超导量子干扰装置(SQUID)。这些目前需要冷却到液氮或液氦温度。然而，室温或接近室温的超导体和微型低温冷却器的开发可以允许现场部署以及便携式或移动探测器。由于MEG不太受介质电导率和介电性质的影响并且由于其固有地检测磁场向量，因此MEG技术允许体积地映射脑活动并且区别可能抑制可检测的EEG信号的互补活动。MEG技术还支持场的向量映射，因为磁发射器固有地是偶极子，并且因此固有地可获得大量信息。
EEG和MEG可以监测神志状态。例如，深度睡眠的状态与较大幅度的较慢EEG振荡相关联。各种信号分析方法允许鲁棒地标识不同SS、麻醉深度、癫痫发作以及与详细认知事件的连接。
正电子发射层析成像(PET)扫描：PET扫描是帮助显示组织和器官如何起作用的成像测试(Bailey,D.L、D.W.Townsend、P.E.Valk、M.N.Maisey(2005)。“正电子发射层析成像：基础科学(Positron Emission Tomography:Basic Sciences)”新泽西州锡考克斯：施普林格出版社(Secaucus,NJ:Springer-Verlag)ISBN 1-85233-798-2)。PET扫描使用放射性药物(正电子发射示踪剂)来示出此活动。其使用此放射来产生针对脑的不同活动着色的3-D图像。
fMRI：功能性磁共振成像或功能性MRI(fMRI)是使用MRI技术的功能性神经成像程序，其通过检测与血流相关联的变化来测量脑活动(“磁共振，关键同行评审简介；功能性MRI(Magnetic Resonance,a critical peer-reviewed introduction)”《欧洲磁共振论坛(European Magnetic Resonance Forum)》2014年11月17日检索；Huettel、Song和McCarthy(2009))。Yukiyasu Kamitani等人,“神经元(Neuron)”(DOI:10.1016/j.neuron.2008.11.004)使用在功能性MRI扫描仪中提取的脑活动图像来从头开始重建黑白图像。另请参见Celeste Biever的“‘思想阅读’软件可以记录您的梦(Mind-reading'software could record your dreams)”。《新科学家(New Scientist)》,2008年12月12日,(www.newscientist.com/article/dn16267-mind-reading-software-could-record-your-dreams/)。
功能性近红外光谱(fNIRS)：fNIR是非侵入性成像方法，其涉及对根据近红外(NIR)光衰减的测量或时间或相位变化而解析的生色团浓度进行定量。NIR谱光利用光学窗口，在光学窗口中皮肤、组织和骨骼对700-900nm的谱中的NIR光几乎是透明的，而血红蛋白(Hb)和脱氧血红蛋白(脱氧-Hb)是更强的光吸收剂。脱氧-Hb和氧-Hb的吸收谱的差异允许通过使用多个波长的光衰减来测量血红蛋白浓度的相对变化。选择两个或更多个波长，其中一个波长高于810nm的等吸点并且一个低于所述等吸点，在所述等吸点处，脱氧Hb和氧-Hb具有相同的吸收系数。使用修正的比尔-朗伯定律(modified Beer-Lambert law)(mBLL)，可以将相对浓度计算为总光子路径长度的函数。典型地，光发射器和检测器同侧放置在受试者头骨上，因此记录的测量结果是由于椭圆通路之后的后向散射(反射)的光。fNIR作为功能性成像方法的使用依赖于神经血管偶联原理，其也被称为血液动力学响应或血氧水平相关性(BOLD)响应。此原理还形成fMRI技术的核心。通过神经血管偶联，神经元活动与局部大脑血流的相关变化相联系。fNIR和fMRI对相似生理变化敏感并且通常是比较方法。涉及fMRI和fNIR的研究示出认知任务的高度相关的结果。与fMRI相比，fNIR在成本和便携性方面具有几个优势，但由于在光发射器功率方面的限制，其不能用于测量超过4cm深的皮层活动并且具有更有限的空间分辨率。fNIR包含出于功能性目的使用扩散光学层析成像(DOT/NIRDOT)。多路复用fNIRS通道可以允许脑活动的2D地形功能图(例如，利用HitachiETG-4000或Artinis Oxymon)，而使用多个发射器间距可以用于构建3D层析成像图。
Beste Yuksel和Robert Jacob,《脑自动化赞颂歌(Brain Automated Chorale(BACh))》，ACM CHI 2016,DOI:10.1145/2858036.2858388提供了通过测量其脑如何努力地工作来帮助初学者学习在钢琴上弹奏巴赫赞颂歌的系统。这可以通过使用功能性近红外光谱(fNIRS)估计脑的工作量来完成，所述fNIRS是测量脑-在此情况下前额皮层中的氧气水平的技术。努力工作的大脑会吸收更多氧气。绑扎在玩家前额上的传感器与计算机通话，所述计算机在线递送新音乐，一次一行。另请参见Anna Nowogrodzki的“思想阅读技术帮助初学者快速学习演奏巴赫(Mind-reading tech helps beginners quickly learn to playBach)”,《新科学家》,2016年2月9日,可在线获得：www.newscientist.com/article/2076899-mind-reading-tech-helps-beginners-quickly-learn-to-play-bach/。
LORETA：通常被称为LORETA的低分辨率脑电磁层析成像是功能成像技术，其通常在时频域中使用线性约束的最小方差向量束形成器，如Gross等人,“相干源的动态成像：研究人脑中的神经相互作用(Dynamic imaging of coherent sources:Studying neuralinteractions in the human brain”,PNAS 98,694-699,2001中描述的。其允许可变时频范围内的图像(大多为3D)诱发和诱导的振荡活动，其中时间是相对于触发事件的。存在与用于LORETA的技术有关的成像的三个类别。参见wiki.besa.de/index.php？title＝Source_Analysis_3D_Imaging#Multiple_Source_Beamformer_.28MSBF.29。多源束形成器(MSBF)是用于对脑活动进行成像的工具。其应用于时频域并且基于单次试验数据。因此，其不仅可以对诱发的图像进行成像并且还可以对诱导的活动进行成像，这在数据的时域平均中是不可见的。相干源动态成像(DICS)可以找到脑中的任何两对体素之间或外部源与脑体素之间的相干性。DICS需要时频变换的数据并且可以找到诱发的活动和诱导的活动的相干性。以下成像方法基于分布式多源模型提供脑活动的图像：CLARA是LORETA图像的迭代应用，其将获得的3D图像集中在每个迭代步骤中。LAURA使用具有局部自回归函数形式的空间加权函数。LORETA具有实施为空间加权先验的3D拉普拉斯运算符。sLORETA是通过分辨率矩阵标准化的未加权的最小范数。除了另外的深度权重之外，swLORETA与sLORETA等效。SSLOFO是其中源空间连续缩小的标准化最小范数图像的迭代应用。用户定义的体积图像允许利用不同的成像技术进行实验。可能的是为一系列分布式源图像指定用户定义的参数，以产生新成像技术。如果没有单独的MRI可用，则最小范数图像会显示在标准脑表面上并且针对标准源位置进行计算。如果可用，则使用单独脑表面来构建分布式源模型并对脑活动进行成像。与经典LORETA不同，皮层LORETA不是在3D体积中而是在皮层表面上计算的。与经典CLARA不同，皮层CLARA不是在3D体积中而是在皮层表面上计算的。多源探针扫描(MSPS)是用于验证离散多源模型的工具。源灵敏度图像显示当前离散源模型中的所选源的灵敏度并且因此是数据无关的。
神经反馈：神经反馈(NFB)，也被称为神经疗法或神经生物反馈，是使用脑活动最常见的脑电图(EEG)的实时显示来教导脑功能进行自我调节的一种类型的生物反馈。典型地，传感器放置在头皮上以测量活动，其中测量结果使用视频显示器或声音来显示。反馈也可以呈各种其它形式。典型地，试图通过初级感觉输入来呈现反馈，但是这不是对所述技术进行限制。
神经反馈增强性能的应用延伸到音乐、舞蹈和表演等艺术领域。音乐学院音乐家的研究发现，α-θ训练有益于三个音域：音乐性、传达和技术。从历史上看，创建了α-θ训练(一种形式的神经反馈)以通过诱导催眠-“与创造见解相关联的边界清醒状态”通过促进神经元连接性来协助创造力。Α-θ训练也已示出可改善儿童的新手歌唱。Α-θ神经反馈与心脏速率可变性训练(一种形式的生物反馈)结合已通过增强竞争性交际舞的表现和增加当代舞者的认知创造力，在舞蹈中产生了益处。另外，神经反馈也已示出可能由于表演时的更大的沉浸感而在行为者中注入优异的流动状态。
对专家在执行与其相应专业领域相关的任务时的脑波活动的几项研究显示出与顶级性能相关联的所谓“流量”的某些特征迹象讯号。Mihaly Csikszentmihalyi(芝加哥大学(University of Chicago))发现，最熟练的象棋玩家在前额皮层中显示出较少的EEG活动，这通常与游戏期间较高的认知过程，如工作记忆和言语表达相关联。
Chris Berka等人,《先进的脑监测(Advanced Brain Monitoring)》,加利福尼亚州卡尔斯巴德(Carlsbad,California),《国际体育与社会杂志(The InternationalJ.Sport and Society)》,第1卷,第87页,着眼于奥林匹克弓箭手和职业高尔夫球手的脑波。在弓箭手射箭或高尔夫球手击球前几秒钟，团队发现α带模式小量增加。这可能与在诱发的电位研究中观察到的视情况而定的消极变化以及也被称为运动前电位或准备电位(RP)的Bereitschaftspotential或BP(来自德语，“准备电位”)相对应，所述准备电位是脑的运动皮层和互补运动区的引起自愿肌肉移动的活动的度量。Berka还使用神经反馈对新手射手进行了训练。每个人都与梳理并显示特定脑波的电极连同测量其心跳的监测器连接。通过控制其呼吸并且学习在其前面的屏幕上有意地操纵波形，新手设法产生流动状态的α波特性。这进而帮助其提高其击中目标的准确性。
低能量神经反馈系统(LENS)：LENS或低能量神经反馈系统使用非常低功率的电磁场将反馈载送给接受反馈的人。反馈沿将脑波载送到放大器和计算机的相同引线行进。尽管反馈信号弱，但其在接收反馈的个人没有进行有意识的努力的情况下，还会在脑波中产生可测量的变化。系统是软件控制的，以接收来自EEG电极的输入，以控制刺激。通过头皮。神经反馈使用与主导脑波频率不同但与其相关的反馈频率。当暴露于此反馈频率时，EEG幅度分布的功率改变。大多数情况下，脑波的功率会降低，但有时其功率也会增加。在任一种情况下，结果都是脑波状态改变并且脑调节自身的能力更强。
基于内容的脑波分析：记忆不是唯一的。Janice Chen,《自然神经科学(NatureNeuroscience)》,DOI:10.1038/nn.4450示出，当人们描述出自福尔摩斯戏剧的情节时，其脑活动模式对于每个场景几乎都是彼此完全相同的。此外，也有证据表明，当人们将此告诉其它某人时，其也将相同的活动植入到其脑中。此外，Chen等人在未看过电影的人倾听其它某人对电影的描述的研究中发现，倾听者的脑活动看起来与看过电影的人非常相似。另请参见Andy Coghlan的“脑以完全相同的方式记录和记住事物(Our brains record andremember things in exactly the same way)”,《新科学家》,2016年12月5日(www.newscientist.com/article/2115093-our-brains-record-and-remember-things-in-exactly-the-same-way/)。
Brian Pasley,《神经工程学前沿(Frontiers in Neuroengineering)》,doi.org/whb开发了用于读取思维的技术。小组假设，听见言语并且对自身进行思考可能会激发脑中相同的神经签名中的一些神经签名。其猜想被训练以识别出大声听到的言语的算法也可能能够识别出思考的话语。在实验中，经言语训练的解码器能够使用神经活动单独来重建志愿者中的几个志愿者正在思考的话语。另请参见Helen Thomson的“聆听内部的声音(Hearing our inner voice)”,《新科学家》,2014年10月29日(www.newscientist.com/article/mg22429934-000-brain-decoder-can-eavesdrop-on-your-inner-voice/)。
Jack Gallant等人能够使用将受试者在观看图像时的脑活动与其在观看“训练”照片时捕获的图像进行比较的软件，根据脑扫描来检测某人正在观看一组图像中的哪一个。然后，程序从一组先前未查看的图片中选择最可能匹配的。
Ann Graybiel和Mark Howe使用电极来分析在教导大鼠在迷宫中导航时，大鼠腹侧纹状体中的脑波。当老鼠学习任务时，其脑活动示出快速γ波爆发。一旦老鼠掌握了任务，其脑波就会减慢到其初始频率的几乎四分之一，变成β波。Graybiel团队假定，此转变反映学习何时变成习惯。
Bernard Balleine,《美国国家科学院院刊(Proceedings of the NationalAcademy of Sciences)》,DOI:10.1073/pnas.1113158108。另请参见Wendy Zukerman的“脑波减慢时的习惯形成(Habits form when brainwaves slow down)”,《新科学家》,2011年9月26日(www.newscientist.com/article/dn20964-habits-form-when-brainwaves-slow-down/)假定，较慢的脑波可能是脑清除了过量活动以改善行为。建议可能增强在其通过增强此β波活动来学习技术时的速率。
US 9,763,592提供了一种用于指示用户行为改变的系统，所述系统包括：收集和分析生物电信号数据集；以及基于分析提供行为改变建议。可以提供刺激以提示用户进行的动作，所述动作可以是视觉的、听觉的或触觉的。
象棋游戏是认知任务的良好实例，所述认知任务需要大量训练和经验。已进行了关于象棋玩家的许多EEG研究。Pawel Stepien、Wlodzimierz Klonowski和NikolaySuvorov,“对象棋玩家的EEG的非线性分析(Nonlinear analysis of EEG in chessplayers)”,《EPJ非线性生物医学物理学(EPJ Nonlinear Biomedical Physics)》,20153:1示出了相比于滑动窗口经验模式分解，用于分析与象棋任务有关的EEG信号的Higuchi碎形维度方法(Higuchi Fractal Dimension method)的更好可用性。所述论文示出，即使当在不同的EEG带对信号的总功率的贡献中，在游戏与放松状态之间不存在显著差异时，游戏期间的EEG信号也是更复杂的、非线性的且不固定的。需要从更多象棋专家聚集更多数据并且将其与来自新手象棋玩家的数据进行比较。另请参见Junior,L.R.S.、Cesar,F.H.G.、Rocha,F.T.和Thomaz,C.E.,“EEG和象棋玩家的眼睛移动图(EEG and Eye Movement Mapsof Chess Players)”,《第六届模式识别应用和方法国际会议论文集(Proceedings of theSixth International Conference on Pattern Recognition Applications andMethods.)(ICPRAM 2017)》第343-441页,(fei.edu.br/～cet/icpram17_LaercioJunior.pdf)。
估计基于EEG的功能连接性为研究脑活动与情感状态之间的关系提供了有用的工具。参见You-Yun Lee、Shulan Hsieh,“通过基于EEG的功能连接性模式的手段对不同情感状态进行分类”,4/17/2014,(doi.org/10.1371/journal.pone.0095415)，其旨在通过基于EEG的功能连接性模式的手段对不同情感状态进行分类，并且示出了基于EEG的功能连接性变化在情感状态之间显著不同。此外，通过使用二次判别分析的模式分类分析对连接性模式进行了检测。结果指示，分类速率优于偶然性。估计基于EEG的功能连接性为研究脑活动与情感状态之间的关系提供了有用的工具。
神经调制/神经增强：神经调制是通过将如电刺激或化学药剂等刺激针对性地递送到身体内的特定神经部位来改变神经活动。执行神经调制来归一化或调制神经组织功能。神经调制是演进的疗法，其可能涉及一定范围的电磁刺激，如磁场(TMS、rTMS)、电流(TES，例如tDCS、HD-tDCS、tACS、电睡眠)或直接注入硬脑膜下空间中的药物(鞘内药物递送)。新兴应用涉及针对性地引入基因或基因调节器和光(光遗传学)。最临床的经验是电刺激。神经调制，无论是电的还是磁的，都采用通过刺激神经细胞活动引起的身体的自然生物学响应，所述神经细胞活动可以通过释放如多巴胺等递质或如肽物质P等可以调制神经电路的可激励性和激发模式的其它化学信使来影响神经的群体。还存在对神经膜的更直接的电生理作用。根据一些应用，最终效果是从神经网络功能的扰动的状态对其进行“归一化”。用于神经刺激的推测的作用机制包含去极化阻断、神经激发的随机归一化、轴突阻断、神经激发角化病的减少以及对神经网络振荡的抑制。尽管神经刺激的确切机制未知，但经验有效性已引起了临床上可观的应用。
NE是指基于对不具有任何精神疾病的健康人的潜在神经生物学的了解，靶向地增强和延伸认知和情感能力。如此，其可以被视为涵盖提高认知、情感和运动功能的药理学和非药理学方法的涵盖性术语伴随这些目标的首要道德法律话语。至关重要的是，对于有资格作为神经增强剂的任何药物，其必须可靠地产生超出健康个体(或在具有病理学的选择组的个体中)的正常功能的实质性认知、情感或运动益处，同时不会造成副作用：至多达到通常使用的可比的合法物质或活动的水平，例如咖啡因、酒精和睡眠不足。NE药理剂包含经充分验证的促智药，如拉西坦(racetam)、长春西汀(vinpocetine)和磷脂酰丝氨酸(phosphatidylserine)以及用于治疗患有神经病症的患者的药物。非药理学措施包含用于改善各种认知和情感功能的非侵入性脑刺激以及很有潜力将可用的广泛运动和认知动作延伸到人类的脑机界面。
脑刺激：非侵入性脑刺激(NIBS)绕开了其它成像技术的相关方法，从而使得其可能在认知过程与特定脑区的功能之间建立因果关系。NIBS可以提供关于特定过程何时发生的信息。NIBS提供了研究除了过程定位之外的脑机制的机会，提供了关于给定脑区域中的活动何时参与认知过程以及甚至其如何参与的信息。在使用NIBS来探索认知过程时，重要的是不仅了解NIBS如何起作用，而且了解神经结构本身的功能。认知神经科学中使用了包含经颅磁刺激(TMS)和经颅电刺激(TES)的非侵入性脑刺激(NIBS)方法来诱导脑活动的瞬时变化并且从而改变受试者的行为。
NIBS的应用旨在确立给定皮层区域在进行中的具体运动、感知或认知过程中的作用。物理上，NIBS技术通过不同的机制影响神经元状态。在TMS中，使用螺线管(线圈)来递送强且瞬时的磁场或“脉冲”，以在线圈下方的皮层表面处诱导瞬时电流。脉冲引起受电流影响的细胞膜的快速且高于阈值的去极化，随后引起互连神经元的跨突触去极化或超极化。因此，强大的TMS可以诱导引起神经元中的动作电位的电流，而弱(阈下)可以改变细胞对去极化的易感性。复杂的线圈组可以提供复杂的3D激励场。相比之下，在TES技术中，刺激涉及通过电极对向头皮直接施加弱电流。结果，TES诱导皮层神经元的阈下极化，所述阈下极化弱到无法生成动作电位。(阈上tES对应于电惊厥疗法，所述电惊厥疗法是对抑郁的目前不看好但显然有效的治疗)。然而，通过改变固有神经元可激励性，TES可以诱导剩余膜电位的改变以及皮层神经元的突触后活动。这进而可以改变神经元的自发激发速率并且调制其对传入信号的响应，从而导致突触功效的改变。NIBS的典型应用涉及不同类型的方案：TMS可以以精确时间处的单个脉冲(spTMS)的形式、以通过可变间隔分开的脉冲对的形式或以重复TMS(rTMS)的常规或模式化方案中的一系列刺激的形式递送。在TES中，不同方案通过所使用的电流并且通过其极性确立，所述不同协议可以是直接的(阳极或阴极经颅直接电刺激：tDCS)、以固定频率交替(经颅交流电刺激：tACS)、振荡的经颅直流电流刺激(振荡tDCS)、高清经颅直流电刺激(HD-tDCS)或处于随机频率(经颅随机噪声刺激：tRNS)。(Nitsche等人,2008；Paulus,2011)。
通常，NIBS对中枢神经系统的最终作用取决于一长串参数(例如，频率、时间特性、强度、线圈/电极的几何配置、电流方向)，所述参数在任务之前(离线)或期间(在线)作为实验程序的一部分递送。另外，这些因素与同解剖结构(例如，脑组织的性质和其位置)以及受刺激的区/受试者的生理(例如，性别和年龄)和认知状态有关的几个变量相互作用。夹带假设表明了使用外部振荡力(例如，rTMS，还有tACS)在脑中诱导特定振荡频率的可能性。振荡皮层活动的生理基础在于相互作用的神经元的定时；当神经元组使其激发活动同步时，脑节律出现，网络振荡生成，并且脑区之间相互作用的基础可能发展。因为用于脑刺激的各种实验方案、对所采用的实际方案的描述以及受限的控制，所以所报道的研究的一致性缺乏并且可推断性也受到限制。因此，尽管在外颅脑刺激的作用的各个方面达成了某种共识，但所达到的结果具有在一定程度的不确定性，这取决于实施方案的细节。另一方面，在具体实验方案内，可能的是获得统计学上显著且可重复的结果。这暗示了反馈控制对于出于给定目的而控制刺激的实施方案可能是有效的；然而，采用反馈控制的现有研究缺乏。
神经元阈值的变化是由膜可渗透性的变化引起的(Liebetanz等人,2002)，这会影响任务相关网络的响应。相同的作用机制可能负责TES方法和TMS两者，即系统中的噪声诱导。然而，TES诱导的神经活动将受到系统状态的高度影响，因为其是神经调制方法(Paulus,2011)，并且其作用将取决于受刺激的区的活动。因此，最终结果将极大取决于任务特性、系统状态以及TES与此类状态相互作用的方式。
在TMS中，磁脉冲会导致电流快速增加，这在一些情况下可能导致细胞膜的受电流影响的高于阈值的去极化，从而触发动作电位并且导致连接的皮层神经元的跨突触去极化或超极化，这取决于其对一个或多个受刺激的神经元的激发的自然响应。因此，TMS激活神经群体，所述神经群体取决于几个因素，可能与任务执行全等(促进)或不全等(抑制)。TES诱导处于阈下水平的皮层神经元的极化，所述极化弱到不能诱发动作电位。然而，通过以固有神经元可激励性诱导极性偏移，TES可以改变神经元的自发激发速率并且调制对传入信号的响应。在此意义上，TES诱导的作用甚至更受条件所确定的受刺激的区的状态的约束。简而言之，NIBS导致对状态的刺激诱导的调制，所述刺激诱导的调剂基本上可以被定义为噪声诱导。诱导的噪声将不仅是随机活动，还将取决于来自对状态刺激的特性的许多参数的相互作用。
NIBS诱导的噪声将受到受刺激的区的神经群体的状态的影响。尽管NIBS“触发”的神经元的类型和数量是理论上随机的，但是诱导的神经元活动的改变可能与持续进行的活动相关，然而即使提到了非确定性过程，但是引入的噪声将不是完全随机的要素。因为其将部分地由实验变量确定，所以可以估计由刺激和由上下文引入的噪声的水平，以及两种噪声水平(刺激和上下文)之间的相互作用。已知的经颅刺激不准许利用集中的且高度针对性的信号来刺激脑的清楚限定的区，以建立唯一脑-行为关系；因此，脑刺激中的已知引入的刺激活动是“噪声”。
美容神经科学已经成为新的研究领域。Roy Hamilton、Samuel Messing和AnjanChatterjee,“重新思考思考帽-使用非侵入性脑刺激的神经增强的伦理学(Rethinkingthe thinking cap-Ethics of neural enhancement using noninvasive brainstimulation)”,《神经病学(Neurology)》,2011年1月11日,第76卷,第2期,187-193,(wwww.neurology.org/content/76/2/187.)讨论了使用非侵入性脑刺激技术，如经颅磁刺激和经颅直流电刺激来增强神经功能：认知技能、情绪和社交认知。
电脑刺激(EBS)或局灶性脑刺激(FBS)是用于通过使用电流直接或间接激励细胞膜来刺激脑中的神经元或神经网络的临床神经生物学电疗法的一种形式。参见en.wikipedia.org/wiki/Electrical_brain_stimulation。CNS刺激可以影响运动技能。
经颅电刺激(tES)：tES(tDCS、tACS和tRNS)是使用弱直流电来极化目标脑区域进行皮层刺激的一组非侵入性方法。最常使用和最知名的方法是tDCS，因为对于使用tDCS的所有考虑均已延伸到其它tES方法。关于tDCS在认知中的应用的假设与TMS的那些非常相似，例外的是tDCS从未被视为虚拟病变方法。tDCS可以增加或减少受刺激的脑区域中的皮层可激励性并且相应地促进或抑制行为。tES不会诱导动作电位而是相反调制神经元响应阈值，使得其可以被定义为阈下刺激。
Michael A.Nitsche和Armin Kibele,“非侵入性脑刺激和神经夹带增强竞技表现-综述(Noninvasive brain stimulation and neural entrainment enhance athleticperformance-a review)”,《(J.Cognitive Enhancement)》1.1(2017):73-79讨论了非侵入性脑刺激(NIBS)绕开了其它成像技术的相关方法，从而使得其可能在认知过程与特定脑区的功能之间建立因果关系。NIBS可以提供关于特定过程何时发生的信息。NIBS提供了研究除了过程定位之外的脑机制的机会，提供了关于给定脑区域中的活动何时参与认知过程以及甚至其如何参与的信息。在使用NIBS来探索认知过程时，重要的是不仅了解NIBS如何起作用，而且了解神经结构本身的功能。认知神经科学中使用了包含经颅磁刺激(TMS)和经颅电刺激(tES)的非侵入性脑刺激(NIBS)方法来诱导脑活动的瞬时变化并且从而改变受试者的行为。NIBS的应用旨在确立给定皮层区域在进行中的具体运动、感知或认知过程中的作用(Hallett,2000；Walsh和Cowey,2000)。物理上，NIBS技术通过不同的机制影响神经元状态。在TMS中，使用螺线管(线圈)来递送强且瞬时的磁场或“脉冲”，以在线圈下方的皮层表面处诱导瞬时电流。(US2004078056)脉冲引起受电流影响的细胞膜的快速且高于阈值的去极化(Barker等人,1985,1987)，随后引起互连神经元的跨突触去极化或超极化。因此，TMS诱导在神经元中引起动作电位的电流。复杂的线圈组可以提供复杂的3D激励场。相比之下，在tES技术中，刺激涉及通过电极对向头皮直接施加弱电流(Nitsche和Paulus,2000；Priori等人,1998)。结果，tES诱导皮层神经元的阈下极化，所述阈下极化弱到无法生成动作电位。然而，通过改变固有神经元可激励性，tES可以诱导剩余膜电位的改变以及皮层神经元的突触后活动。这进而可以改变神经元的自发激发速率并且调制其对传入信号的响应(Bindman等人,1962,1964,1979；Creutzfeldt等人,1962)，从而导致突触功效的改变。NIBS的典型应用涉及不同类型的方案：TMS可以以精确时间处的单个脉冲(spTMS)的形式、以通过可变间隔分开的脉冲对的形式或以重复TMS(rTMS)的常规或模式化方案中的一系列刺激的形式递送(对于完整分类，参见Rossi等人,2009)。通常，NIBS对中枢神经系统的最终作用取决于当在任务之前(离线)或期间(在线)递送时作为实验程序的一部分的一长串参数(例如，频率、时间特性、强度、线圈/电极的几何配置、电流方向)(例如，Jacobson等人,2011；Nitsche和Paulus,2011；Sandrini等人,2011)。另外，这些因素与和解剖学相关的几个变量(例如，脑组织的性质和其位置，Radman等人,2007)以及受刺激的区/受试者的生理学(例如，性别和年龄，Landi和Rossini,2010；Lang等人,2011；Ridding和Ziemann,2010)和认知(例如，Miniussi等人,2010；Silvanto等人,2008；Walsh等人,1998)状态。
经颅直流电刺激(tDCS)：颅电疗法刺激(CES)是非侵入性脑刺激的一种形式，其可跨人的头部施加小的脉冲电流来治疗各种病状，如焦虑、抑郁和失眠。参见en.wikipedia.org/wiki/Cranial_electrotherapy_stimulation。经颅直流电刺激(tDCS)是神经刺激的一种形式，其使用经由头皮上的电极递送到所关注的脑区的恒定低电流。其最初被开发用于帮助患有脑损伤或精神病状，如重度抑郁病症的患者。tDCS似乎具有用于治疗抑郁症的某种潜力。参见en.wikipedia.org/wiki/Transcranial_direct-current_stimulation。
正在针对加速学习对tDCS进行研究。使用轻度电击(通常2毫安的电流)以对神经元膜去极化，从而使细胞更具激励性并且对输入做出响应。Weisend,《实验性脑研究(Experimental Brain Research)》,第213卷,第9页(DARPA)示出，tDCS在某人练习技能的时间期间加速新神经通路的形成。tDCS似乎带来了流动状态。受试者的移动变得更加自动化，其沉着地报告，专注注意力，并且其表现立即改善。(参见Adee,Sally,“刺激脑进入分区：快速追踪到纯粹专注(Zap your brain into the zone:Fast track to purefocus)”,《新科学家》,第2850号,2012年2月1日,www.newscientist.com/article/mg21328501-600-zap-your-brain-into-the-zone-fast-track-to-pure-focus/)。
Reinhart,Robert MG.,“区间θ相位偶联和适应性行为的破坏和抢救(Disruptionand rescue of interareal theta phase coupling and adaptive behavior)”,《美国国家科学院院刊》(2017)：为额皮层的区间θ相位同步与适应性人类行为的多个组成部分之间的因果关系提供了证据。Reinhart的结果支持空间分布在额皮层中的有节律的群体活动的精确定时将信息传送给直接行为的观念。鉴于先前工作示出相位同步可以改变尖峰时间有关的塑性，以及Reihart的发现示出对神经活动和行为的刺激作用可以持续20分钟的电刺激时间段，因此合理的是假设外部调制的区间偶联通过引起功能连接性方面的神经塑性改变来改变行为。Reinhart表明，可能能够非侵入地干预人脑中的远处节律活动的时间偶联，以优化(或阻止)某一区的尖峰对另一个区的突触后突触影响，从而改善(或削弱)认知行动控制和学习所需的跨区通信。此外，功能连接性的这些神经塑性改变是利用0°相位诱导的，这表明诱导同步不需要对如MFC和IPFC等区域之间的通信延迟进行细致计算来有效地改变行为和学习。这与工作符合，示出了尽管在远处的脑区之间存在长的轴突传导延迟，但是0°相位滞后的θ相位同步可能会发生在这些区域之间并且是有意义的认知和动作功能的基础。还可能的是，具有非零时间滞后的第三皮层下或后部区域与这两个额区相互作用，以驱动目标导向行为的改变。
高清tDCS：纽约市立大学(The City University of New York)通过引入4×1HD-tDCS蒙太奇发明了高清经颅直流电刺激(HD-tDCS)。4×1HD-tDCS蒙太奇允许精确靶向皮层结构。电流的区域由4×环的面积限定，使得减小环半径增加聚焦性。4×1HD-tDCS允许单焦点刺激，这意味着中心1×电极的极性将确定环下方的神经调制的方向。这与常规tDCS相反，在常规tDCS中，对一个阳极和一个阴极的需要总是产生双向调制(甚至当使用额外的头电极)。因此，4×1HD-tDCS提供了不仅选择要靶向的皮层脑区域，而且还利用设计的极性调制所述脑区域的可激励性，而无需考虑反电极回流的能力。
经颅交流电刺激(tACS)：经颅交流电刺激(tACS)是通过皮肤和头骨施加的交流电以频率特定的方式夹带潜在脑的神经振荡的非侵入性手段。参见en.wikipedia.org/wiki/Transcranial_alternating_current_stimulation
美国公开申请第20170197081号公开了使用经皮电刺激(TES)对神经进行经皮电刺激以改变或诱导认知状态的方法。
经颅交流电刺激(tACS)是通过皮肤和头骨施加的交流电以频率特定的方式夹带潜在脑的神经振荡的非侵入性手段。参见en.wikipedia.org/wiki/Transcranial_alternating_current_stimulation；
经颅随机噪声刺激(tRNS)：经颅随机噪声刺激(tRNS)是非侵入性脑刺激技术以及经颅电刺激(tES)的形式。参见en.wikipedia.org/wiki/Transcranial_random_noise_stimulation。刺激可以包括经颅脉冲的电流刺激(tPCS)。
经颅磁刺激：经颅磁刺激(TMS)是其中使用变化的磁场以通过电磁感应来使电流在脑的小区域中流动的方法。在TMS程序期间，磁场发生器或“线圈”放置在接受治疗的人的头部附近。线圈连接到脉冲发生器或刺激器，所述脉冲发生器或刺激器向线圈递送变化的电流。TMS在诊断上用于测量中枢神经系统与骨骼肌之间的连接，以评价多种疾病状态下的损害，包含中风、多发性硬化症、肌萎缩性侧索硬化症、移动病症和运动神经元疾病。有证据表明，TMS可用于治疗神经性疼痛、重度抑郁病症和其它病状。
PEMF：当向脑施加脉冲电磁场(PEMF)时，被称为经颅磁刺激，并且自2008年已获得FDA批准用于未能对抗抑郁药做出响应的人。对脑的弱磁刺激通常被称为经颅脉冲电磁场(tPEMF)疗法。参见en.wikipedia.org/wiki/Pulsed_electromagnetic_field_therapy。
深度脑刺激(DBS)：深度脑刺激(DBS)是涉及植入被称为神经刺激器(有时被称为“脑起搏器”)的医疗装置的神经外科手术程序，所述神经刺激器通过植入的电极向脑中的具体目标(脑核)发送电脉冲，以用于治疗移动和神经精神疾病。参见en.wikipedia.org/wiki/Deep_brain_stimulation。
经颅脉冲超声(TPU)：经颅脉冲超声(TPU)使用低强度、低频率超声(LILFU)作为用于刺激脑的方法。参见en.wikipedia.org/wiki/Transcranial_pulsed_ultrasound；
感觉刺激：可以使用光、声音或电磁场来远程地传送脑波的时间模式。
光刺激：通过使用节律性视觉刺激，反复研究了α频率范围(8-13Hz)内的脑振荡的功能相关性。关于在节律性刺激期间，在EEG中测量的稳态视觉诱发的电位(SSVEP)的起源存在两种假设：脑振荡的夹带和事件相关响应(ERP)的叠加。夹带而不是叠加假设证明了节律性视觉刺激作为用于操纵脑振荡的手段，因为叠加设想了单个响应的线性求和，这与正在进行的脑振荡无关。通过将通过节律性视觉闪烁刺激的脑振荡的相位偶联与通过非节律性抖动刺激、时间变化、刺激频率和强度状况诱导的振荡进行比较测量了用不同频率和强度的节律性闪烁光刺激的参与者。发现相位偶联随着刺激强度的增加以及更接近每个参与者的固有频率的刺激频率而更加明显。甚至在单个序列的SSVEP中，也发现了非线性特征(相位锁定的间歇性)与叠加假设所设想的单个响应的线性求和相矛盾。因此，证据表明视觉节律性刺激夹带脑振荡，从而验证了作为对脑振荡的操纵的节律性刺激方法。参见Notbohm A、Kurths J、Herrmann CS,“通过节律性光刺激对脑振荡的修改提供了夹带的证据，但没有提供事件相关的响应的叠加的证据(Modification of Brain Oscillationsvia Rhythmic Light Stimulation Provides Evidence for Entrainment but Not forSuperposition of Event-Related Responses)”,《人类神经科学前沿(Front HumNeurosci)》,2016年2月3日；10:10.doi:10.3389/fnhum.2016.00010.eCollection 2016。
还已知的是周期性视觉刺激可以触发癫痫发作。
耳蜗植入物：耳蜗植入物是为极度失聪或两个耳朵严重重听的人提供声音感觉的外科手术植入的电子装置。参见en.wikipedia.org/wiki/Cochlear_implant。
迷走神经刺激：迷走神经刺激(VNS)是涉及向迷走神经递送电脉冲的医疗治疗。其用作对某些类型的顽固性癫痫和难治性抑郁症的辅助治疗。参见en.wikipedia.org/wiki/Vagus_nerve_stimulation。
脑-脑接口：脑-脑接口是一种动物的脑与另一种动物的脑之间的直接通信通路。脑-脑接口用于帮助大鼠彼此协作。当第二只大鼠不能够选择正确的杠杆时，第一大鼠注意到(没有得到第二次奖励)并且产生了一轮任务相关的神经元激发，这使得第二只大鼠更有可能选择正确的杠杆。还进行了人类研究。
2013年，来自华盛顿大学(University of Washington)的研究者能够使用电脑记录和某种形式的磁刺激向受者发送脑信号，所述脑信号使受者击中计算机游戏上的激发按钮。2015年，研究者将猴子和大鼠两者的多个脑连接起来，以形成了“有机计算机”。假设了通过使用脑-脑接口(BTBI)，可以使用动物脑作为其计算单位来构造生物计算机或脑网。最初的探索工作证明，老鼠之间在远处的笼中的协作通过来自植入其脑中的皮层微电极阵列的信号连接。当符合进入信号的“解码大鼠”执行动作时并且当通过“编码大鼠”发射导致“所期望的动作”的信号时，对大鼠进行奖励。在初始实验中，奖励的动作是在对应于接近主位置的发光的LED的杠杆的定位的远处位置中推动杠杆。大鼠需要大约一个月来使自身适应进入的“脑波”。当解码大鼠不能够选择正确的杠杆时，编码大鼠注意到(没有得到预期的奖励)并且产生了一轮任务相关的神经元激发，这使得第二只大鼠更有可能选择正确的杠杆。
在另一研究中，使用了电脑读数来触发某种形式的磁刺激，以向受者发送基于受试者的脑活动的脑信号，所述脑信号使受者击中计算机游戏上的激发按钮。
脑-计算机接口：脑计算机接口(BCI)，有时也被称为神经控制接口(NCI)、思想-机接口(MMI)、直接神经接口(DNI)或脑-机接口(BMI),是增强的或有线的脑与外部装置之间的直接通信通路。BCI与神经调制的不同之处在于其允许双向信息流。BCI通常涉及研究、映射、协助、增强或修复人类认知或感觉运动功能。
合成心灵感应，也被称为技术心灵感应或心灵致动学(geeldon.wordpress.com/2010/09/06/synthetic-telepathy-also-known-as-techlepathy-or-psychotronics/)，描述了过程，所述过程使用计算机通过其拦截、处理人的思维(作为电磁辐射)的脑-计算机接口和生成的可由人脑感知的返回信号。Dewan,E.M.,“枕α节律眼部定位和透镜适应(Occipital Alpha Rhythm Eye Position and Lens Accommodation)”,《自然(Nature)》214,975-977(1967年6月03日)证明了对α波的精神控制，所述精神控制将其断开和闭合，以通过思想单独产生话语和短语的莫尔斯密码表示。U.S.3,951,134提出了使用无线电远程地监测和改变脑波并且参考了对波形进行解调，以将其显示给操作者以进行查看，并且将其传递给计算机以进行进一步分析。1988年，Farwell,L.A.和Donchin,E.,(1988),“直言不讳：朝向利用事件相关的脑电位的精神假肢(Talking off the top of your head:towarda mental prosthesis utilizing event-related brain potentials.)”脑电图和临床神经生理学,70(6),510-523描述了使用P300响应系统传输语言信息的方法，所述方法将匹配的观察到的信息与受试者所思考的组合。在此情况下，能够选择受试者所思考的字母表的字母。理论上，可以使用任何输入并且可以构建词典。美国专利第6,011,991号描述了出于通信的目的远程地监测个体的脑波的方法并且概述了通过传感器监测个体的脑波，然后将此信息具体地通过卫星传输到计算机以进行分析的系统。此分析将确定个体是否正在尝试传达“与匹配的所存储归一化信号相对应的话语、短语或思维”。
合成心灵感应的方法可以被分类为两个主要组：被动和主动。像声纳一样，接收器可以参与或被动收听。被动接收是在首先不广播信号的情况下“读取”信号的能力。这可以大致等同于调谐到无线电站中-脑生成可以在一定距离处接收到的电磁辐射。所述距离通过接收器的灵敏度、所使用的滤波器和所需要的带宽确定。大多数大学的预算有限，并且会使用如EEG(和类似装置)等接收器。相关军事技术是监控系统TEMPEST。Robert G.Malech的方法需要在目标处广播经调制的信号。所述方法使用通过脑的调制干扰的主动信号。因此，可以使用返回信号来推断原始脑波。
计算机调解落入两个基本类别：解释性和交互式。解释性调解是对来自人脑的信号的被动分析。计算机“读取”信号，然后将所述信号与信号和其含义的数据库进行比较。使用统计分析和重复，假阳性随着时间的推移减少。交互式调解可以处于被动-主动模式或主动-主动模式。在此情况下，被动和主动表示读取脑并且写入到脑以及其利用还是没有利用广播信号的方法。交互式调解也可以手动或通过人工智能执行。手动交互式调解涉及人类操作者产生如言语或图像等返回信号。A.I.调解利用受试者的认知系统来标识图像、前言语、物体、声音和其它伪像，而不是开发A.I.例程来执行此类活动。基于A.I.的系统可以并入自然语言处理接口，所述接口会产生感觉、精神印象、幽默和对话，以提供计算机化个性的精神图片。可以使用统计分析和机器学习技术，如神经网络。
ITV新闻社(ITV News Service)在1991年3月产生了商用无线电广播(100Mhz)上背负的超声的报告，其旨在夹带伊拉克军队的脑并产生绝望的感觉。U.S.5,159,703提到“静默通信系统，在所述静默通信系统中，在非常低或非常高的音频频率范围内或在相邻超声频谱内的非听觉载体是以期望的智能进行调解并且声学地或振动地传播的幅度或频率，其用于典型地通过使用扬声器、耳机或压电换能器诱导到脑”。
已知的是分析EEG模式以提取对某些意志活动的指示(美国专利第6,011,991号)。此技术描述了可以使用计算机将EEG记录与所存储归一化信号进行匹配。然后此匹配的信号转换为对应参考。专利申请描述了方法：“能够标识个体的脑中的特定节点的其激发会影响如食欲、饥饿、口渴、通信技巧等特性的系统”；以及“安装到人身上(例如，头皮下方)的装置可以以预定方式或顺序被激励以远程地使一个或多个特定标识的脑节点被激发，以在个体中引起预定感觉或反应”，没有对实施方案的技术描述。本专利还描述了“脑活动通过脑电图(EEG)方法、脑磁图(MEG)方法等[监测]”。
脑夹带：脑夹带，也被称为脑波同步和神经夹带，是指脑自然地使其脑波频率与周期性外部刺激，通常为听觉、视觉或触觉的节律同步的能力。脑波夹带技术用于通过产生以有规律的周期性间隔发生的刺激，以模拟脑在期望的状态下的电循环来诱导各种脑状态，如放松或睡眠，由此“训练”脑自觉地改变状态。重发声学频率、闪烁的光或触觉振动是应用以生成不同感觉响应的刺激的最常见的实例。假设的是，听见某种频率的这些节拍可以诱导与特定神经活动相对应的期望的神志状态。以Hz计测量的神经激发模式与如专注关注、深度睡眠等警觉状态相对应。
神经振荡是脑和中枢神经系统中的节律性或重复性电化学活动。此类振荡可以通过其频率、幅度和相位来表征。神经组织可以生成通过单个神经元内的机制以及通过其之间的相互作用驱动的振荡活动。其还可以调整频率以与外部声学或视觉刺激的周期性振动同步。神经振荡的功能作用仍不完全了解；然而，其已经示出与情感响应、运动控制和许多认知功能(包含信息转移、感知和记忆)相关。具体地，神经振荡，尤其是θ活动，广泛地与记忆功能相联系，并且θ活动与γ活动之间的偶联被视为对包含情景记忆的记忆功能至关重要。脑电图(EEG)已被最广泛地用于研究由被称为神经群的大神经元组生成的神经活动，包含研究在睡眠和清醒的周期期间脑电图概况中发生的变化。EEG信号在睡眠期间显著地变化并且示出从较快频率到越来越慢的频率的转变，这表明神经振荡的频率与认知状态(包含意识和神志)之间的关系。
术语“夹带”用于描述许多物理系统和生物系统使其周期性和节律通过相互作用同步的共有趋势。此趋势已被标识为与通常对声音和音乐的研究特别相关，并且特别地与声学节律有关。神经运动夹带对声学刺激的最普遍和最熟悉的实例可以在脚或手指随着歌曲的节律性节拍自发地轻拍时观察到。已经标识并记录了发生在身体之外的各种人类活动的外源性节律性夹带，所述节律性夹带包含人将其言语模式的节律调整为其所通信的受试者的那些的方式，以及观众鼓掌的节律性一致。即使在陌生人群之间，也观察到呼吸速率、动力有关的且微妙的表达性运动移动以及节律性言语模式会相应于听觉刺激(如一段节律一致的音乐)而同步并夹带。此外，运动与重复触觉刺激的同步发生在动物(包含猫和猴子以及人类)中，并且伴有脑电图(EEG)读数的偏移。发生在身体内的内源性夹带的实例包含人类昼夜睡眠清醒周期与24小时的明暗周期同步，以及人对声音和音乐的频率跟随响应。
脑波或神经振荡与声学波和光波共享基本分量，包含频率、幅度和周期性。皮层神经群的同步电活动可以响应于外部声学或光学刺激而同步，并且还可以将其频率和相位与特定刺激的频率和相位夹带或同步。脑波夹带是此“神经夹带”的口语体，其作为术语用于表示由皮层神经元群中的同步电活动产生的振荡的聚合频率可以调整以与外部刺激的周期性振动同步，如感知为音高的持续声学频率、被感知为节律的间歇的声音或有规律地节律性地间歇的闪烁光的有规律地重复的模式。
可通过脑电图(EEG)测量结果证明的神经振荡的变化是通过听音乐而引起的，听音乐可以以强化作用且向营养地调制自主醒觉，从而分别增加和减少醒觉度。音乐听觉刺激也已被证明可改善免疫功能、促进放松、改善情绪并有助于应力缓解。
频率跟随响应(FFR)，也被称为频率跟随电位(FFP)，是对听到的声音和音乐的特定响应，神经振荡通过所述特定响应调整其频率以匹配听觉刺激的节律。意图是影响皮层脑波频率的声音的使用被称为听觉驱动，通过所述听觉驱动，神经振荡的频率被“驱动”以夹带声源的节律。
可以进行事件相关的时频测量的基线校正，以考虑事件前基线活动。通常，基线周期由时间锁定事件之前的时间窗口内的值的平均限定。存在至少四种用于时频分析的基线校正的常用方法。所述方法包含各种基准值归一化。参见
“已使用脑电图(EEG)和功能性磁共振成像fMRI来研究与不同情感状态相关联的特定脑活动(Electroencephalograms(EEG)and functional Magnetic ResonanceImaging,fMRI have been used to study specific brain activity associated withdifferent emotional states)”Mauss和Robinson在其评论论文中指出，“情感状态可能涉及电路，而不是孤立地考虑的任何脑区域(emotional state is likely to involvecircuits rather than any brain region considered in isolation)”(Mauss IB,Robinson MD(2009)《情感测量：情感认知评论(Measures of emotion:Areview.CognEmot)》23:209–237)。
可以在与认知任务(ERP)结合或不与任务(EP)结合的情况下检查每个分量的幅度、来自刺激的潜伏期和协方差(在多个电极部位的情况下)。稳态视觉诱发的电位(SSVEP)使用通常叠加在显示认知任务的TV监测器之前的持续正弦调制的闪烁光。测量了含有刺激频率的窄频带中的脑响应。幅度、相位和相干性(在多个电极部位的情况下)可能与认知任务的不同部分相关。脑夹带可通过EEG或MEG活动检测。脑夹带可通过EEG或MEG活动检测。
夹带假设(Thut和Miniussi,2009；Thut等人,2011a,2012)表明了使用外部振荡力(例如，rTMS，还有tACS)在脑中诱导特定振荡频率的可能性。振荡皮层活动的生理基础在于相互作用的神经元的定时；当神经元组使其激发活动同步时，脑节律出现，网络振荡生成，并且脑区之间相互作用的基础可能发展(Buzsàki,2006)。因为用于脑刺激的各种实验方案、对所采用的实际方案的描述以及受限的控制，所以所报道的研究的一致性缺乏并且可推断性也受到限制。因此，尽管在外颅脑刺激的作用的各个方面达成了各种共识，但所达到的结果具有在一定程度的不确定性，这取决于实施方案的细节。另一方面，在具体实验方案内，可能的是获得统计学上显著且可重复的结果。这暗示了反馈控制对于出于给定目的而控制刺激的实施方案可能是有效的；然而，缺乏采用反馈控制的研究。
不同的认知状态与脑中的不同振荡模式相关联(Buzsàki,2006；Canolty和Knight,2010；Varela等人,2001)。Thut等人(2011b)通过并行EEG-TMS实验直接测试了夹带假说。其首先确定了顶-枕α调制的单独源和单独α频率(脑磁图研究)。然后，其在记录静息时的EEG活动的同时以单独α功率施加rTMS。结果证实了夹带假设的三个预测：TMS之后对特定频率的诱导、TMS刺激期间由于同步而对振荡的增强以及诱导的频率和正在进行的活动的相位对准(Thut等人,2011b)。
如果关联刺激是其中激活的定时和强度是关键因素的人类神经塑性的一般原理，则可能的是在区内或区之间使用外力与相位同步/对准振荡也可以利于有效通信和关联塑性(或更改通信)。在此方面，已示出关联皮层-皮层刺激增强受刺激的区之间的振荡活动的相干性(Plewnia等人,2008)。
在相干共振中(Longtin,1997)，在可激励系统中添加某种量的噪声导致最相干和最熟练的振荡响应。脑对外部定时嵌入的刺激的响应可能会导致相位方差减少以及正在进行的EEG活动(夹带、相位重置)的相位分量的对准(聚类)增强，这可以改变信噪比并且可以增加(或减少)信号功效。
如果将脑内的神经元活动视为松散偶联的振荡器组，则可以控制的各个参数包含神经元的区域的大小、振荡频率、共振频率或时间常数、振荡器阻尼、噪声、幅度、与其它振荡器的偶联以及当然可以包含刺激和/或功率损失的外部影响。在人脑中，药理干预可能是显著的。例如，改变可激励性的药物，如咖啡因、神经递质释放和再摄取、神经传导，可以影响神经振荡器的操作。同样，阈下外部刺激作用(包含DC、AC和磁电磁作用)也可以影响神经振荡器的操作。
相位重置或偏移可以使输入同步并有利于通信，并且最终有利于Hebbian塑性(Hebb,1949)。因此，节律性刺激可以诱导尖峰神经元中的统计学上更高程度的相干性，所述更高程度的相干性促进特定认知过程的诱导(或阻碍所述过程)。在此，透视图略有不同(相干共振)，但是下划线机制与目前为止描述的机制相似(随机共振)，并且另外的关键因素是在刺激的特定节律性下的重复。
20世纪70年代，英国生物物理学家和心理生物学家C.Maxwell Cade监测了高级冥想者以及其学生中的300名学生的脑波模式。在此，发现最高级的冥想者的特定脑波模式与其学生中的其余学生不同。他指出，这些冥想者示出高活性的α脑波，其伴随有为α波的幅度的约一半的β、θ以及甚至δ波。参见Cade,“清醒的思想：生物反馈和更高意识状态的发展”(Dell,1979)。Anna Wise延伸了Cade的研究并且发现包含作曲家、发明家、艺术家、运动员、舞蹈家、科学家、数学家、大公司的CEO和总裁等杰出成就者的脑波模式与一般表现者不同，并且在β、α、θ和δ脑波之间具有特定平衡，其中α的幅度最强。参见Anna Wise,“高效能思想：掌握脑波以获取洞察力、治愈力和创造力(The High-Performance Mind:MasteringBrainwaves for Insight,Healing,and Creativity)”。
夹带由于所证明的对单个TMS脉冲的EEG响应的特性而是貌似合理的，所述TMS脉冲的谱组成类似于受刺激的皮层的自发振荡。例如，“静息的”视觉皮层(Rosanova等人,2009)或运动皮层(Veniero等人,2011)的TMS触发α波，这是两种类型的皮层在静息状态下的自然频率。利用夹带假设，噪声生成框架移动到更复杂且延伸的水平，在所述更复杂且延伸的水平中，噪声与正在进行的活动同步。尽管如此，解释结果的模型将不会改变，刺激将与系统相互作用，并且最终结果将取决于引入或修改噪声水平。夹带假设对在线重复性TMS范例的频率参与以及诱导相位对准的可能性进行了明确预测，即通过外部spTMS重置正在进行的脑振荡(Thut等人,2011a,2012；Veniero等人,2011)。夹带假设在获得关于脑如何运作的知识方面，而不是在单个过程在哪里发生并且如何发生方面，优于定位方法。TMS脉冲可以相位对准目标皮层的自然、正在进行的振荡。当另外的TMS脉冲与相位对准的振荡同步(即，以相同频率)递送时，将发生另外同步的相位对准，这将使目标区的振荡与TMS训练共振。因此，当将TMS被频率调谐为潜在脑振荡时，可能会预期夹带(Veniero等人,2011)。
双耳节拍：双耳节拍是听觉脑干响应，其源于每个半球的上橄榄核。其是由两个不同的听觉冲动的相互作用产生的，所述两个不同的听觉冲动源于相对的耳，低于1000Hz并且频率的不同之处介于一与30Hz之间。例如，如果向右耳呈现400Hz的纯音调并且同时向左耳呈现410Hz的纯音调，则将随着两种波形在上橄榄核内同相和异相啮合而经历10Hz的幅度调制的驻波，即两个音调之间的差异。此双耳节拍在话语的普通意义上是听不到的(人类的听力范围是20-20,000Hz)。其被感知为听觉节拍并且理论上可以用于通过频率跟随响应(FFR)来夹带特定神经节律-皮层电位以外部刺激的频率夹带或共振的趋势。因此，理论上可能的是利用特定双耳节拍频率作为神志管理技术来夹带特定皮层节律。双耳节拍似乎与脑中的脑电图(EEG)频率跟随响应相关联。
嵌入了双耳节拍的与音乐或各种发爆声或背景声音混合在一起的音频的用途是多种多样的。其范围为放松、冥想、应力减少、疼痛管理、睡眠质量改善、睡眠需要减少、超级学习、创造力和直觉增强、远距离观看、心灵感应以及体外经历和清醒的梦。嵌入双耳节拍的音频通常与各种冥想技术以及积极的肯定和可视化组合。
当呈现两种不同频率的信号时(每只耳朵一种信号)，脑会检测这些信号之间的相位差。在自然情况下，检测到的相位差将提供方向信息。当利用立体声耳机或扬声器听到这些相位差时，脑会以不同方式处理此反常信息。两种信号的感知整合发生，从而产生第三“节拍”频率的感觉。随着两种不同输入频率同相和异相啮合，信号之间的差异会忽大忽小。由于这些不断增加和减少的差异，听到了幅度调制的驻波-双耳节拍。在两个听觉输入之间的差异的频率下，双耳节拍被感知为波动节律。证据表明，双耳节拍是在脑干的上橄榄核(听觉系统中对侧整合的第一部位)产生的。研究还表明，频率跟随响应源于下丘。此活动被传导到皮层，在皮层处，头皮电极可以记录此活动。在作为EEG谱的特性的低频率下(<30Hz)可以容易地听到双耳节拍。
同步的脑波长期与冥想和催眠状态相关联，并且嵌入有双耳节拍的音频具有诱导和改善此神志状态的能力。其原因是生理上的。每个耳都“硬接线”(可以这么说)到脑的两个半球。每个半球都具有其自身的橄榄核(声音处理中心)，所述橄榄核从每个耳接收信号。为了保持此生理结构，当感知到双耳节拍时，实际上存在两个相等幅度和频率的驻波，每个半球一个。因此，存在两个单独的驻波将每个半球的一部分夹带成相同的频率。双耳节拍似乎有助于表明的冥想和催眠神志状态下的半球同步。还通过增加脑的左半球与右半球之间的跨溶胶通信增强了脑功能。en.wikipedia.org/wiki/Beat_(acoustics)#Binaural_beats.
时频分析：Brian J.Roach和Daniel H.Mathalon,“事件相关的EEG时频分析：精神分裂症的早期γ带相位锁定的测量和分析概述(Event-related EEG time-frequencyanalysis:an overview of measures and analysis of early gamma band phaselocking in schizophrenia)”《精神分裂症通报(Schizophrenia Bull)》,美国(USA),2008；34:5:907-926描述种用于EEG时频分析的机制。可以对EEG信号执行傅立叶和小波变换(和其逆)。
存在很多用于EEG数据的时频分解的方法，包含短期傅立叶变换(STFT)(GaborD.,“通信理论(Theory of Communication)”《电气工程师学会杂志(J.Inst.Electr.Engrs.)》1946；93:429-457)、持续(Daubechies I.,“小波十讲(TenLectures on Wavelets)”宾夕法尼亚州费城：工业和应用数学学会(Philadelphia,Pa:Society for Industrial and Applied Mathematics)；1992:357,21.Combes JM、Grossmann A、Tchamitchian P,“小波：国际会议的时频方法和相位空间程序(Wavelets:Time-Frequency Methods and Phase Space-Proceedings of the InternationalConference)”；1987年12月14-18日；法国马赛(Marseille,France))或离散(Mallat SG.“多分辨率信号分解的理论：小波表示(A theory for multiresolution signaldecomposition:the wavelet representation)”《IEEE模式分析和机器智能汇刊(IEEETrans Pattern Anal Mach Intell)》1989；11:674-693)小波变换、希尔伯特变换(LyonsRG,“了解数字信号处理(Understanding Digital Signal Processing)”第2版,新泽西上萨德尔河：普伦蒂斯霍尔出版社(Upper Saddle River,NJ:Prentice Hall PTR)；2004:688)和匹配追踪(Mallat S,Zhang Z.,“匹配追踪与时频词典(Matching pursuits withtime-frequency dictionaries)”,《IEEE信号处理汇刊(IEEE Trans.Signal Proc.)》1993；41(12):3397-3415)。可以使用例如具有小波工具箱的MatLab(www.mathworks.com/products/wavelet.html.)来实施原型分析系统。
单指令多数据处理器，如包含nVidia CUDA环境或AMD Firepro高性能计算环境的图形处理单元，是已知的并且可以用于通用计算、可具体用于数据矩阵变换。
可以以允许并行化的形式来表示统计分析，所述统计分析可以使用各种并行处理器来有效地实施，所述并行处理器的常见形式是存在于典型图形处理器(GPU)中的SIMD(单指令多数据)处理器。
已采用了人工神经网络来分析EEG信号。
主分量分析：主分量分析(PCA)是使用正交变换将可能相关的变量的一组观察结果转化成被称为主分量的线性不相关的变量的一组值的统计程序。如果对于p变量存在n个观察结果，则不同主分量的数量为min(n-1,p)。此变换是以此方式定义的：第一主分量具有最大可能方差(也就是说，尽可能多地考虑数据中的可变性)，并且每个后续分量进而在与前面的分量正交的约束下具有最高方差。所产生的向量是不相关的正交基组。PCA对原始变量的相对缩放敏感。PCA是基于真实特征向量的多元分析中最简单的。通常，其操作可以被视为以最佳解释数据中的方差的方式揭示数据的内部结构。如果将多元数据集可视化为高维度数据空间(每变量1个轴)中的坐标集，则PCA可以为用户提供低维度突片，即当从其最信息化的角度查看时对此对象的投影。这是通过使用仅前几个主分量完成的，使得经变换的数据的维数降低。PCA与因子分析密切相关。因子分析典型地并入关于潜在结构的更多域特定的设想并且对略微不同的矩阵的特征向量进行求解。PCA还与规范相关分析(CCA)相关。CCA定义了最优地描述两个数据集之间的交叉协方差的坐标系，而PCA定义了最优地描述单个数据集中的方差的新正交坐标系。参见en.wikipedia.org/wiki/Principal_component_analysis。
验证性因子分析的一般模型表示为x＝α+Λξ+ε。协方差矩阵表示为E[(x-μ)(x-μ)′]＝ΛΦΛ′+Θ。如果残余协方差矩阵Θ＝0并且潜在因子之中的相关矩阵Φ＝Ι，则因子分析等同于主分量分析，并且所产生的协方差矩阵被简化为Σ＝ΛΛ′。当存在p数量的变量并且提取了所有p分量(或因子)，则此协方差矩阵可替代地可以被表示为Σ＝DΛD′,或Σ＝λDΑD′,，其中D＝n×p特征向量的正交矩阵，并且Λ＝λΑ，特征值的p×p矩阵，其中λ是标量并且A是其元素与Σ的特征值成正比的对角矩阵。以下三个成分确定所观察的数据的几何特征：λ参数化观察的体积，D指示朝向，并且A表示观察的形状。
当群体异质性被明确地假设为处于基于模型的聚类分析中时，观察到的协方差矩阵被分解为以下一般形式
Figure BDA0003065971300000411
其中λk参数化第k个聚类的体积，Dk指示所述聚类的朝向，并且Ak表示所述聚类的形状。下标k表示每个组成(或聚类)可以具有不同的体积、形状和朝向。
假设将值带入
Figure BDA0003065971300000412
的随机向量X的平均矩阵和协方差矩阵分别为μX和∑X。λ1＞λ2＞…＞λm＞0是∑X的有序特征值，使得∑X的第i特征值意指其的第i最大。类似地，当向量αi对应于∑X的第i特征值时，其是∑X的第i特征向量。要得出主组成(PC)的形式，考虑最大化
Figure BDA0003065971300000421
的优化问题，受
Figure BDA0003065971300000422
约束。使用了拉格朗日乘子方法(Lagrangemultiplier method)来解决此问题。
Figure BDA0003065971300000423
Figure BDA0003065971300000424
因为-φ1是∑X的特征值，其中α1是对应的归一化的特征向量，则由所选α1将
Figure BDA0003065971300000425
最大化为∑X的第一特征向量。在此情况下，
Figure BDA0003065971300000426
被命名为X的第一PC，α1是z1的系数的向量，并且var(z1)＝λ1。
为了找到第二
Figure BDA0003065971300000427
需要最大化
Figure BDA0003065971300000428
受与z1不相关的z2约束。因为
Figure BDA0003065971300000429
所以此问题被同等地设置为最大
Figure BDA00030659713000004210
受
Figure BDA00030659713000004211
约束，并且
Figure BDA00030659713000004212
仍利用拉格朗日乘子方法。
Figure BDA00030659713000004213
Figure BDA00030659713000004214
Figure BDA00030659713000004215
Figure BDA00030659713000004216
因为-φ2是∑X的特征值，其中α2是对应的归一化的特征向量，则由所选α2将
Figure BDA00030659713000004217
最大化为∑X的第二特征向量。在此情况下，
Figure BDA00030659713000004218
被命名为X的第二PC，α2是z2的系数的向量，并且var(z2)＝λ2。继续以此方式，其可以示出，第i PC
Figure BDA00030659713000004219
是通过将αi选择为∑X的第i特征向量构建的并且具有方差λi。关于PCA的关键结果是，主分量是原始数据的仅线性函数集，所述仅线性函数不相关且具有正交系数向量。
对于任何正整数p≤m，使B＝[β1,β2,...,βp]为具有标准正交列的真m×p矩阵，即，
Figure BDA0003065971300000431
并且Y＝BTX。通过取B＝[α1,α2,...,αp]最大化Y的协方差矩阵迹，其中αi是∑X的第i特征向量。因为∑X与所有不同特征值是对称的，因此{α1,α2,...,αm}是标准正交基，其中αi为∑X的第i特征向量，并且可以将B的列表示为
Figure BDA0003065971300000432
i＝1,...,p，因此具有B＝PC，其中P＝[α1,...,αm]，C＝{cij}是m×p矩阵。然后，PT∑XP＝Λ，其中Λ为其第k对角元素是λk的对角矩阵，并且Y的协方差矩阵是
Figure BDA0003065971300000433
其中
Figure BDA0003065971300000434
是C的第i行。因此，
Figure BDA0003065971300000435
因为CTC＝BTPPTB＝BTB＝I，所以
Figure BDA0003065971300000436
并且C的列标准正交。通过格拉姆-施密特(Gram-Schmidt)方法，C可以扩展到D，使得D使其列作为
Figure BDA0003065971300000437
的标准正交基并且含有C作为其第一p列。D是正方形形状，因此是正交矩阵并且使其列作为
Figure BDA0003065971300000438
的另一标准正交基。C的一个行是D的一个列的一部分，因此
Figure BDA0003065971300000439
i＝1,...,m。考虑约束
Figure BDA00030659713000004310
并且目标
Figure BDA00030659713000004311
得出如果
Figure BDA00030659713000004312
(i＝1,...,p)并且
Figure BDA00030659713000004313
(i＝p+1,...,m)，则最大化trace(∑Y)。当B＝[α1,α2,...,αp]时，直接计算得到C是除了cii＝1，i＝1,...,p之外的全零矩阵。这满足最大化条件。实际上，通过取B＝[γ1,γ2,...,γp]，其中{γ1,γ2,...,γp}是span{α1,α2,...,αp}的子空间的任何标准正交基，也满足最大条件，从而得到Y的相同协方差矩阵迹。
假设希望通过其投影到跨越B的列的子空间来近似随机向量X，其中B＝[β1,β2,...,βp]是具有标准正交列，即
Figure BDA00030659713000004314
的m×p矩阵。如果
Figure BDA00030659713000004315
是X的每个组成的残余方差，则当B＝[α1,α2,...,αp]时最小化
Figure BDA0003065971300000441
其中{α1,α2,...,αp}是∑X的第一p特征向量。换言之，当B＝[α1,α2,...,αp]时，最小化X-BBTX的协方差矩阵迹。当作为数据分析方法中的通常应用的处理步骤，E(X)＝0时，此性质是说当B＝[α1,α2,...,αp]时，最小化E||X-BBTX||2。
随机向量X到跨越B的列的子空间的投影是
Figure BDA0003065971300000442
然后残余向量是ε＝X-BBTX，其具有协方差矩阵
∑ε＝(I-BBT)∑X(I-BBT)。然后,
Figure BDA0003065971300000443
另外，已知：
trace(∑XBBT)＝trace(BBT∑X)＝trace(BT∑XB)
trace(BBT∑XBBT)＝trace(BT∑XBBTB)＝trace(BT∑XB)。
最后的方程式来自事实B具有标准正交列。因此，
Figure BDA0003065971300000444
为了最小化
Figure BDA0003065971300000445
其足够最大化trace(BT∑XB)。这可以通过选择B＝[α1,α2,...,αp]来完成，其中{α1,α2,...,αp}是如上∑X的第一p特征向量。
参见Pietro Amenta、Luigi D'Ambra,“具有外部信息的广义约束的主分量分析(Generalized Constrained Principal Component Analysis with ExternalInformation)”,(2000)。假定分别在阶(n×p1),...,(n×pK)和(n×q1),...,(n×qS)的矩阵Xk(k＝1,...,K)和Ys(s＝1,...,S)中收集了关于K个解释变量集和n个统计单位的S个标准变量的数据。假设在不丢失一般性的情况下，标识Xk和Ys的其中Dn＝diag(1/n)的变量的空间度量的统一矩阵、统计单位的权重矩阵。此外假定，Xk和Ys以权重Dn为中心。
分别将X＝[X1|...|XK]和Y＝[Y1|…|YS]设为阶(n×∑kpk)和(n×∑sqs)的连接的K和S矩阵列。还使WY＝YY'，同时指出vk为每个Xk的线性组合的系数向量(pk,1)，使得zk＝Xkvk。使Ck为维度pk×m(m≤pk)的矩阵，与集k的外部信息解释变量相关联。
具有外部信息的广义CPCA(GCPCA)(Amenta,D'Ambra,1999)在于寻找K系数向量vk(或以相同的方式，K线性组合zk)同时受制约C'kvk＝0约束，使得：
Figure BDA0003065971300000451
或者，以等效的方式，
Figure BDA0003065971300000452
其中A＝Y'X，B＝diag(X′1X1,…,X'KXK)，C'＝[C′1|...|C'k]，v'＝(v1'|...|vk')并且f＝B0.5v，其中
Figure BDA0003065971300000453
受约束的最大问题证明是标准
Figure BDA0003065971300000454
的延伸(Sabatier,1993)，其中更多标准变量集具有外部信息。此受约束的最大问题的解导致求解特征方程式。
(PX-PXB-1C)WYg＝λg
其中g＝Xv，
Figure BDA0003065971300000455
是与
Figure BDA0003065971300000456
的直和分解相关联的斜投影算子。
Figure BDA0003065971300000457
分别在
Figure BDA0003065971300000458
并且PC＝C(C'B-1C)-1C'B-1的情况下，I和B-1为到跨越矩阵Xk和C的列的子空间正交投影算子。此外，
Figure BDA0003065971300000459
是到跨越矩阵XB-1C.的列的子空间的正交投影算子。开始于关系
Figure BDA0003065971300000461
(其是从表达式(I-PC)X'WYg＝λBv获得的)，系数向量vk和最大化(1)的线性组合zk＝Xkvk分别可以通过关系
Figure BDA0003065971300000462
和
Figure BDA0003065971300000463
给出。
解特征向量g可以书写为线性组合zk之和：g＝∑kXkvk。注意，根据斯图姆定理(Sturm theorem)，与特征系统相关联的特征值小于或等于GCPCA特征系统中的那些：
Figure BDA0003065971300000464
空间主分量分析：使J(t,i；α,s)为受试者s在条件α下在刺激起始之后的t个时间帧处体素i中的如LORETA估计的目前密度。使area:Voxel→fBA为向每个体素i∈体素指定对应fBA b∈fBA的函数。在第一预处理步骤中，针对每受试者s计算对每个Fba求平均的电流密度的值
Figure BDA0003065971300000465
其中Nb是受试者s在条件α下，fBA b中的体素的数量。
在第二分析阶段，使每受试者s的并且在条件α下的来自每个fBA b的平均当前密度x(t,b；α,s)经受相关矩阵和方差极大旋转的空间PCA分析。
在本研究中，空间PCA使用以上定义的fBA作为沿已对EEG进行采样的时间历元(0-1000ms；512个时间帧)采样的变量，并且估计了逆解。收集每受试者和条件下的空间矩阵(每个矩阵被大小设定为b×t＝36×512个元素)并且对其进行PCA分析，包含协方差矩阵的计算；特征值分解和方差极大旋转，以最大化因子荷载。换言之，在空间PCA分析中，将每受试者在每种条件下的平均电流密度近似为
Figure BDA0003065971300000466
在此x(t；α,s)∈R36是表示fBA的时间相关性激活的向量的情况下，x0(α,s)是其平均激活，并且xk(α,s)和ck是主分量以及如使用主分量分析所计算的其对应系数(因子荷载)。
非线性维数降低：高维度数据，意指需要多于两个或三个维度来表示的数据，可能难以解释。一种简化方法是假定所关注的数据位于更高维度的空间内的嵌入式非线性流形上。如果流形的维度足够低，则数据可以在低维度空间中可视化。非线性方法可以广泛地分为两组：提供映射的那些(从高维度空间到低维度嵌入或反之亦然)，以及仅提供可视化的那些。在机器学习的上下文中，映射方法可以被视为初步特征提取步骤，在所述初步特征提取步骤之后，应用模式识别算法。典型地，仅提供可视化的那些是基于接近数据(即距离测量结果)的。相关线性分解方法包含独立分量分析(ICA)、主分量分析(PCA)(也被称为Karhunen-Loève变换-KLT)、奇异值分解(SVD)和因子分析。
自组织图(SOM，也被称为Kohonen图)和其概率变体生成拓扑映射(GTM)使用嵌入式空间中的点表示来形成基于从嵌入式空间到高维度空间的非线性映射的隐变量模型。这些技术与密度网络上的工作有关，所述密度网络也基于大约相同的概率模型。
主曲线和流形为非线性维数降低提供了自然几何框架并且并通过显式构造嵌入式流形并且通过使用标准几何投影编码到流形中来延伸对PCA的几何解释。如何定义流形的“简单性”是问题相关性的，然而，其通常通过流形的固有维度和/或平滑度来度量。通常，主流形被定义为对优化问题的解。目标函数包含数据近似质量和用于流形的弯曲的一些惩罚项。流行的初始近似是通过线性PCA、Kohonen的SOM或自动编码器生成的。弹性图方法为主流形学习提供了期望最大化算法，其中在“最大化”步骤处最小化了二次能量泛函。
自动编码器是被训练以近似同一函数的前馈神经网络。即，其被训练以从值的向量映射到同一向量。当用于维数降低目的时，网络中的隐藏层之一被限制为含有仅少量网络单元。因此，网络必须学习将向量编码为少量维度并且然后将其解码回到原始空间中。因此，网络的前半部分是从高维空间映射到低维度空间的模型，并且后半部分是从低维空间映射到高维度空间的模型。尽管自动编码器的观念很老，但是直到最近，通过使用受制约的玻尔兹曼机(restricted Boltzmann machine)和堆叠式去噪自动编码器，对深度自动编码器进行训练才成为可能。与自动编码器相关的是NeuroScale算法，所述NeuroScale算法使用受多维度缩放和Sammon映射启发的应力函数(请参见下文)来学习从高维度空间到嵌入式空间的非线性映射。NeuroScale中的映射基于径向基函数网络。
高斯过程潜变量模型(GPLVM)是使用高斯过程(GP)来找到高维度数据的较低维度的非线性嵌入的概率性维数降低方法。其为PCA的概率公式的延伸。模型被概率性地定义，并且然后潜变量被边缘化，并且通过最大化可能性获得参数。像核PCA一样，其使用核函数来形成非线性映射(呈高斯过程的形式)。然而，在GPLVM中，映射是从嵌入式(潜)空间到数据空间(像密度网络和GTM一样)，然而在核PCA中，其是沿相反方向。其最初是针对高维度数据的可视化提出的，但已延伸为在两个观察空间之间构建共享流形模型。已专门针对人运动建模提出了GPLVM及其许多变体，例如，反向约束的GPLVM、GP动态模型(GPDM)、平衡的GPDM(B-GPDM)和拓扑约束的GPDM。为了在步态分析中捕获姿势和步态流形的偶联效应，提出了多层联合步态-姿势流形。
曲线分量分析(CCA)在输出空间中寻找点的配置，所述配置尽可能地保留原始距离，同时专注于输出空间中的小距离(与Sammon的映射相反，所述Sammon的映射专注于原始空间中的小距离)。应当注意，CCA作为迭代学习算法，实际上开始于专注于大距离(像Sammon算法一样)，然后逐渐将专注改为小距离。小距离信息将重写大距离信息，条件是必须在两者之间做出折衷。CCA的应力函数与右Bregman散度之和有关。曲线距离分析(CDA)训练自组织神经网络来拟合流形，并且试图保留其嵌入中的测地距离。其基于“曲线分量分析”(其延伸了Sammon的映射)，但相反使用了测地距离。微分同胚维数降低或Diffeomap学习将数据输送到较低维度的线性子空间的平滑微分同胚映射。所述方法解决了平滑时间索引的向量场，使得沿开始于数据点处的场的流将终止于较低维度的线性子空间处，从而尝试在正和逆映射两者下均保留成对差异。
用于流形学习的通用算法是核主分量分析(核PCA)，其是PCA和核技巧的组合。PCA开始于计算M×n矩阵X的协方差矩阵。其然后将数据投影到所述矩阵的前k个特征向量中。相比之下，KPCA开始于在变换到较高维度的空间之后计算数据的协方差矩阵。然后，其将经变换的数据投影到所述矩阵的前k个特征向量中，就像PCA一样。其使用核技巧来排除大量计算，使得可以在不实际计算Φ(x)的情况下执行整个过程。当然，必须选择Φ，使得其具有已知对应核。
拉普拉斯特征映射(也被称为局部线性特征映射，LLE)是通过构造数据相关的核矩阵执行的核PCA的具体实例。KPCA具有内部模型，因此其可以用于将点映射到其在训练时间不可用的嵌入中。拉普拉斯特征映射使用谱技术来执行维数降低。此技术依赖于以下基本假设：数据位于高维度空间中的低维度流形中。此算法不能嵌入样本点之外，但是存在基于再生核希尔伯特空间正则化(Reproducing kernel Hilbert space regularization)的技术来添加此能力。此类技术也可以应用于其它非线性维数降低算法。如主分量分析等传统技术不考虑数据的固有几何形状。拉普拉斯特征映射根据数据集的邻域信息构建图。每个数据点充当图上的节点，并且节点之间的连接性通过邻域点的接近度控制(例如，使用k最近邻接算法)。由此生成的图可以被视为高维度空间中的低维度流形的离散近似。基于图对成本函数的最小化确保了流形上彼此靠近的点在低维度空间中彼此靠近地映射，从而保留局部距离。拉普拉斯–贝尔特拉米算子(Laplace–Beltrami operator)在流形上的特征函数充当嵌入维，因为在温和条件下，此算子具有可计数的谱，所述可计数的谱是流形上的平方可积函数的基础(与单位圆流形上的傅里叶级数比较)。尝试将拉普拉斯特征映射放置在坚实的理论基础上已经取得了一些成功，因为在某些非制约性假定下，随着点的数量达到无穷大，图拉普拉斯矩阵已示出收敛到L拉普拉斯–贝尔特拉米算子。在分类应用中，低维度流形可以用于对可以根据观察到的实例组定义的数据类进行建模。每个观察到的实例都可以通过两个被称为“内容”和“风格”的独立因素来描述，其中“内容”是与类的本质相关的不变因素，并且“风格”表示所述类在实例之间的变化。不幸的是，当训练数据由在风格方面变化很大的实例组成时，拉普拉斯特征映射可能不能产生所关注的类的相干表示。在通过多元序列表示的类的情况下，已提出结构化拉普拉斯特征映射，以通过在拉普拉斯特征映射邻域信息图内添加另外的约束以更好地反映类的固有结构来克服此问题。更具体地，图用于编码多元序列的顺序结构并且最小化风格变化、不同序列的数据点以及甚至序列内的接近度(如果其含有重复的话)两者。使用动态时间规整，通过找到表现出高度相似性的多元序列的区段之间和区段内的对应性来检测接近度。
与LLE一样，Hessian LLE也基于稀疏矩阵技术。相比于LLE，其倾向于得到更高质量的结果。不幸的是，其计算复杂性非常昂贵，因此其不适用于大量采样的流形。其不具有内部模型。修改的LLE(MLLE)是另一个LLE变体，其在每个邻域中使用多个权重来解决导致LLE映射失真的局部权重矩阵调制问题。MLLE产生类似于Hessian LLE的鲁棒投影，但是没有显著的另外的计算成本。
流形对齐利用以下假定：由相似生成过程产生的全异数据集将共享相似的潜在流形表示。通过学习从每个原始空间到共享流形的投影，恢复了对应性，并且可以将知识从一个域转移到另一个域。大多数流形对齐技术考虑仅两个数据集，但所述概念延伸到任意许多初始数据集。扩散映射利用热扩散与随机游走之间的关系(马尔可夫链(MarkovChain))；在流形上的扩散算子与对在图上定义的函数进行运算的马尔可夫转移矩阵之间进行类推，所述函数的节点是从流形采样的。关系透视映射是多维度缩放算法。算法通过模拟封闭流形上的多粒子动力学系统在流形上找到数据点的配置，其中数据点映射到粒子，并且数据点之间的距离(或相异度)表示排斥力。随着流形的大小逐渐生长，多粒子系统逐渐冷却并且收敛到反映数据点的距离信息的配置。局部切线空间对齐(LTSA)是基于以下直觉：当流形正确展开时，与流形的所有切线超平面都将变得对齐。其开始于计算每个点的k最近邻。其通过计算每个局部邻域中的前d个主分量来计算每个点处的切线空间。然后，其进行优化以找到与切线空间对齐的嵌入。局部多维度缩放在局部区域中执行多维度缩放并且然后使用凸优化将所有件拟合在一起。
最大方差展开先前被称为半定嵌入。此算法的直觉是，当流形正确展开时，点之上的方差最大化。此算法也开始于找到每个点的k最近邻。其然后试图解决最大化所有非相邻点之间的距离的问题，所述距离受约束使得相邻点之间的距离保留。非线性PCA(NLPCA)使用反向传播来训练多层感知器(MLP)以与流形拟合。与仅更新权重的典型MLP训练不同，NLPCA更新权重和输入两者。也就是说，权重和输入两者均被视为潜在值。训练之后，潜在输入是观察到的向量的低维度表示，并且MLP从所述低维度表示映射到高维度观察空间。流形雕刻使用渐进式优化来找到嵌入。像其它算法一样，其计算k最近邻并且尝试寻找保留局部邻域中的关系的嵌入。其在同时在较低维度中调整点以保留那些关系的同时，缓慢地将方差缩放到较高维度之外。
Ruffini(2015)讨论了多通道经颅电流刺激(tCS)系统，所述tCS系统提供了EEG引导的经优化的非侵入性脑刺激的可能性。使用了tCS电场逼真的脑模型来创建正向“引导场”矩阵，并且从中，采用了EEG逆变器以进行皮层映射。开始于EEG，定义了可以产生观察到的EEG电极电压的2D皮层表面偶极场。
Schestatsky等人(2017)讨论了经颅直流电刺激(tDCS)，所述tDCS利用诱导神经元膜激励性的偏移的恒定电流通过头皮进行刺激，从而导致皮层活动的继发性变化。尽管tDCS对潜在皮层具有大部分神经调制作用，但是tDCS的作用也可以在远处的神经网络中观察到。对tDCS的作用的伴行EEG监测可以提供关于tDCS的机制的有价值信息。EEG发现可以是tDCS的作用的重要替代标志并且因此可以用于优化其参数。此经组合的EEG-tDCS系统还可用于特征在于皮层可激励性的异常峰的神经病状(如癫痫)的预防性治疗。此系统将是非侵入性闭环装置的基础。tDCS和EEG可以并行使用。
EEG分析方法已经出现，在所述EEG分析方法中，分析了在单个事件相关数据记录中EEG动力学中的事件相关的变化。参见Allen D.Malony等人,“用于整合电磁神经成像和分析的计算神经信息学(Computational Neuroinformatics for IntegratedElectromagnetic Neuroimaging and Analysis)”,PAR-99-138。Pfurtscheller报告了用于量化刺激之后对α带(大约10-Hz)活动的平均瞬时抑制的方法。在各种窄频带(4-40Hz)中观察到事件相关的失步(ERD，谱幅度减小)和事件相关的同步(ERS，谱幅度增大)，所述窄频带系统地取决于任务和认知状态变量以及刺激参数。Makeig(1993)报道了整个EEG谱中的事件相关的变化，从而产生了其称为事件相关的谱扰动(ERSP)的2-D时间/频率度量。此方法避免了与先验窄频带的分析相关联的问题，因为所关注的用于分析的带可以基于完整时间/频率变换的显著特征。Rappelsburger等人引入了事件相关的相干性(ERCOH)。已经测试了多种其它信号处理度量可用于EEG和/或MEG数据，包含基于混沌理论和双谱的维度度量。已经提出使用神经网络以用于应用于临床和实际问题的EEG模式识别，但是为了对涉及的神经动力学进行明确建模，通常不采用这些方法。神经动力学作为用于物理治疗的方法，是对神经系统的动员。所述方法依赖于通过对神经组织和神经系统周围的非神经结构进行机械治疗来影响疼痛和其它神经生理。身体通过肌肉骨骼系统为神经系统提供机械接口。随着移动，肌肉骨骼系统在神经组织中施加不均匀的应力和移动，这取决于局部解剖和机械特性以及身体移动的模式。这激活了神经组织中的一系列机械和生理响应。这些响应包含神经滑动、加压、伸长、张力以神经内微循环、轴突运输和冲动交通的变化。
越来越大数量的EEG(和MEG)通道的利用度和关注直接导致如何组合来自不同通道的数据的问题。出于此目的，Donchin提倡使用基于主分量分析(PCA)的线性因子分析方法。时间PCA假定在所有数据条件下，激活每个得到的分量的时间过程都是相同的。因为这对于许多数据集来说是不合理的，所以潜在地更关注空间PCA(通常之后是分量旋转程序，如Varimax或Promax)。为此，已经提出了将PCA的几个变体用于ERP分解。
Bell和Sejnowski发布了基于信息论的迭代算法，所述迭代算法通过最小化其互信息将线性混合的信号分解为时间独立性的。盲源分离的第一方法最小化了观察到的变量之中的第三阶和第四阶相关性并且在模拟方面达到了有限的成功。通用方法使用简单神经网络算法，所述简单神经网络算法使用联合信息最大化或“infomax”作为训练标准。通过使用压缩非线性来变换数据并且遵循所产生的混合物的熵梯度，将十个记录的语音和音乐声源去混合。使用了类似方法来执行盲反卷积，并且使用了“infomax”方法来分解视觉场景。
盲分解对生物医学时间序列分析的第一应用应用了infomax独立分量分析(ICA)算法来分解EEG和事件相关电位(ERP)数据，并且报告了ICA对监测警觉性的用途。这将伪像和EEG数据分为由空间稳定性和时间独立性定义的组成分量。ICA还可以用于在求平均之前从持续或事件相关的(单次实验)EEG数据中去除伪像。Vigario等人(1997)使用不同的ICA算法支持使用ICA来标识MEG数据中的伪像。同时，对ICA的广泛关注已导致对生物医学数据以及其它领域的多种应用(Jung等人,2000b)。与EEG/MEG分析最相关，ICA在分离功能性磁共振成像(fMRI)数据的功能独立分量方面有效。
自从原始infomax ICA算法发布以来，已经提出了一些延伸。并入“自然梯度”术语避免了矩阵求逆，从而大大加速了算法收敛并且使其可用于与个人计算机一起用于大数据EEG和fMRI数据集。初始“球体化”步骤进一步增加了算法收敛的可靠性。原始算法假定源具有激活值的“稀疏”(超高斯)分布。最近在“延伸的ICA”算法中放松了此制约，所述延伸的ICA算法允许标识超高斯源和次高斯源两者。信号处理文献中出现了许多变体ICA算法。通常，这些做出了关于要分开的分量的时间或空间结构的更具体的假定，并且相比于infomax算法，其典型地是更计算密集型的。
由于单独电极(或磁传感器)各自记录脑源和非脑源的混合物，因此难以解释和比较跨头皮通道的谱度量。例如，两个电极信号之间的相干性的增加可以反映出投影到两个电极的强脑源的激活，或者主要投影到电极之一的脑发生器的去激活。然而，如果EEG(或MEG)数据的独立分量可以被视为用于测量功能不同的脑网络内的活动，则独立分量之间的事件相关的相干性可以揭示出其偶联和解耦(在一个或多个EEG/MEG频率下)中的瞬时事件相关的变化。ERCOH分析已应用于选择性关注任务中的独立EEG分量。
发明内容
睡眠病症影响显著部分的成人群体。在美国，介于5千万与7千万之间的成人具有睡眠病症。失眠是最常见的具体睡眠病症，其中约30％的成人报道有短期问题并且10％具有慢性失眠。慢性失眠与记忆退化、对内分泌功能和免疫响应的不利作用以及肥胖症和糖尿病风险的增加相关联。尽管在任何年龄，管理失眠都是一项挑战，但是由于年龄相关的共病性医疗病状的增加和药品使用以及年龄相关的睡眠结构的改变，失眠尤其是危急的老年病状，其会缩短睡眠时间并且损害睡眠质量。因此，提高睡眠质量是老年成人的最常见的健康抱怨之一。广泛地规定了用于缓解失眠的药品。然而，促进睡眠的药剂，如催眠药可能产生不利作用，尤其是在老年人中。即使如褪黑素等天然补品也会造成一些副作用，包含头痛、抑郁、白天嗜睡、头晕、胃痉挛和易怒。
除了睡眠质量随着成人群体的年龄而普遍退化之外，慢波睡眠(SWS)的数量和质量的退化也是尤其令人烦恼的，所述SWS是非REM深度睡眠。SWS在人类的脑修复和恢复中发挥了重要作用。研究已经示出，SWS的15％的减少以及清醒的数量和持续时间的增加与正常老龄化相关联。实验性破坏SWS已示出可增加浅层睡眠、睡眠破碎、白天睡眠倾向并且会损害白天功能。鉴于SWS有助于睡眠持续，因此增强SWS可以导致患有失眠的患者和老年人的睡眠质量和白天功能的改善。此外，累积的证据指向SWS是将短期记忆巩固成长期记忆的时间。最近的研究将SWS的退化与阿尔茨海默氏病和其它形式的失眠的早期发作连接。还表明SWS阶段的丢失可以在这些使人衰弱的年龄相关的疾病中起作用。不幸的是，大多数标准睡眠丸剂在缓解失眠的同时，对于改善SWS成就甚微。一些证据表明，一些催眠药物会改变睡眠结构，从而不利地影响SWS。因此，对用于促进睡眠，特别是老年人群体中缺乏的深度非REM SS(SWS)的非药物技术的需要尚未得到满足。
用于促进睡眠的有前途的非药物方法之一是通过光、声音和/或经颅电刺激(TES)进行的神经调制。纽约市立大学(CUNY)的NE实验室(NE Lab)与神经调制实验室(Neuromodulation Laboratory)协作进行的有限人类实验在其它受试者(受者)体内复制健康供者的期望的SS中示出了前途。健康志愿者的脑电图(EEG)随着其打盹进入阶段1的睡眠而被记录，如由α波的主导所证明的。随后从噪声中对这些EEG记录进行滤波，将其反转并且用于经颅内源性睡眠来源的刺激(tESD)。用利用睡眠的供者的记录的本地脑波调制的tESD刺激的志愿者受试者快速打盹并且进入阶段1的睡眠，如通过EEG、心脏速率、呼吸速率和睡眠后认知测试所证明的。相比于包含假性刺激、tDCS和tACS(10Hz)的研究的对照组相比，这些结果更好。这些结果表明可以使用利用记录的来自在睡觉的健康供者的本地脑波调制的tACS来在另一受试者体内复制健康供者的期望的SS。
存在用于标识健康睡眠或病理性睡眠的不同期的标志的重要研究；标志允许将观察到的EEG分类成睡眠/清醒类别的期之一。申请者不清楚任何研究，所述任何研究旨在：综合标识睡眠期间EEG信号的所有独立组成；以及综合分析独立分量的存在与特定睡眠阶段的统计上显著的相互依赖性。对与睡眠相关联的独立分量的全面标识和分析将允许将这些分量和/或得到的信号用于tACS方案。
从健康人类受试者获得各个睡眠阶段期间脑波的EEG记录并且对所述EEG记录进行预处理。然后对三个睡眠阶段的以及来自至少10位健康受试者的清醒时的EEG记录(例如，通过公共EEG数据库)进行平滑化和滤波。分析EEG记录以标识与具体SS相关的统计上显著的波形分量。基于SS/清醒状况，开发针对EEG的分量系数的模型(例如，线性多元模型)；并且测量所述模型的统计显著性。开发了可以提供安全且有效的神经刺激以诱导期望的SS的刺激方案。
由于睡眠病症，尤其是失眠招致了巨大的经济负担和社会成本。睡眠干扰是成人的常见症状并且与各种因素有关，包含咖啡因、烟草和酒精的使用；睡眠习惯；以共病性疾病。流行病学研究指示，睡眠病症影响很大一部分成人群体。在美国，介于5千万与7千万之间的成人具有睡眠病症。失眠是最常见的具体睡眠病症，其中约30％的成人报道有短期问题并且10％具有慢性失眠。慢性失眠与记忆退化、对内分泌功能和免疫响应的不利作用以及肥胖症和糖尿病风险的增加相关联3。另外，存在与失眠相关联的由于对医疗保健利用的影响、工作域的影响和生活质量而引起的显著经济负担和社会成本。在美国，最近对直接和间接成本的估计每年为1000亿美元往上。尽管在任何年龄，管理失眠都是一项挑战，但是由于年龄相关的共病性医疗病状的增加和药品使用以及年龄相关的睡眠结构的改变，失眠尤其是危急的老年病状，其会缩短睡眠时间并且损害睡眠质量。因此，提高受试者的睡眠质量是老年成人的最常见的健康抱怨之一。
老年人中存在慢波睡眠(SWS)退化。除了睡眠质量随着成人群体的年龄而普遍退化之外，慢波睡眠(SWS)的数量和质量的退化也是尤其令人烦恼的，所述SWS是深度非REM睡眠。SWS在人类的脑修复和恢复中发挥了重要作用。其为睡眠期间最突出的EEG事件并且表现为在非REM睡眠的最深阶段每秒大约发生一次的EEG信号的自发大振荡。研究已经示出，SWS的量的显著减少(约15％减少)以及清醒的数量和持续时间的增加与正常老龄化相关联。鉴于SWS有助于睡眠持续，并且SWS的实验破坏增加了浅层睡眠和睡眠破碎，增强了白天睡眠倾向并且损害了白天功能，SWS的增强可以导致患有失眠的患者和老年人的睡眠维持和白天功能的改善。此外，累积的证据指向SWS作为将短期记忆巩固成长期记忆的时间。最近的研究将SWS的退化与阿尔茨海默氏病和其它形式的失眠的早期发作连接。还表明SWS阶段的丢失可能是这些使人衰弱的年龄相关的疾病的罪魁祸首。
SWS增强是老年人的潜在非病理学疗法。考虑到慢波在睡眠期间的关键作用，做出某些努力以通过增强SWS来提高睡眠功效不足为奇。最近，许多药物已示出可增加SWS。尽管在不同突触部位起作用，但是总体上增强这些药物的作用的慢波是通过增强GABA能传输介导的。具体而言，临床研究示出，噻加宾(tiagabine)和加波沙朵(gaboxadol)两者均增加了睡眠制约之后SWS的持续时间。噻加宾还改善了评估执行功能的认知任务的性能并且减少了睡眠制约对警觉性的消极作用。尽管这些结果是积极的，但用于睡眠增强的药理学方法通常会引起与依赖性和耐受性相关的问题并且通常与残余白天副作用相关联。一些证据表明，一些催眠药物在缓解失眠的同时会改变睡眠结构，从而不利地影响SWS。即使如褪黑素等天然补品也会造成一些副作用，包含头痛、短期感觉抑郁、白天嗜睡、头晕、胃痉挛和易怒。因此，对用于促进睡眠，特别是老年人群体中缺乏的深度非REM SS的非药物技术的需要尚未得到满足。
第一受试者(处于期望的睡眠状态的“供者”)的脑活动可以通过记录如通过脑活动模式，如EEG信号表示的睡眠的神经相关性来捕获。第一受试者的神经相关性的表示用于控制对第二受试者(“受者”)的刺激，试图在受者体内诱导供者的相同脑活动模式，以辅助受者获得通过供者已获得的期望的睡眠状态。
用于非药理地增强深度睡眠的一个策略是基于人工和合成刺激范例，用光、声音、电流或磁场刺激脑。在SWS起始之后，以0.75Hz施加的持续5分钟间隔(由1分钟的断开阶段分开)的间歇性经颅直流电刺激(tDCS)可以增加无刺激间隔期间慢振荡带(<1Hz)中的EEG功率。类似地，在SWS开始时受tDCS刺激会加速受试者的SWA体内稳态衰减。此外，可以通过在非REM睡眠期间使用经颅磁刺激(TMS)直接扰动皮层来触发慢波。其它研究专注于以更生理学自然的方式诱导慢波的可能性。在针对健康成人的更大型研究中，相比于没有提供刺激的假性夜晚相比，前庭设备的双侧电刺激缩短了睡眠起始潜伏期。还评估了躯体感觉和听觉刺激的作用。虽然利用躯体感觉刺激观察到的变化很小，但是声学刺激在增强睡眠慢波方面特别有效。具体地，使用其中音调在通过无刺激间隔隔开的15秒的区块中播放的非间歇性刺激，慢波在刺激区块中似乎惊人地大且数量众多。另外，高密度EEG研究(hdEEG，256个通道)示出，所诱导的慢波的形态、地形和行进模式可与自然睡眠期间观察到的自发慢波的那些区别开。最近的研究发现在音调呈现之后在非REM睡眠期间，EEG SWA增加，并且慢振荡活动(0.5-1Hz)响应于在0.8Hz下在光断开之前2分钟开始并且持续90分钟的持续声学刺激而增加。与先前的具有人工和合成刺激范例的神经刺激方法不同，本刺激方案使用从健康受试者的本地脑活动EEG记录中提取的，通过统计方法(例如，主分量分析或空间主分量分析、自相关等)处理的源来源的波形，所述波形将脑活动的各个分量分开。这些分开的脑EEG活动然后被修改或调制，并且随后被反转并用于经颅内源性睡眠来源的刺激(tESD)。内源性脑波形的应用不仅应保持触发SWS的功效，而且还应减轻与使用合成范例进行的长期脑刺激相关联的安全担忧。
本技术提供了一种通过向第二受试者(受者)移植睡眠状态-一种期望的SS或一系列SS从第一受试者(供者)(或从多个供者)来改善睡眠的方法。(在一些实施例中，第一受试者和第二受试者可以是在不同时间点或基于方案或算法的相同受试者。)
所述方法试图在受试者体内达到从人得到的脑波模式。脑波模式是复杂的，表示经调制的波形的叠加。所述调制优选地基于另一受试者或多受试者的脑波模式来确定。
睡眠是神志的自然周期性暂停，基本上是在其单独阶段几乎不会受人睡眠影响的过程。其是潜意识(在技术意义上)精神状态，表示静息状态、活动模式、活动节律、就绪度、接受性、或其它状态，通常与特定输入无关。本质上，所述第一受试者(处于期望的SS或经历其一系列单独阶段的“供者”)的特定SS或一系列不同SS的睡眠状态是通过记录例如通过如EEG或MEG信号等脑活动模式所表示的睡眠状态的神经相关性捕获的。然后可以使用所述第一受试者的作为直接表示或记录的表示的神经相关性来控制对所述第二受试者(“受者”)的刺激，试图在所述第二受试者(受者)体内诱导与所述第一受试者(供者)中存在的脑活动模式相同的脑活动模式，从而移植所述第一受试者(供者)的所述睡眠状态，以协助所述第二受试者(受者)获得所述供者获得的所述期望的SS。在替代性实施例中，采用来自处于第一SS的所述第一受试者(供者)的信号来防止所述第二受试者(受者)达到第二SS，其中所述第二SS是不期望的睡眠阶段。此外，可以控制所述第二受试者的不同SS的持续时间和定时。这使得能够改变每个SS的单独持续时间或强度以及其出现的顺序。在一些实施例中，可以使用来自所述第一受试者的所述信号来触发所述第二受试者的睡眠或防止睡眠或嗜睡以及相关联的症状，如疲劳、缺乏注意力等。
在一些实施例中，获取睡眠状态信息之前或之后是标识SS、所述第一受试者(供者)或观察者进行直接报告或对生理参数(例如，脑活动模式、心跳、呼吸模式、血液中的氧饱和度、温度、眼睛移动、皮肤阻抗等)进行自动化分析或两者。
在其它实施例中，处理所述脑活动模式不试图对其进行分类或表征，而是对信息进行滤波并且将信息变换成适用于控制对所述第二受试者的刺激的形式。具体地，根据此实施例，考虑了在传统的大脑活动模式分析中尚未可靠地进行分类的细微处。例如，应当理解，所有脑活动都反映在突触电流和其它神经调制中，并且因此，理论上讲，有意识和潜意识信息在理论上可通过脑活动模式分析获得。由于可用的处理技术通常未能区别大量不同脑活动模式，因此所述可用处理技术必定不足但在改进。然而，仅由于计算算法不可用于提取信息并且不表示所述信息不存在。因此，此实施例采用相对原始的脑活动模式数据，如经滤波的或未经滤波的EEG，来控制对所述第二受试者的刺激，而不用完全理解或确切地了解哪些重要信息存在。在一个实施例中，记录脑波并且向另一受试者进行“回放”，类似于记录和回放音乐。此记录回放可以是数字的或模拟的。典型地，刺激可以包含低维数刺激，如立体声-光、双耳、等张音调、触觉或其它感觉刺激、可双向操作以及对频率和相位和/或波形和/或经颅刺激，例如TES、tDCS、HD-tDCS、tACS或TMS的控制。可以并行施加多个不同类型的刺激，例如，视觉、听觉、其它感觉、磁、电。
同样，目前对脑活动模式中的信号分量的基本特性的理解的缺乏并不防止其获取、存储、通信和处理(在某种程度上)。刺激可以是直接的，即对应于脑活动模式的视觉、听觉或触觉刺激，或基于所述第二受试者的脑活动模式的导数控制或反馈控制。
为了全部或部分地解决前述问题和/或本领域的技术人员可能已经观察到的其它问题，本公开提供了如通过在以下所述的实施方案中举例的方式描述的方法、过程、系统、设备、仪器和/或装置。
虽然精神状态典型地被视为是个体内部的且主观的，但实际上，此类状态在个体之间是常见的并且具有可确定的生理和电生理群体特性。进一步地，精神状态可以以绕过正常认知过程的方式外部改变或诱导。在一些情况下，精神状态的触发是主观的，并且因此诱导特定状态所需的特定受试者相关性感觉或激励方案将有所不同。例如，基于暴露历史、社会和文化规范等的差异，嗅觉刺激对不同的人可能具有不同的作用。另一方面，一些精神状态响应触发是规范性的，例如“催人泪下”的媒体。
精神状态以脑波模式表示，并且在正常人中，脑波模式和新陈代谢(例如血流量、氧消耗等)遵循原型模式。因此，通过监测个体的脑波模式，可以确定或估计所述人的状态或一系列精神状态。然而，脑波模式可能与上下文、其它活动和过去的历史相互关连。进一步地，虽然可以观察到原型模式，但是模式中也存在个体变化。脑波模式可以包含指示精神状态的特征空间和事件模式。可以对人的脑波信号进行处理以提取这些模式，所述模式例如可以表示为3-100Hz的频率范围内的半球信号。然后可以将这些信号合成或调制成一个或多个刺激信号，然后采用所述一个或多个刺激信号以试图达到与源类似的脑波模式的方式将对应精神状态诱导到受者体内。不需要针对每种情况新获取要引入的脑波模式。而是，可以从一个或多个个体获取信号，以获得用于各种相应精神状态的模范。一旦确定，就可以将处理后的信号表示存储在非易失性存储器中以供以后使用。然而，在精神状态与上下文或内容或活动之间的复杂相互作用的情况下，可能适当的是，从上下文或内容环境或活动适合于所述情况的单个个体得到信号。进一步地，在一些情况下，没有描述或完全表征单个精神状态、情感或情绪，并且因此从来源获取信号是有效的锻炼。
利用目标脑波模式库，提供了一种系统和方法，其中目标受试者可以沉浸在呈现中，所述呈现不仅包含多媒体内容，还包含伴随多媒体内容的一系列定义的精神状态、情感状态或情绪。以此方式，多媒体呈现就变成完全沉浸式的。在此情况下，可以通过如虚拟现实或增强现实头戴装置等头戴装置来提供刺激。此头戴装置设置有立体显示器、双耳音频以及EEG和经颅刺激电极组。这些电极(如果设置的话)通常递送不痛苦的阈下信号，所述阈下信号通常是AC信号，对应于期望目标模式的期望频率、相位和空间位置。电极还可以用于通过在并行施加期望的模式的同时对其进行破坏性干扰来抵消不期望的信号。头戴装置还可以生成对应于期望的状态的视觉和/或听觉信号。例如，听觉信号可以诱导双耳节拍，所述双耳节拍导致脑波夹带。视觉信号可以包含强度波动或其它调制模式，尤其是下层的，也被调适成引起脑波夹带或对期望的脑波模式的诱导的那些。
头戴装置优选地包含用于接收来自用户的反馈的EEG电极。也就是说，刺激系统试图达到精神状态、情感或来自用户的情绪响应。EEG电极准许确定所述状态是否达到，以及如果未实现，则确定当前状态是什么。可能的是达到期望的脑波模式是状态相关性的，并且因此用于达到期望的状态的刺激的特性取决于受试者的起始状态。确定精神状态、情感或情绪的其它方式包含面部表情分析、面部肌肉的肌电图(EMG)分析、明确的用户反馈等。
提供了一种创作系统，所述创作系统准许内容设计者确定期望什么精神状态，并且然后将这些状态编码到媒体中，然后由媒体再现系统对其进行解释，以生成适当的刺激。如以上所指示的，刺激可以是音频、视觉、多媒体、其它感觉或电或磁脑刺激，并且因此不需要具有经颅电或磁刺激的VR头戴装置。进一步地，在一些实施例中，可以将模式直接编码成视听内容，进行下层编码。
在一些情况下，目标精神状态可以从专家、行为者或专业范例得到。可以基于面部表情、EMG、EEG或其它手段从行为者或范例中读取状态。例如，原型范例参与触发响应的活动，如查看大峡谷(Grand Canyon)或卢浮宫(Louvre)内的艺术品。然后记录或表示范例的响应，并且优选地记录表示响应的脑波模式。然后，将相同经历的表示呈现给目标，目标的目的是也经历与范例相同的经历。这典型地是自愿且公开的过程，因此目标试图自愿遵从期望的经历。在一些情况下，技术的用途并未对目标公开，例如在广告呈现或广告牌中。为了使行为者充当范例，所述人达到的情感必须是真实的。但是，所谓的“方法行为者”确实真实地达到其所传送的情感。然而，在一些情况下，例如在将面部表情用作精神状态的指示时，行为者可以呈现具有不真实的精神状态的期望的面部表情。做出与情感相对应的表情的动作通常会达到针对性的精神状态。
为了校准系统，可以在处于期望的状态下时测量人的脑模式。获取的用于校准或反馈的脑模式不需要具有相同的质量或精度或数据深度，并且实际上可以表示响应而不是主要标记。也就是说，在系统中，在表示精神状态的脑波模式与适于诱导脑状态的刺激模式之间，可能存在一些不对称性。
本发明总体上涉及通过向受试者的脑传送脑波模式来在受试者体内达到精神状态。这些脑波可以是人工的或合成的，或可以从第二受试者(例如，经历真实经历或参与活动的人)的脑得到。典型地，在第二受试者正经历真实经历的同时，得出所述第二受试者的波模式。
一种特殊情况是第一受试者和第二受试者是同一个体。例如，在对象处于特定精神状态时记录脑波模式。相同的模式可以协助在另一时间达到相同的精神状态。因此，在从第二受试者获取脑波信息与使第一受试者经受相应刺激之间可能存在时间延迟。信号可以记录和传输。
时间模式可以通过光(可见或红外)、声音(或超声)、经颅直流电或交流电刺激(tDCS或tACS)、经颅磁刺激(TMS)、深度经颅磁刺激(深度TMS或dTMS)、重复经颅磁刺激(rTMS)、嗅觉刺激、触觉刺激或能够传送频率模式的任何其它手段非侵入性地传送或诱导。在优选实施例中，采用正常人类感觉来刺受试者，如光、声音、气味和触摸。可以采用刺激的组合。在一些情况下，刺激或组合是先天的，并且因此在很大程度上是泛受试者的。在其它情况下，对上下文的响应是学习到的，并且因此是受试者特定的。因此，来自受试者的反馈可能适合于确定适合于达到精神状态的触发和刺激。
此技术可以有利地用于增强对刺激或上下文的精神响应。仍另一方面提供了精神状态的改变。所述技术可以用于人类或动物。
本技术可以采用对神经元活动模式执行的事件相关的EEG时间和/或频率分析。在时间分析中，在时间和空间上对信号进行分析，通常寻找相对于时间和空间的变化。在频率分析中，在分析历元内，使用例如，傅立叶变换(FT或一种实施方案，快速傅立叶变换，FFT)将典型地为样品的时间顺序的数据变换为频域表示，并且分析在历元期间存在的频率。分析的窗口可以是滚动的，并且因此频率分析可以是持续的。在混合时频分析，例如小波分析中，使用“小波变换”，例如离散小波变换(DWT)或持续小波变换(CWT)对历元期间的数据进行变换，所述小波变换能够构造信号的时频表示，所述时频表示提供了很好的时间和频率定位。可以分析经变换的数据随时间和空间的变化。通常，脑波分析的空间方面是解剖学上建模的。在大多数情况下，解剖学被视为通用的，但在一些情况下，存在显著差异。例如，脑损伤、精神疾病、年龄、种族、母语、训练、性别、偏手性和其它因素可能导致脑功能的空间布置不同，并且因此当将情绪从一个个体转移到另一个个体时，优选的是通过经历大致相同的经历并且测量EEG或MEG的空间参数来归一化两个个体的脑解剖学。注意，脑的空间组织是高度持久的，没有损伤或疾病，并且因此这仅需要不频繁地执行。然而，由于电极放置可能不精确，因此可以在电极放置之后执行空间校准。
可以捕获EEG幅度和相位关系的不同方面，以揭示神经元活动的细节。“时频分析”揭示了脑对信息的并行处理，其中脑的各个区域内的各种频率的振荡反映了同时发生和相互作用的多个神经过程。参见Lisman J、Buzsaki G.,“由γ和θ振荡的组合函数形成的神经编码方案(A neural coding scheme formed by the combined function of gamma andtheta oscillations)”,《精神分裂症通报(Schizophr Bull)》,2008年6月16日；doi:10.1093/schbul/sbn060。此时频分析可以采取小波变换分析的形式。这可以用于辅助整合和动态适应性信息处理。当然，变换可以是基本上无损的并且可以以任何方便的信息域表示来执行。这些基于EEG的数据分析揭示了频率特定的神经元振荡以及其在范围从感觉处理到更高阶认知的脑功能中的同步。因此，可以选择性地分析这些模式，以转移到受试者体内或在受试者体内诱导。
可以在高维度空间中执行统计聚类分析，以隔离或分割充当信号源的区域并且表征各个区域之间的偶联。此分析还可以用于建立每个脑区域内的信号类型，以及表征不同信号类型之间的转变的决策边界。这些转变可以是状态相关的，并且因此可以基于时间分析而不是仅并行振荡器状态来检测转变。
在谱分解和/或时间/空间/谱分析期间，各种度量利用从EEG提取的从复杂数据得到的幅度和/或相位角信息。一些度量估计了跨试验在一个通道内EEG的幅度或相位一致性，然而其它度量估计了跨试验在通道之间幅度或相位差异的一致性。除了这两个家族的计算之外，在试验和记录部位内，还存在检查频率之间的偶联的度量。当然，在时频分析领域中，可以检查除了已经提到的那些关系之外的许多类型的关系。
这些感觉处理特定的神经元振荡，例如受试者的(“源”)脑波模式或到经训练以产生期望的状态的人(例如，在“方法”中训练的行为者)的脑波模式可以存储在有形介质上和/或可以根据响应性质利用脑的频率同时传送给受者。参见Galbraith,Gary C.、DarleneM.Olfman和Todd M.Huffman,“选择性关注影响人脑干频率跟随响应(Selectiveattention affects human brain stem frequency-following response)”,《Neuroreport》14,第5期,(2003):735-738,journals.lww.com/neuroreport/Abstract/2003/04150/Selective_attention_affects_human_brain_stem.15.aspx。
根据一个实施例，将对所述第二受试者的刺激与反馈过程组合，以验证所述第二受试者对刺激适当地做出响应，例如，如同所述第一受试者对SS具有预定相似性、其SS与所述第一受试者具有预定差异，或者与基线SS相比具有期望的改变、不是基于脑活动本身或SS的神经相关性，而是基于可以测量、报告或观察到的身体、心理或行为作用。
反馈典型地提供给刺激器的具有至少部分模型基础的控制器，所述控制器改变刺激参数以优化刺激。
如以上所讨论的，模型典型地难以定义。因此，基于模型的控制器是不完整定义的，并且预期存在错误和伪像。然而，通过采用基于模型的控制器，可以使用定义的那些参数来改善对缺乏模型的对应控制器的响应。
例如，据信脑波表示共振的一种形式，其中神经元群以协调的方式相互作用。波的频率与神经对神经递质的响应性、沿神经通路的距离、扩散限制等有关。也就是说，同一SS可以取决于其脑的大小的差异、存在的神经调制剂、其它解剖、形态和生理差异等由两个不同个体中的略微不同的频率略表示。这些差异可以在几微秒或更短的时间内测量到，从而导致频率的微小变化。因此，控制器的模型组成可以确定神经传输的参数和整体特征(相对于刺激)并且重新合成刺激信号以匹配受试者的脑波的正确频率和相位，其中适应性地确定对波形的优化。这可能不像加速或减慢信号回放那样简单，因为表示SS的神经相关性的各种脑波的不同元素在受试者之间可能具有不同相对差异。
当然，在一些情况下，对目标受试者(受者)的刺激的一个或多个组分可以表示为抽象或语义定义的信号，并且更具体地，对用于定义刺激的信号的处理将涉及在从第一受试者(供者)或多个供者接收到的源信号之间进行高水平调制或变换，以定义用于刺激第二受试者(受者)的目标信号。
优选地，每个分量表示反映脑活动的神经相关性子集，所述子集在空间和时间上或者在如小波等混合表示中具有高度自相关性。一旦信号的特性已知，这些就可以通过最优滤波(例如，光PCA)分开，并且要记住信号伴随有调制模式，并且这两个分量本身可以具有某种弱偶联和相互作用。
例如，如果第一受试者(供者)正在听音乐，则将存在与特定音乐同步的神经相关性的重要组分。另一方面，音乐本身可以不是对目标受试者(受者)的期望的刺激的一部分。进一步地，目标受试者(受者)可以处于不同声学环境中，并且其可以取决于受者的声学环境来修改残余信号，使得刺激适于达到期望的作用并且不表示幻象、注意力分散或不相关或不适当的内容。为了执行信号处理，方便的是存储信号或经部分处理的表示，但是可以实施完整的实时信号处理链。根据另一个实施例，标识了至少一个第一受试者(供者)的睡眠状态的特定阶段，并且捕获了脑活动的神经相关性，并且基于所捕获的神经相关性和所标识的SS使第二受试者(受者)经受刺激。SS典型地表示为有限分类空间内的语义变量。SS标识不需要通过对神经相关性信号的分析并且可以是由第一受试者，例如，基于其它身体信号或由观察者进行的自愿自我标识，或者是由第三方使用例如观察、fMRI或心理评估进行的手动分类。所标识的SS是有用的，例如，因为其表示可以朝(或在一些情况下，针对)其操控第二受试者(受者)的目标。
刺激可以是向第二受试者(受训者或受者)施加的一种或多种刺激，其可以是电或磁经颅刺激(tDCS、HD-tDCS、tACS、振荡tDCS或TMS)、感觉刺激(例如，视觉、听觉或触觉)、机械刺激、超声刺激等，并且可以相对于波形、频率、相位、强度/幅度、持续时间进行控制，或通过反馈、由第二受试者的自我报告的作用、由第三方进行的手动分类、对第二受试者受者的脑活动、行为、生理参数等进行的自动分析进行控制。
典型地，过程的目的是如下改善受者的睡眠：通过向第二受试者(受者)移植至少一个第一受试者(供者)的期望的SS或一系列阶段，通过在第二受试者(受者)体内诱导至少一个第一受试者(供者)的SS(或一系列阶段)的对应于第一受试者的所述SS的神经相关性，通过使用包括在一定时间段内从第一受试者的SS的神经相关性得到的波形的刺激参数。
典型地，所述第一受试者和所述第二受试者在空间上彼此远离并且在时间上也可以远离。在一些情况下，所述第一受试者和所述第二受试者是在时间上错位的同一受试者(人类或动物)。在其它情况下，所述第一受试者和所述第二受试者在空间上彼此接近。这些不同实施例主要在将信号从至少一个第一受试者(供者)转移到第二受试者(受者)方面有所不同。然而，当所述第一受试者和所述第二受试者共享共同环境时，神经相关性以及尤其对来自第二受试者的神经相关性的实时反馈的信号处理可以涉及与第一受试者的神经相关性的交互算法。
根据另一个实施例，所述第一受试者和所述第二受试者各自经受刺激。在一个特别有趣的实施例中，所述第一受试者和所述第二受试者彼此实时通信，其中所述第一受试者接收基于所述第二受试者的刺激，并且所述第二受试者接收基于所述第一受试者的反馈。这可能导致神经相关性(例如神经元振荡或脑波)同步，以及因此两个受试者之间的SS的同步。神经相关性可以是导致可以，例如，EEG、qEEG或MEG信号的形式检测到的脑波的神经元振荡。传统上，发现这些信号具有主导频率，所述主导频率可以通过各种分析确定，例如谱分析、小波分析或主分量分析(PCA)。一个实施例提供了，至少一个第一受试者(供者)的脑波的调制模式是独立于脑波(但是典型地在同一类的脑波内)的主导频率而确定的，并且此调制施加在对应于所述第二受试者(受者)的主导频率的脑波上。也就是说，一旦所述第二受试者达到了与所述第一受试者相同的脑波模式(其可以通过除了电磁、机械或感觉刺激以外的手段达到)，则以引导所述第二受试者的SS的方式施加所述第一受试者的调制模式。
根据另一实施例，用刺激信号刺激所述第二受试者(受者)，所述刺激信号忠实地表示至少一个第一受试者(供者)的神经相关性的定义的分量的频率组成。定义的分量可以基于主分量分析、独立分量分析(ICI)、基于特征向量的多元分析、因子分析、规范相关分析(CCA)、非线性维数降低(NLDR)或相关技术来确定。
刺激可以例如通过使用TES装置执行，如tDCS装置、高清tDCS装置、振荡tDCS装置、脉冲-tDCS(“电睡眠”)装置、振荡tDCS、tACS装置、CES装置、TMS装置、rTMS装置、深度TMS装置、光源或声源，所述光源或声源被配置成分别在光信号或声音信号上调制主导频率。刺激可以是光信号、声音信号(声音)、电信号、磁场、嗅觉或触觉刺激。电流信号可以是脉冲信号或振荡信号。刺激可以通过颅电刺激(CES)、经颅电刺激(TES)、深度电刺激、经颅磁刺激(TMS)、深度磁刺激、光刺激、声音刺激、触觉刺激或嗅觉刺激来施加。听觉刺激可以是例如双耳节拍或等时音调。
本技术还提供了一种处理器，所述处理器被配置成处理来自第一受试者(供者)的SS的神经相关性，并且选择性地取决于来自第一受试者的神经相关性的波形模式来产生或定义第二受试者(受者)的刺激模式。所述处理器还可以执行PCA、空间PCA、独立分量分析(ICA)、特征值分解、基于特征向量的多元分析、因子分析、具有线性隐藏层的自动编码器神经网络、线性判别分析、网络分量分析、非线性维数降低(NLDR)或数据分析的另一种统计方法。
将信号呈现给第二设备，所述第二设备被配置成刺激所述第二受试者(受者)，所述信号可以是取决于非反馈控制算法的开环刺激或依赖于闭环反馈的算法。所述第二设备产生旨在于第二受试者(受者)体内诱导期望的SS的刺激，例如，表示与所述第一受试者(供者)中存在的SS相同的SS。
对神经相关性执行的典型过程是用于去除噪声的滤波。在一些实施例中，噪声滤波器可以例如以50Hz、60Hz、100Hz、120Hz和另外的泛音(例如，三次和更高谐波)提供。与第二受试者(受者)相关联的刺激器典型地将执行解码、解压缩、解密、逆变换、调制等。
可替代地，真实波或其哈希(hash)可以通过区块链来认证，并且由此可由不可变的记录来认证。在一些情况下，可能的是使用呈其加密形式无需解密的存储的加密的信号。
由于不同脑大小和其它解剖、形态和/或生理差异，与同一SS相关联的主导频率在不同受试者中可能不同。因此，对受者有力地施加供者的频率可能不是最优的，所述频率可以或不可以精确地对应于与同一SS相关联的受者的频率。因此，在一些实施例中，可以使用供者的频率以在受者体内开始诱导期望的SS的过程。有时，当受者闭合以达到期望的睡眠状态时，刺激停止或被神经反馈替代，从而允许受者的脑找到其与期望的SS相关联的自身最优频率。
在一个实施例中，可以根据源信号对来自第二受试者的反馈信号相应地进行编码，并且将两者之间的误差最小化。根据一个实施例，处理器可以执行与频带滤波不同的降噪。根据一个实施例，将神经相关性变换成稀疏矩阵，并且在变换域中，掩盖表示噪声的概率高的分量，而保留表示信号的高概率的分量。也就是说，在一些情况下，表示调制的重要的分量可能不是先验已知的。然而，取决于其在第二受试者(受者)中诱导期望的响应的作用，可以标识“重要”分量，并且可以对其余部分进行滤波或抑制。经变换的信号然后可以被逆变换并且用作刺激信号的基础。
根据另一实施例，提供了一种SS修改的方法，例如脑夹带，所述方法包括：确定多个第一受试者(供者)的SS；例如使用EEG和MEG之一获取所述多个第一受试者(供者)的脑波，以产生含有对应于不同SS的脑波的数据集。数据库可以利用对应用于所述多个第一受试者的SS、活动、环境或刺激模式的分类来编码，并且所述数据库可以包含跨例如大量SS、活动、环境或刺激模式的获取的脑波。在许多情况下，数据库记录将反映相应脑波的特征频率或主导频率。
所述数据库可以根据例如SS、活动、环境或刺激模式以及第二受试者(受者)的基于一个或多受试者(供者)的所述数据库记录定义的刺激模式来访问。
由此检索的一个或多个记录用于定义所述第二受试者(受者)的刺激模式。作为相对琐碎的实例，女性受者可以主要基于来自女性供者的记录而刺激。类似地，某个年龄的儿童受者可以主要基于类似年龄的儿童供者的记录来刺激。同样，可以对各种人口统计、个性和/或生理参数进行匹配，以确保对源受试者与目标受试者之间的高度对应性。在目标受试者体内，可以采用引导或遗传算法以从信号的各个分量中选择修改参数，所述信号基于来自目标受试者的反馈来最佳地达到期望的目标状态。
当然，更微妙的方法是处理整个数据库并且基于全局脑波刺激模型来刺激第二受试者，但是这是不需要的，并且模型的潜在基础可以证明不可靠或不准确。事实上，可能优选的是从仅单个第一受试者(供者)得到刺激波形，以保持信号的微调制方面，所述微调制方面如上所述尚未充分表征。然而，一个或多个供者的选择不需要是静态的并且可以经常改变。供者记录的选择可以基于记录的其它用户的群体统计，即记录具有还是不具有预期作用，从而对其响应模式与给定受者相关性最高的供者进行滤波等。供者记录的选择还可以基于来自受者的反馈模式。
刺激过程典型地试图针对受者的期望的SS，所述期望的睡眠阶段是自动地或半自动地确定的或手动输入的。在一个实施例中，使用记录来定义合成载体或一组载体的调制波形，并且所述过程可以包含频域多路复用的多子载体信号(不一定是正交的)。可以通过不同子通道和/或通过不同刺激器电极、电流刺激器、磁场发生器、机械刺激器，感觉刺激器等并行施加多个刺激。可以施加所述刺激以达到所述第二受试者(受者)与一个或多个第一受试者(供者)的脑夹带。如果所述多个供者是相互夹带的，则每一个将具有对应脑波模式，这取决于脑波夹带的基础。供者之间的此联系可能有助于确定相应供者与受者之间的相容性。例如，可以确定所夹带的脑波的特征模式，即使对于不同目标SS也是如此，并且可以将所述特征模式相关，以找到相对接近的匹配并且排除相对较差的匹配。
此技术还可以为社交网络、约会站点、就业、使命(例如，空间或军事)或职业测试或其它人际环境提供基础，其中人可以基于夹带特性彼此匹配。例如，与没有夹带的那些相比，彼此有效地夹带的人可以具有更好的相容性，以及因此更好的婚姻、工作或社会关系。夹带作用不需要限于SS，并且可以跨任何上下文产生。
如以上所讨论的，所述多个第一受试者(供者)可以使其相应脑波模式存储在单独数据库记录中。使用来自多个第一受试者(供者)的数据来训练神经网络，然后通过输入目标阶段和/或反馈信息来访问所述神经网络，并且所述神经网络输出刺激模式或用于控制一个或多个刺激器的参数。当多个第一受试者(供者)形成刺激模式的基础时，优选的是，然后使用刺激的从来自所述多个第一受试者(供者)的SS的脑波模式或其它神经相关性的特征得到并且包括所述特征的神经网络输出参数来控制刺激器，所述刺激器例如生成一个或多个其自身的载体波，所述一个或多个其自身的载体波然后基于所述神经网络的输出调制。经训练的神经网络不需要定期检索记录，并且因此可以以更时间持续的方式，而不是基于记录的控制的更细分的方案操作。
在任何反馈相关性方法中，可以通过神经网络处理SS的脑波模式或其它神经相关性，以产生引导或控制刺激的输出。刺激是例如光信号、声音信号、电信号、磁场、嗅觉信号、化学信号和振动刺激或机械刺激中的至少一种。所述过程可以采用SS和脑波模式的关系数据库，例如与相应SS相关联的频率/神经相关波形模式。关系数据库可以包括：第一表，所述第一表进一步包括脑波模式的多个数据记录；以及第二表，所述第二表包括多个SS，所述SS中的每个SS都与至少一个脑波模式相关联。将SS和与所述SS相关联的脑波模式相关的数据存储在关系数据库中并维护。通过接收对所选(现有或期望的)SS的查询来访问关系数据库，并且返回表示相关联的脑波模式的数据记录。然后可以使用从所述关系数据库检索到的所述脑波模式来调制刺激器，以试图选择性地取决于所期望的睡眠阶段来产生作用。
本技术的另外的方面提供了一种计算机设备，所述计算机设备用于创建和维护SS和与所述SS相关联的频率的关系数据库。所述计算机设备可以包括非易失性存储器，所述非易失性存储器用于存储SS和与所述SS相关联的脑活动的神经相关性的关系数据库，所述数据库包括：第一表，所述第一表包括与所述SS相关联的脑活动的神经相关性的多个数据记录；以及第二表，所述第二表包括多个SS，所述SS中的每个SS与所述第一表中的一个或多个记录相联系；处理器，所述处理器与所述非易失性存储器耦合，并且被配置成处理关系数据库查询，然后使用所述关系数据库查询以查找所述数据库；RAM，所述RAM与所述处理器和所述非易失性存储器耦合，以用于暂时保持数据库查询和从所述关系数据库检索的数据记录；以及IO接口，所述IO接口被配置成接收数据库查询并且递送从所述关系数据线检索的数据记录。可以使用结构化查询语言(SQL)或SQL(例如，noSQL)数据库的替代方案来存储和检索记录。由通用计算机维护和操作的以上描述的关系数据库通过使对具体SS和与其相关联的脑波的搜索更加有效，由此尤其降低对计算机功率的需求来改善通用计算机的操作。
本技术的另外的方面提供了一种脑夹带的方法，所述方法包括：确定至少一个第一受试者(供者)的SS，使用EEG和/或MEG的至少一个通道记录所述至少一个第一受试者(供者)的脑波；将记录的脑波存储在物理存储装置中，从所述存储装置检索所述脑波，通过经颅电和/或磁刺激向第二受试者(受者)施加包括从所述EEG和/或所述MEG的至少一个通道得到的脑波模式的刺激信号，由此达到所述第二受试者(受者)期望的所述SS。刺激可以具有与所述EEG或所述MEG相同的维度(数量的通道)或者不同数量的通道，典型地为减少的。例如，所述EEG或所述MEG可以包括64个、128个或256个通道，而所述经颅刺激器可以具有32个或更少的通道。用于经颅刺激的电极的放置可以与用于记录EEG或MEG以保留记录的信号的拓扑并且可能地将这些信号用于空间调制的电极的放置大约相同。
变换数据的优点之一是能够选择将原始数据中表示的所关注的信息与噪声或其它信息分开的变换。一些变换保留了空间和状态转变历史并且可以用于更全面的分析。变换的另一个优点是，其可以以可以应用相对简单的低阶线性函数或统计函数的形式呈现所关注的信息。在一些情况下，期望的是对数据执行逆变换。例如，如果原始数据包含噪声，如50或60Hz的干扰，则可以执行频率变换，然后对干扰和其高阶互调产物进行窄带滤波。可以执行逆变换以将数据返回到其时域表示以用于进一步处理。(在简单滤波的情况下，可以采用有限冲激响应(FIR)或无限冲激响应(IIR)滤波器)。在其它情况下，在经变换的域中继续进行分析。
变换可以是有效算法的一部分，所述有效算法通过使所关注的信息的表示消耗较少信息位(如果呈数字形式)和/或允许其使用较低带宽进行通信来压缩数据以进行存储或分析。典型地，压缩算法将不是无损的并且因此，压相对于截短的信息是不可逆的。
典型地，信号的一种或多种变换和滤波是根据定义的算法，使用传统计算机逻辑执行的。中间阶段可以存储和分析。然而，在一些情况下，可以使用神经网络或深度神经网络、卷积神经网络架构或甚至模拟信号处理。根据一组实施例，变换(如果有的话)和分析是在并行处理环境中实现的。如使用SIMD处理器，如GPU(或GPGPU)。在此类系统中实施的算法的特征在于避免了数据相关性分支指令，其中许多线程并行执行相同指令。
分析EEG信号以确定电活动模式所发射的位置(例如，体素或脑区域)，并且对波模式进行表征。EEG信号的空间处理典型地将在内容分析之前，因为噪声和伪像可能对空间分辨率有用。进一步地，来自一个脑区域的信号在从另一脑区域的信号分析中典型地将是噪声或干扰；因此空间分析可以是理解分析的一部分。空间分析典型地呈几何和/或解剖学上约束的统计模型的形式，并行采用原始输入中的所有原始输入。例如，在输入数据是经皮脑电图信息的情况下，从32个EEG电极，在四维或更高维度的矩阵中对例如500sps、1ksps或2ksps采样的32环绕输入通道进行处理，以准许随着时间、空间和状态而映射位置并传达冲动。
矩阵处理可以在执行Matlab(马萨诸塞沃本的Mathworks公司(Mathworks,WoburnMA))软件平台的Windows 10操作系统下，在标准计算环境，例如i7-7920HQ、i7-8700K或i9-7980XE处理器中执行。可替代地，矩阵处理可以在计算机集群或网格或云计算环境中执行。所述处理还可以在分布式和松偶联环境或异步环境中采用并行处理。一个优选实施例采用单指令多数据处理器，如图形处理单元，如nVidia CUDA环境或AMD Firepro高性能计算环境。
可以实施人工智能(AI)和机器学习方法，如人工神经网络、深度神经网络等，以提取所关注的信号。神经网络充当优化的统计分类器并且可以具有任意复杂性。可以采用具有多个隐藏层的所谓的深度神经网络。所述处理通常取决于标记的训练数据，如EEG数据或EEG数据的各种经处理、经变换或经分类的表示。标签表示采集期间受试者的情感、情绪、上下文或状态。为了处理由EEG表示的连续数据流，可以实施递归神经网络架构。取决于神经网络之前的预处理，可以避免递归的正式实施。可以从EEG的传统时空处理中得出四维或更多维度的数据矩阵并且将其馈送到神经网络。由于时间参数在输入数据中表示，因此不需要神经网络临时存储器，但是此架构可能需要大量输入。也可以采用主分量分析(PCA，en.wikipedia.org/wiki/Principal_component_analysis)、空间PCA(arxiv.org/pdf/1501.03221v3.pdf,adegenet.r-forge.r-project.org/files/tutorial-spca.pdf,www.ncbi.nlm.nih.gov/pubmed/1510870)以及聚类分析(en.wikipedia.org/wiki/Cluster_analysis，参见US 9,336,302、9,607,023和引用的参考文献)。
通常，此类型的实施的神经网络在操作中将能够接收未标记的EEG数据并且在采集未分类的EEG期间产生表示受试者的预测的或估计的任务、表现、上下文或状态的输出信号。当然，可以使用统计分类器而不是神经网络。
通过常规处理、神经网络处理或两者进行的分析的EEG有两个目的。首先，其允许推断在哪些条件下脑的哪些区会受到哪些种类的电活动的影响。其次，其允许在受训者训练期间进行反馈(假定训练者与受训者之间具有适当的空间和解剖学相关性)，以帮助系统达到所期望的状态，或者在适当时，达到所期望的一系列状态和/或状态变换。根据本技术的一方面，所施加的刺激取决于测量的起始状态或状况(其可以表示参数的复杂上下文和历史依赖矩阵)，并且因此目标表示期望的复杂向量变化。因此，本技术的此方面试图了解与训练者的活动或任务相关联的复杂时间-空间-脑活动，并且寻求与受训者的相同活动或任务相关联的对应复杂时间-空间-脑活动，使得训练者的复杂时间-空间-脑活动状态与试图在受训者中达到的对应状态不同。这允许从性质不同的人转移训练范例，以在不同上下文下并且在某种程度上达到不同的结果。
从训练者获取数据的条件将包含任务数据和感觉刺激数据两者。即，本系统的优选应用是从训练者或技术个体获取EEG数据，然后将所述EEG数据用于将学习或更可能的学习准备状态转移给幼稚的受训者。受训者的目标是产生刺激参数集，所述刺激参数组将在受训者中达到在学习技能或任务时或之前导致训练者的EEG状态的相应神经活动或者达到任务的执行。
应当注意，EEG不是可以获取的仅神经或脑活动或状态数据，并且当然任何和所有此类数据都可以包含在本技术的范围内，并且因此EEG是可以使用的数据类型的仅代表性实例。其它类型包含fMRI、脑磁图、运动神经元活动、PET等。
尽管在训练者中不需要映射与任务不同的刺激响应模式，但这样做是有利的，因为训练者可用于延长的时间段，受训者的刺激可能影响神经活动模式，并且可能的是训练者将使刺激响应神经活动模式与一个或多个受训者相关。应当注意的是，前述已经表明训练者是单个个体，而实际上，训练者可以是训练者或技术个体的群体。因此，对脑活动数据的分析和处理对于每个相应个体以及对于整个群体而言都可以是适应性的。
例如，系统可以确定并非所有人类受试者都具有共同刺激响应脑活动相关性，并且因此群体需要分离和聚类。如果可以对差异进行归一化，则可以采用归一化矩阵或其它校正。另一方面，如果差异不允许可行的归一化，则可以对一个或多个群体进行细分，将不同训练者用于不同细分市场。例如，在一些任务中，男性脑的活动模式和能力与女性脑不同。这与性别之间的解剖学差异偶联表明系统可以提供性别特定的实施方案。类似地，年龄差异可以为群体的细分提供合理且科学的基础。然而，取决于所需的信息库和矩阵的大小以及一些其它因素，每个系统可以提供有整个群体所需的基本上所有参数，其中用户特定的实施方案基于用户概况或初始设置、校准和系统训练期。
根据本发明的一个方面，源受试者仪表装备有用于确定经历事件期间的局部脑活动的传感器。目的是标识处理此响应所涉及的脑区域。
传感器通常将试图确定神经元激发模式和脑区域激励模式，所述神经元激发模式和脑区域激励模式可以通过植入的电极、经皮脑电图、脑磁图、fMRI和其它技术来检测。在适当的情况下，优选的是经皮EEG，因为这是非侵入性的并且相对简单。
利用处于安静状态下的传感器对源进行观察，所述安静状态是他或她正在经历事件的状态以及源处于静息下或参与导致不同状态的不同活动的各种控制状态。可以在足够长的时间段内并且在重复的试验之上获得数据，以确定持续时间的作用。数据也可以是群体统计结果并且不需要从仅单个个体单次得到。
然后，使用4D(或更高)模型对传感器数据进行处理，以确定随时间推移与所关注的状态相关联的脑活动的特征位置相关性模式。在数据是从具有各种醒觉程度的群体得到的情况下，模型会维持此醒觉状态变量维度。
然后，受者准备接受精神状态。可以评估受者的精神状态。这可以包含对调查问卷的答复、销售评估或其它心理评估方法。进一步地，可以获得受者的经皮EEG(或其它脑活动数据)，以确定受者的起始状态以及经历期望的精神状态期间的活动。
另外，对受试者施加一组刺激，如视觉模式、声学模式、前庭、气味、味道、触摸(轻触摸、深触摸、本体感受、伸展、热、冷、疼痛、愉悦、电刺激、针灸等)、迷走神经(例如，副交感神经)，任选地在基线脑状态的范围内，以获取定义感觉刺激的个体和各个组合对受者的脑状态的作用的数据。群体数据也可以用于此方面。
然后，可以与受者或受者数据的群体结合对来自源或源群体的数据(见上文)进行处理，以提取定义随受者的时间推移的最优感觉刺激的信息，以达到期望的脑状态，从而产生期望的精神状态。
通常，对于源群体和受者而言，数据处理任务是艰巨的。然而，统计分析通常将具有准许对用于处理数据的数学变换进行并行化的形式，所述并行化可以使用各种并行处理器来有效地实施，所述并行处理器是作为典型图形处理器(GPU)中存在的SIMD(单指令多数据)处理器的常见形式。由于GPU的成本有效性，因此优选的是使用高效可并行化算法来实施分析，即使计算复杂度标定地大于CISC类型的处理器实施方案也是如此。
在对受者进行刺激期间，可以监测EEG模式以确定通过感觉刺激是否达到了期望的状态。可以实施闭环反馈控制系统来修改寻求达到目标的刺激。可以使用演进的遗传算法来开发用户模型，所述用户模型将精神状态、醒觉度和效价、感觉刺激和脑活动模式相关，以在受者的精神状态被进一步增强的情况下，优化当前刺激和学习期以及促进未来的期两者并且准许将系统用于一系列精神状态。
刺激可以包括化学信使或用于改变受试者的神志水平或另外改变脑化学或功能的刺激。化学品可以包括激素或内分泌类似物分子(如促肾上腺皮质激素[ACTH](4-11))、兴奋剂(如可卡因、咖啡因、尼古丁、苯乙胺)、精神活性药物、亲精神物质或致幻物质(改变脑功能，从而导致感知、情绪、神志和行为暂时变化，如愉悦(例如欣快感)或导致优势(例如，提高警觉性)的化学物质)。
尽管典型地避免受控或“非法”物质，但在一些情况下，这些物质可能适于使用。例如，各种药物可以改变脑的状态以增强或选择性地增强刺激作用。此类药物包含兴奋剂(例如，可卡因、哌甲酯(利他林(Ritalin))、麻黄碱、苯丙醇胺、安非他命)、麻醉剂/鸦片剂(鸦片、吗啡、海洛因、美沙酮、氧化吗啡、氧可酮、可待因、芬太尼)、致幻剂(麦角酸酰二乙氨(LSD))、PCP、MDMA(迷狂药)、墨斯卡灵(mescaline)、赛洛西宾(psilocybin)、迷幻蘑菇(古巴光盖伞)、毒蝇伞蘑菇、大麻/印度大麻)、迷幻鼠尾草、苯海拉明(苯那君(Benadryl))、盐酸环苯扎林、烟草、尼古丁，安非他酮(载班(Zyban))、阿片拮抗剂抑制剂、γ氨基丁酸(GABA)激动剂或拮抗剂、NMDA受者激动剂或拮抗剂抑制剂(例如，酒精、赞安诺(Xanax)、安定(Valium)、酣乐欣(Halcion)、利眠宁(Librium)、其它苯二氮卓类、劳拉西泮(Ativan)、氯硝安定(Klonopin)、阿米妥(Amytal)、宁比泰(Nembutal)、速可眠(Seconal)、苯巴比妥(Phenobarbital)、其它巴比妥类)、迷幻剂、解离剂和谵妄药(例如，特种类的乙酰胆碱抑制剂致幻剂)。例如，卡尔哈特·哈里斯(Carhart-Harris)使用fMRI示出，LSD和裸盖菇素通过使神经元同时激发，导致分别正常工作的脑的不同部分同步。此作用可以用于诱导脑的各个区域的同步，以提高精神状态。
注意到，大量的天然和人工物质可以改变情绪或醒觉度，并且因此可能会影响情感或非目标精神状态。通常，此类物质将穿过血脑屏障并且发挥精神作用。然而，通常这可能不是需要的或适当的。例如，痛苦的刺激可以改变情绪，而不会充当精神药物；另一方面，麻醉剂也可以通过钝化情感来改变情绪。进一步地，感觉刺激可以诱导情绪和/或情感变化，如气味、视力、声音、各种类型的触摸和本体感受感觉、平衡和前庭刺激等。因此，改变感觉感知或刺激的外周作用物质可能与情绪相关。同样，药学精神药物可能改变警觉性、知觉性、记忆和关注，这可能与特定任务的精神状态控制相关。
精神状态可以与学习或执行技能相关联。所述技能可以包括精神技能，例如认知、警觉、注意力、关注、专注、记忆化、可视化、放松、冥想、速读、创造技能、“全脑思维”、分析、推理、问题解决、批判性思维、直觉、领导力、学习、速读、耐心、平衡、感知、语言学或语言、语言理解、定量、“流体智力”、疼痛管理、维持积极态度的技能、外语、音乐、音乐创作、写作、诗歌创作、数学、科学、艺术、视觉艺术、修辞、情感控制、同理心、同情心、动机技能、人、计算、科学技能或发明家技能。参见美国专利和公开6,435,878、5,911,581和20090069707。所述技能可以包括：运动技能，例如，精细运动、肌肉协调、行走、奔跑、跳跃、游泳、舞蹈、体操、瑜伽；体育或运动(sport)、按摩技能、武术或搏击、射击、自卫；演讲、歌唱、玩乐器、笔法、书法、绘画、油画、视觉、听觉、嗅觉、玩游戏、博弈、雕刻家、工匠、按摩或装配技能。在要增强技能和要达到(或抑制)情感的情况下，并行地，对受者的刺激可以以达到结果的方式组合。在一些情况下，分量是通用的，而在其它情况下，则是主观的。因此，组合ny需要基于受者特性进行调适。
本技术可以体现在设备中，所述设备用于从源获取脑活动信息，处理脑活动信息以揭示目标脑活动状态和寻求在受者中达到所述状态的一组刺激，并且生成用于受者的刺激，以达到目标脑活动状态和潜在状态转变并且在一段时间内维持目标脑活动状态和潜在状态转变。所生成的刺激可以被反馈控制。可以使用通用计算机来处理信息，所述通用计算机为微处理器、FPGA、ASIC、片上系统或专用系统，其采用定制的配置来有效地达到所需的信息变换。通常，源和受者异步地起作用，其中源的脑活动被记录并且之后被处理。然而，实时处理和脑活动转移也是可能的。在通用可编程处理器实施方案或技术部分的情况下，计算机指令可以存储在非瞬态计算机可读介质上。典型地，所述系统将具有专用组件，如经颅刺激器或经修改的音频和/或显示系统，并且因此所述系统将不是通用系统。进一步地，甚至在通用系统中，操作本身也可根据本技术增强。
受试者的精神状态可以通过光、声音、经颅直流电刺激(tDCS)、经颅交流电刺激(tDAS)或HD-tACS、经颅磁刺激(TMS)或能够传送频率模式的其它方式非侵入性地诱导。
脑波的传输可以通过与植入脑中的电极直接电接触或远程地采用光、声音、电磁波和其它非侵入性技术来完成。光、声音或电磁场可以用于通过在受试者所暴露的光、声音或电磁场信号上调制编码的时间频率，向受试者远程地传送预记录的脑波的时间模式。
每种活动、精神或运动和情感都与具有具体空间和时间模式的独特脑波相关联，即，特征频率或频率随时间和空间的特征分布。此类波可以通过几种已知技术来读取和记录，所述已知技术包含脑电图(EEG)、脑磁图(MEG)、精确低分辨率脑电磁层析成像(eLORETA)、感觉诱发电位(SEP)、fMRI、功能近红外光谱(fNIRS)等。大脑皮层由在网络中互连的神经元构成。皮层神经元不断地发送和接收神经冲动-电活动，即使在睡眠期间也是如此。由EEG或MEG(或另一装置)装置测量的电活动或磁活动反映了大脑皮层中神经元的固有活动以及皮层下结构和感觉感受器发送给其的信息。
已经观察到，通过经颅直流电刺激(tDCS)、经颅交流电刺激(tACS)、高清经颅交流电刺激(HD-tDCS)、经颅磁刺激(TMS)或通过植入脑中的电极提供解码的时间模式向另一动物或人回放脑波允许受者达到即将到来的或用于增加达到的速度的精神状态。例如，如果(通过EEG或通过植入的电极)解码了对熟悉的迷宫进行导航的小鼠的脑波，则向不熟悉此迷宫的另一只小鼠播放此时间模式将允许其更快地学习对此迷宫进行导航。
类似地，记录与一个受试者的特定响应相关联的脑波并且之后向另一受试者“回放”此响应将诱导第二受试者的类似响应。更具体地，当对一种动物假设精神状态时，脑的一部分将具有特征活动模式。进一步地，通过“人工地”诱导另一只动物的相同模式，另一只动物将具有相同的精神状态，或更容易被诱导成所述状态。所关注的模式可能深驻留在脑中，并且因此通过皮层电位和模式淹没在EEG信号中。然而，也可以使用除了表面电极EEG之外的其它技术来确定和在空间上辨别深度脑活动，例如与边缘系统辨别开。例如，各种类型的磁传感器可以感测深度脑活动。参见例如9,618,591；9,261,573；8,618,799；以及8,593,141。
在一些情况下，可以采用皮层激励模式主导的EEG来感测精神状态，因为皮层模式可能与较低水平的脑活动相关。注意，不需要在每次使用系统时都执行对精神状态的状态表示的确定；相反，一旦确定了与特定精神状态相关联的脑空间和时间活动模式以及同步状态，就可以针对多个目标并且随着时间的推移使用这些模式。
类似地，虽然目标是例如触发目标以假定相同的脑活动模式为范例，但这可以以各种方式达到，并且诱导期望的模式的这些方法不需要是侵入性的。进一步地，可以使用用户反馈，尤其是在人类受让人的情况下，来调谐过程。最后，使用各种感觉，尤其是视力、声音、前庭、触摸、本体感受、味道、气味、迷走神经传入、其它颅神经传入等，可以用于触发高水平精神活动，所述高水平精神活动在特定受试者体内达到期望的精神状态、情感或情绪。
因此，在可以包含实验室规模和/或侵入性监测的实验受试者体内，确定了与特定情感或精神状态相对应的一组脑电活动模式。优选地，这些也与表面EEG发现相关。对于受让人，提供了一种无害且非侵入性的刺激系统。例如，可以仅使用视听刺激。提供了EEG电极组来测量脑活动，并且提供了适应性或遗传算法方案来优化视听呈现，寻求在受让人体内诱导在实验受试者体内发现的目标模式。在确定可以是路径相关性的刺激模式之后，可能的是这些模式将会持续，但是在更长的时间段内，可能对一种或多种刺激模式会存在某种脱敏。在一些情况下，视听刺激不充足，并且采用TMS或其它电磁刺激(超阈值或优选地阈下)来协助达到期望的状态并且将其维持期望的时间段。
脑波的传输可以通过与植入脑中的电极直接电接触或远程地采用光、声音、电磁波和其它非侵入性技术来完成。
光、声音或不可及电磁场可以用于通过在受试者所暴露的光、声音或电磁场信号上调制编码的时间频率，向受试者远程地传送预记录的脑波的时间模式。
采用光、声音或电磁场来通过在受试者所暴露的光、声音或电磁场信号上调制编码时间频率，向受试者远程地传送脑波的时间模式(可能是预记录的)。
当一组神经元同时激发时，活动表现为脑波。不同的脑波频率与脑中的不同精神状态相联系。
通过根据时间模式提供选择性刺激，可以在目标个体(例如，人、动物)中诱导期望的精神状态，其中时间模式与当处于期望的精神状态下时的目标的EEG模式相关或者表示转变，所述转变表示朝达到期望的精神状态的过渡。可以通过刺激器的物理布置或刺激(或其结果)所传递的自然神经通路将时间模式靶向到脑内的离散空间区域。
EEG模式可以从另外一个或多个个体、不同时间的同一个体或期望的精神状态的体内动物模型得到。因此，所述方法可以在第二受试者体内复制第一受试者的精神状态。精神状态典型地不是神志状态或观念，而是潜意识(在技术意义上)状态，表示情感、就绪度、接受力或其它状态，通常独立于特定思维或观念。本质上，第一受试者的精神状态(处于期望的精神状态下的“训练者”或“供者”)是通过记录精神状态的神经相关性捕获的，例如通过脑活动模式，如EEG或MEG信号所表示的。然后可以使用所述第一受试者的作为直接表示或记录的表示的神经相关性来控制对所述第二受试者(“受训者”或“受者”)的刺激，试图在所述第二受试者(受者/受训者)中诱导与所述第一受试者(供者/训练者)中存在的脑活动模式相同的脑活动模式，以协助所述第二受试者(受者/受训者)获得所述供者/受训者已获得的所述期望的精神状态。在替代性实施例中，采用来自处于第一精神状态的所述第一受试者(供者/训练者)的信号来防止所述第二受试者(受者/受训者)达到第二精神状态，其中所述第二精神状态是不期望的睡眠阶段。
可以通过多通道EEG或MEG从处于期望的脑状态的人那里获取源脑波模式。脑状态的计算模型很难创建。然而，根据本技术，此模型是不需要的。相反，可以通过统计过程(例如，PCA或相关技术)或经统计训练的过程(例如，神经网络)来处理信号。处理后的信号优选地保留了关于信号源特殊位置、频率和相位的信息。在刺激受者的脑时，可以修改源以解决脑大小差异、电极位置等问题。因此，保留的特性是归一化的空间特型、频率、相位和调制模式。
归一化可以基于来自目标受试者的反馈，例如基于目标受试者的当前状态与源受试者的对应状态的比较，或者目标与源之间的已知状态的其它比较。典型地，目标受试者体内的激励电极不对应于反馈电极或源受试者体内的电极。因此，需要另外类型的归一化，这也可以基于统计或经统计训练的算法。
根据一个实施例，将对所述第二受试者的刺激与反馈过程相关联，以验证所述第二受试者对刺激适当地做出响应，例如，与所述第一受试者对所述精神状态具有预定相似性、其精神状态与所述第一受试者具有预定差异，或者与基准精神状态相比具有期望的改变。有利地，刺激可以适应于反馈。在一些情况下，反馈可以是功能性的，即，不基于脑活动本身或精神状态的神经相关性，而是可以报告或观察到的身体、心理或行为作为。
反馈通常提供给刺激器的基于计算模型的控制器，所述基于计算机模型的控制器改变刺激参数以根据适用于目标的脑和脑状态模型优化刺激。
例如，据信脑波表示共振的一种形式，其中神经元群以协调的方式作为一组耦合的或相互作用的振荡器相互作用。波的频率与对神经递质的神经响应性、沿神经通路的距离、扩散限制等以及也许与起搏器神经元或神经通路相关。也就是说，相同的精神状态可以基于两个不同个体的脑的大小差异、存在的神经调制剂、生理差异等由其不同频率表示。这些差异可以在几微秒或更短的时间内测量发哦，从而导致频率的分数变化。然而，如果刺激与目标过程的自然或共振频率不同，则结果可能与预期的不同。因此，基于模型的控制器可以确定神经传输和群特性的参数(相对于刺激)，并且重新合成刺激波以匹配正确的波形，其中波形的优化被适应性地确定。这可能不像加速或减慢信号回放那样简单，因为表示精神状态的神经相关性的各种脑波的不同元素在受试者之间可能具有不同相对差异。因此，根据一组实施例，刺激器基于测量的对刺激的响应与刺激所寻求的期望的精神状态的对应性(误差)，对目标进行自动校准。在基于现有数据结果是混乱的或不可预测的情况下，以采用遗传算法来探索刺激参数的范围并且确定目标的响应。在一些情况下，基于在系统内维持的模型，目标对刺激具有异常或未预期的响应。在此情况下，当标识了与预期响应的偏差时，系统可以，如从可以在线的模型库中，如通过互联网寻求新模型。如果模型是可预测的，则可以在源或训练者的适用模型与目标的适用模型之间提供平移，以解决差异。在一些情况下，所期望的精神状态相对普遍，如睡眠和清醒。在此情况下，脑响应模型可以是统计模型，而不是神经网络或深度神经网络类型的实施方案。
因此，在一个实施例中，提供了一种混合方法，所述混合方法在一方面使用供者来源的脑波，所述供者来源的脑波可以从第一至少一个受试者(供者)的脑活动读数(例如，EEG或MEG)中提取，优选地通过主分量分析或空间主分量分析、自相关或其它统计处理技术(聚类、PCA等)或经统计训练的技术(误差的反向传播等)进行处理，所述供者来源的脑波将脑活动的分量分开，所述分量然后可以基于高水平参数(例如抽象)进行修改或调制。参见ml4a.github.io/ml4a/how_neural_networks_are_trained/。因此，刺激器可以被编程以诱导通过名称(例如，SS1、SS2等)定义或作为一系列“抽象”语义标签、图标或其它表示的一连串脑状态，每个脑状态对应于技术性的脑状态或子状态的顺序。所述顺序可以基于生物学和系统训练自动定义并且由此减轻程序员的底水平任务。然而，在一般情况下，本技术维持使用供者的脑活动读数的分量或子分量，例如EEG或MEG，并且不试图将其表征或抽象为语义水平。
根据本技术，可以采用神经网络系统或统计分类器来表征脑波活动和/或来自受试者的其它数据。除了分类或抽象外，还呈现了可靠性参数，所述可靠性参数预测输出的准确性。在精确度高的情况下，可以提供基于模型的刺激器以选择和/或参数化模型，并生成用于目标受试者的刺激。在精度低的地方，可以使用经滤波的信号表示来控制刺激器，绕过一个或多个模型。此混合方案的优点在于，当采用基于模型的刺激器时，可以独立于源受试者显式地控制许多不同参数。另一方面，在数据处理未能得到对正确的基于模型的刺激器参数的高度有用的预测的情况下，可以避免模型本身，从而有利于直接刺激类型的系统。
当然，在一些情况下，对目标受试者的刺激的一个或多个组成可以表示为抽象或语义定义的信号，并且更具体地，对用于定义刺激的信号的处理将涉及在从第一受试者接收到的源信号之间进行高水平调制或变换，以定义用于刺激第二受试者的目标信号。
优选地，每个分量表示反映脑活动的神经相关性的子集，所述子集在空间和时间上或在如小波等混合表示中具有高度空间自相关。例如，一个信号可以表示经调制的10.2Hz信号，而另一信号表示具有分别不同的空间源的叠加的经调制的15.7Hz信号。一旦信号的空间和时间特性已知，就可以通过最优滤波将其分开，并且记住，信号伴随有调制模式，并且两个分量本身可能具有某种弱偶联和相互作用。
在一些情况下，可以替代基频、调制、偶联、噪声，相位抖动或其它信号特性。例如，如果第一受试者正在听音乐，则将存在与特定音乐同步的神经相关性的重要分量。另一方面，音乐本身可能不是目标受试者的期望的刺激的一部分。因此，通过信号分析和分解，可以从所产生的信号中提取或抑制与音乐具有高时间相关性的来自第一受试者的信号的分量。进一步地，目标受试者可以处于不同声学环境中，并且其可以取决于目标受试者的声学环境来修改残余信号，使得刺激适于达到期望的作用并且不表示幻象、注意力分散或不相关或不适当的内容。为了执行处理，方便的是存储信号或经部分处理的表示，但是可以实施完整的实时信号处理链。此实时信号处理链通常特征在于，缓冲器的平均大小维持恒定，即，考虑到处理可能存在周期性，输出与输入之间的滞后相对恒定。
可以标识第一受试者的精神状态，并且捕获脑活动的神经相关性。基于所捕获的神经相关性和所识别的精神状态，对第二受试者进行刺激。在有限的分类空间内，精神状态可以表示为语义变量。精神状态标识不需要通过对神经相关性信号的分析，并且可以是第一受试者的自愿自我标识、第三方的手动分类或自动确定。例如所标识的精神状态是有用的，因为其表示可以朝(或针对)其操控第二受试者的目标。
刺激可以是对第二受试者的一个或多个输入，所述一个或多个输入可以是电或磁经颅刺激、传感器刺激、机械刺激、超声刺激等，并且可以相对于波形、强度/幅度、持续时间、反馈、由第二受试者的自我报告的作用、由第三方进行的手动分类、对第二受试者的脑活动、行为、生理参数等进行的自动分析进行控制。
所述过程可以用于在所述目标受试者体内诱导期望的精神状态的神经相关性，所述期望的精神状态从同一个人的不同时间或同时或不同时间的不同人得到。例如，试图通过使用包括在一段时间内从第一受试者的精神状态的神经相关性得到的波形的刺激参数来在第二受试者体内诱导第一受试者在期望的精神状态下的神经相关性。
所述第一受试者和所述第二受试者在空间上可以彼此远离并且在时间上也可以远离。在一些情况下，所述第一受试者和所述第二受试者是在时间上错位的同一受试者(例如，人类)。在其它情况下，所述第一受试者和所述第二受试者在空间上彼此接近。在一些情况下，期望的精神状态的神经相关性从具有更简单的脑的哺乳动物得到，所述神经相关性然后被外推到人脑。(动物脑刺激也是可能的，例如以增强训练和表现)。当所述第一受试者和所述第二受试者共享共同环境时，神经相关性以及尤其对来自第二受试者的神经相关性的实时反馈的信号处理可以涉及与第一受试者的神经相关性的交互算法。
所述第一受试者和所述第二受试者可以各自经受刺激器。所述第一受试者和所述第二受试者可以彼此实时通信，其中所述第一受试者接收基于所述第二受试者的刺激，并且所述第二受试者接收基于所述第一受试者的反馈。这可能导致两个受试者之间的精神状态的同步。然而，所述第一受试者不需要基于来自所述第二受试者的实时信号来接收刺激，因为所述刺激可以从第三受试者或不同时间点的所述第一受试者或所述第二受试者得到。
神经相关性可以是例如EEG、qEEG或MEG信号。传统上，发现这些信号具有可以通过各种分析确定的主导频率。一个实施例提供了，第一受试者的脑波的调制模式是独立于脑波(但是典型地在同一类的脑波内)的主导频率而确定的，并且此调制施加在对应于所述第二受试者的主导频率的波上。也就是说，一旦所述第二受试者达到了与所述第一受试者相同的脑波模式(其可以通过除了电磁、机械或传感器刺激以外的手段达到)，则以引导所述第二受试者的精神状态的方式施加所述第一受试者的调制模式。
可以用刺激信号刺激所述第二受试者，所述刺激信号忠实地表示所述第一受试者的神经相关性的定义的分量的频率组成。
所述刺激例如可以通过使用以下执行：tDCS装置、高清tDCS装置、tACS装置、TMS装置、深度TMS装置以及被配置成在光信号和声音信号之一上调制主导频率的光信号和声音信号的源。刺激可以是光信号、声音信号、电信号和磁场中的至少一种。所述电信号可以是直流电信号或交流电信号。刺激可以是经颅电刺激、经颅磁刺激、深度磁刺激、光刺激或声音刺激。视觉刺激可以是环境光或直射光。听觉刺激可以是双耳节拍或等时音调。
本技术还可以提供一种处理器，所述处理器被配置成处理来自第一受试者的精神状态的神经相关性，并且选择性地取决于来自第一受试者的神经相关性的波形模式来产生或定义第二受试者的刺激模式。典型地，所述处理器执行信号分析并且计算至少第一受试者的脑波的主导频率以及还有优选地第一受试者的脑内的空间和相位模式。
将信号呈现给第二设备，所述第二设备被配置成刺激所述第二受试者，所述刺激可以是取决于非反馈控制算法的开环刺激或取决于闭环反馈的算法。在其它情况下，部分或全部采用模拟处理，其中所述算法包括模拟信号处理链。所述第二设备从所述处理器(第一设备)接收信息，所述信息典型地包括以神经相关性表示的波形的一部分的表示。所述第二设备产生旨在在所述第二受试者体内诱导期望的精神状态的刺激，所述刺激例如，表示与存在于所述第一受试者体内的精神状态相同的精神状态。
对神经相关性执行的典型过程是用于去除噪声的滤波。例如，陷波滤波器可以以50Hz、60Hz、100Hz、120Hz和另外的泛音来提供。其它环境信号也可以以频率选择或波形选择(时间)的方式进行滤波。也可以采用如本领域中已知的更高水平的滤波。噪声滤波之后的神经相关性可以被编码、压缩(有损或无损)、加密或另外被处理或变换。与所述第二受试者相关联的刺激器典型地将执行解码、解压缩、解密、逆变换等。
可以采用类似于音频信号所采用的技术的信息安全和复制保护技术来保护神经相关性信号在使用前不受复制或内容分析的影响。在一些情况下，可能的是使用呈其加密形式无需解密的存储的加密的信号。例如，利用支持距离确定的非对称加密方案。参见U.S.7,269,277；Sahai和Waters(2005)《国际密码技术理论与应用年会(AnnualInternational Conference on the Theory and Applications of CryptographicTechniques)》,第457-473页,柏林海德堡施普林格出版社(Springer,Berlin,Heidelberg)；Bringer等人,(2009)《IEEE国际通信会议(IEEE International Conferenceon Communications)》,第1-6页；Juels和Sudan(2006)《涉及、密码和加密(Designs,Codesand Cryptography)》,2:237-257；Thaker等人,(2006)《IEEE国际工作负载表征会议(IEEEInternational Conference on Workload Characterization)》,第142-149页；Galil等人,(1987)《密码技术理论与应用会议(Conference on the Theory and Application ofCryptographic Techniques)》,第135-155页。
因为所述系统可能会闯入性地起作用，所以可能期望的是在使用之前对刺激器或刺激器所采用的参数进行认证。例如，刺激器和其采用的参数可以通过分布式账本，例如，区块链认证。另一方面，在封闭系统中，可以采用数字签名和其它分层认证方案。对某些过程的权限可以根据智能合约来定义执行，所述智能合约的自动权限(即，加密授权)是从区块链或分布式账本系统提供的。当然，也可以采用集中式管理。
实际上，可以根据源信号对来自第二受试者的反馈信号相应地进行编码，并且将两者之间的误差最小化。在此算法中，在可用反馈可用之前，将试图进行认证的信号典型地带入加密的信号的误差容错内。实现此的一种方式是提供预定范围的可接受的可认证信号，然后对所述可接受的可认证信号进行编码，使得认证在推定信号匹配任何预定范围时发生。在神经相关性的情况下，可以提供将不同信号表示为哈希模式的大组数字哈希模式。净结果是相对弱化的加密，但是加密强度仍可以高到足以减轻风险。
处理器可以执行与频带滤波不同的降噪。可以将神经相关性变换成稀疏矩阵，并且在变换域中，掩盖表示高概率噪声的分量，而保留表示高概率信号的分量。区别可以是优化的或适应性的。在一些情况下，表示调制的重要的分量可能不是先验已知的。然而，取决于其在第二受试者体内诱导期望的响应的作用，可以标识“重要”分量，并且可以对其余部分进行滤波或抑制。经变换的信号然后可以被逆变换并且用作刺激信号的基础。
可以提供精神状态修改，例如脑夹带，所述精神状态修改确定多个第一受试者的精神状态；例如使用EEG和MEG之一来获取所述多个第一受试者的脑波，以创建含有所述多个第一受试者的表示脑波的数据集。数据库可以利用对应用于所述多个第一受试者的精神状态、活动、环境或刺激模式的分类来编码，并且所述数据库可以包含跨例如大量精神状态、活动、环境或刺激模式的获取的脑。在许多情况下，数据库记录将反映相应脑波的特征频率或主导频率。如以上所讨论的，训练者或第一受试者是刺激参数的方便的源，但不是唯一可用源。所述数据库可以根据其索引例如精神状态、活动、环境或刺激模式以及第二受试者的基于一个或多受试者的所述数据库记录定义的刺激模式来访问。
由此检索的一个或多个记录用于定义第二受试者的刺激模式。记录的选择和其使用可以取决于第二受试者和/或来自第二受试者的反馈。作为相对琐碎的实例，可以主要取决于来自女性第一受试者的记录来刺激女性第二受试者。当然，更微妙的方法是处理整个数据库并且基于全局脑波刺激模型来刺激第二受试者，但是这是不需要的，并且模型的潜在基础可以证明不可靠或不准确。事实上，可能优选的是从仅单个第一受试者得到刺激波形，以保持信号的微调制方面，所述微调制方面如上所述尚未充分表征。然而，一个或多个第一受试者的选择不需要是静态的并且可以经常改变。第一受试者记录的选择可以基于记录的其它用户的群体统计(即，协作滤波，即，与谁的响应模式具有最高相关性等)。第一受试者记录的选择还可以基于来自第二用户的反馈模式。
刺激的过程可以试图针对第二受试者体内的期望的精神状态，所述期望的精神状态自动地或半自动地确定或手动地输入。然后，所述目标表示针对数据库以选择一个或多个期望的记录的查询的一部分。记录的选择可以是动态过程，并且记录的重新选择可以是反馈相关性的。
可以使用所述记录来定义合成的载体或一组载体的调制波形，并且所述过程可以包含频域多路复用的多子载体信号(其不一定是正交的)。可以通过遭受的子通道和/或通过不同刺激器电极、磁场发生器、机械刺激器、感觉刺激器等并行施加多个刺激。不同子通道或模态的刺激不必从相同的记录得到。
可以施加所述刺激来达到期望的精神状态，例如，第二受试者与一个或多个第一受试者的脑夹带。脑夹带不是此过程的仅可能结果。如果所述多个第一受试者相互夹带，则每个受试者将具有取决于脑波夹带的基础的对应脑波模式。第一受试者之间的此联系可能有助于确定相应第一受试者与第二受试者之间的相容性。例如，可以确定所夹带的脑波的特征模式，即使对于不同目标精神状态也是如此，并且将所述特征模式相关，以找到相对接近的匹配并且排除相对较差的匹配。
此技术还可以为社交网络、约会站点、就业或职业测试或其它人际环境提供基础，其中人可以基于夹带特性彼此匹配。例如，与没有夹带的那些相比，彼此有效地夹带的人可以具有更好的相容性。因此，并非寻求基于个性概况来匹配人，而是可以基于每一方有效地夹带另一方的脑波模式的能力来进行匹配。这增强了非语言沟通并且协助在活动期间达到对应状态。这可以通过监测每个个体对视频的神经响应以及通过基于另一方的精神状态的脑波相关性提供测试刺激，以了解是否有效地达到偶联来评估。另一方面，所述技术可以用于在自然偶联效率低下时协助夹带，或在不期望偶联时阻断偶联。后者的实例是敌对；当两个人在敌对环境中夹带时，确保了情感升级。然而，如果夹带衰减，则可以阻抗不期望的升级。
如以上所讨论的，所述多个第一受试者可以使其相应脑波模式与单独数据库记录关联存储。然而，其也可以组合成更全局的模型。一个此类模型是神经网络或深度神经网络。典型地，此网络将具有递归特征。使用来自多个第一受试者的数据来训练神经网络，然后通过输入目标状态和/或反馈信息来访问所述神经网络，并且所述神经网络输出刺激模式或用于控制刺激器的参数。当多个第一受试者形成刺激模式的基础时，优选的是，然后使用刺激的从来自所述多个第一受试者的精神状态的脑波模式或其它神经相关性的特征得到并且包括所述特征的神经网络输出参数来控制刺激器，所述刺激器例如生成一个或多个其自身的载体波，所述一个或多个其自身的载体波然后基于所述神经网络的输出调制。神经网络不需要定期检索记录，并且因此可以以更时间持续的方式，而不是基于记录的控制的更细分的方案操作。
在任何反馈相关性方法中，可以通过神经网络处理精神状态的脑波模式或其它神经相关性，以产生引导或控制刺激的输出。刺激是例如光(视觉)信号、声音信号、电信号、磁场、振动或机械刺激或其它感觉输入中的至少一种。这些场可以是静态的或动态变化的。
所述过程可以采用精神状态和脑波模式的关系数据库，例如，与相应精神状态相关联的频率/神经相关性波形模式。关系数据库可以包括：第一表，所述第一表进一步包括脑波模式的多个数据记录；以及第二表，所述第二表包括多种精神状态，所述精神状态中的每种精神状态都与至少一个脑波模式相关联。将精神状态和与所述精神状态相关联的脑波模式相关的数据存储在关系数据库中并维护。通过接收对所选精神状态的查询来访问关系数据库，并且返回表示相关联的脑波模式的数据记录。然后可以使用从所述关系数据库检索到的所述脑波模式来调制刺激器，以试图选择性地取决于争议中的精神状态来产生作用。
可以提供一种计算机设备，所述计算机设备用于创建和维持精神状态和与所述精神状态相关联的频率的关系数据库，所述计算机设备包括：非易失性存储器，所述非易失性存储器用于存储精神状态和与所述精神状态相关联的脑活动的神经相关性的关系数据库，所述数据库包括：第一表，所述第一表进一步包括与所述精神状态相关联的脑活动的神经相关性的多个数据记录；以及第二表，所述第二表包括多种精神状态，所述精神状态中的每种精神状态都与所述第一表中的一个或多个记录相联系；处理器，所述处理器与所述非易失性存储器耦合，所述处理器被配置成处理关系数据库查询，然后使用所述关系数据库查询以查找所述数据库；RAM，所述RAM与所述处理器和所述非易失性存储器耦合，以用于暂时保持数据库查询和从所述关系数据库检索的数据记录；以及I/O接口，所述I/O接口被配置成接收数据库查询并且递送从所述关系数据库检索到的数据记录。也可以使用SQL或noSQL数据库来存储和检索记录。
本技术的另外的方面提供了一种脑夹带的方法，所述方法包括：确定第一受试者的精神状态；使用EEG和MEG中的至少单通道的一个记录多个受试者的脑波；将记录的脑波存储在物理存储器装置中；从所述存储器装置检索所述脑波；通过经颅刺激向第二受试者施加包括从所述EEG和所述MEG中的至少单通道的一个得到的脑波模式的刺激信号，由此达到所述第二受试者期望的所述精神状态。所述刺激可以具有与所述EEG或所述MEG相同的阶(通道数量)或者不同数量的通道，典型地为减少的。例如，所述EEG或所述MEG可以包括128个或256个通道，而所述经颅刺激器可以具有8个或更少的通道。各种模态和模式的感觉刺激可以伴随经颅刺激。
所述至少一个通道可以少于六个通道，并且用于经颅刺激的电极的放置可以与用于记录所述EEG和MEG之一的电极的放置大约相同。
本技术可以响应于时间生物学，并且具体地响应于主观的时间感。对于受试者，这可以在主观上自愿地确定，但是也可以自动地确定，例如通过使用例如眼睛移动来判断关注跨度，并且在离散刺激之后分析脑波模式或其它生理参数的持久性。进一步地，还可以分析通过延迟和相位所反映的脑的时间常数。进一步地，可以使用自愿动作之前的视情况而定的消极变化(CNV)，以确定(或测量)有意识的动作定时以及还有更一般地思维与动作之间的时间关系两者。
典型地，脑波活动是利用大量EEG电极测量的，每个EEG电极都通过多个敏感的磁场检测器从头皮上的小区或在MEG的情况下接收信号，所述EEG电极对局部场差异都具有响应性。典型地，脑波捕获是在相对高数量的空间维度上执行的，例如，对应于传感器的数量。鉴于脑波是由数十亿个通过轴突连接的具有长距离的神经元产生的，因此通常对脑波信号进行处理以创建源模型是不可行的。进一步地，所述神经元通常是非线性的并且是相互连接的。然而，不需要源模型。
可以利用各种类型的人工智能技术来分析在所述第一受试者(供者)(或多个供者)和所述第二受试者(受者)两者的脑活动数据中表示的SS的神经相关性。尽管在一些情况下，算法或实施方案不需要是相同的，但是使源处理和反馈处理的方法一致使得反馈不会达到或寻求次优目标SS是有用的。然而，鉴于条件、资源、设备和目的可能存在差异，因此没有必要对这些过程进行协调。人工智能可以采取神经网络或深度神经网络的形式，但是可以使用基于规则/专家的系统、混合和更经典的统计分析。在典型情况下，人工智能过程将具有在其对输入信号的输出响应中非线性并且因此至少违反了线性叠加的原理的至少一个方面。此类系统倾向于准许进行辨别，因为决定和做出决定的过程最终是非线性的。人工智能系统需要进行训练的经验或信息的基础。这可以是有监督的(应用于数据的外部标签)、无监督的(对类的自我辨别)或半监督的(数据的一部分被外部标记)。
可以使用自学习或遗传算法来调谐系统，包含供者系统和受者系统处的信号处理中的两个或任一个。在遗传算法反馈相关性自学习系统中，受试者(例如目标)对各个种类的刺激的响应性可以在刺激空间上确定。此刺激可以在使用的上下文中，与特定目标SS一起提供或可以不受限制。刺激器可以使用刺激模式库来操作，或试图生成合成模式或模式的修改。在一段时间内，系统将学习将期望的SS映射成刺激模式的最优上下文相关性参数。
本技术可以用于在受者体内创建期望的SS，在受者体内消除现有SS两者。在后一种情况下，决定要达到哪种最终状态受到较少约束，并且因此优化不同。例如，在前一种情况下，可能难以达到期望的特定SS，需要一组转变来使受者的脑启动/准备进入目标状态。在系统试图消除不期望的SS的情况下，问题主要是要采取哪个路径来最有效地离开当前状态，要考虑各种成本，如刺激的舒适/不舒适、时间值成本等。因此，即使端点相同，一系列状态在实施这些不同目标中也有所不同，即，用于从状态A达到状态B的最优算法可以与存在于状态A并且终止于状态B的最优算法不同。
本技术可以用于解决其与做梦相关联的SS或其区段。典型地，做梦与许多不同脑区域相关联。如此，做梦的生物学是不同的。通常，梦具有生物化学或荷尔蒙组分以及也许可能会从认知状态衰减或认知状态中不存在的生理组分。做梦长期被视为很大程度上发生在快速眼睛移动(REM)睡眠期间，但据报道，梦还发生在非REM睡眠期间。然而，如果做梦人使人们在睡眠的REM相位期间清醒，则梦通常会被记住。另外，已经示出，例如关于面部的做梦与面部识别所涉及的脑特定区域中的高频率活动的增加相联系，而涉及空间感知、移动和思考的梦类似地与清醒时处理此类任务的脑的区域相联系。因此，尽管从供者进行的一般脑波或其它神经相关性的获取可能相似或相同，但用于第二受试者(受者)的刺激可能在模态、空间位置、强度/波形、其它刺激参数和类型以及所采用的反馈应用的类型方面有所不同。
已知的是，在REM期间具有更多REM睡眠和更强烈的θ(4Hz-7 Hz)活动的人能够更好地巩固情感记忆。表明(Blagrove)了，如果试图通过人工地增加θ波来破坏梦，则可能导致将更清醒的经历纳入梦中。(参见“梦用作过夜疗法(Dreams act as overnighttherapy)”,《新科学家》杂志,2018年5月5日)。从生动的做梦的人移植θ频率脑波也可能帮助达到相同的作用。此外，与利用合成θ频率(例如，等张音调或环境声音节拍)刺激受试者的脑相反，使用承载除了主导θ频率之外的二次谐波(及更高)谐波的供者的脑波来刺激受者的脑可以诱导相同类别的梦，即，如果供者梦到人，则受者将更可能梦到人，尽管是不同的人，因为供者的脑波会刺激受者的视觉皮层。这可能有助于治疗PTSD、压力管理、恐惧症和某些精神疾病。
在药物治疗实施方案中，在一些情况下，可能适合的是施用辅助达到目标SS并且用于情感状态和/或梦的药物或药理性药剂，如褪黑素、安眠或催眠药物、镇静剂(例如，巴比妥类、苯二氮卓类、非苯二氮卓类安眠药、食欲肽拮抗剂、抗组胺剂、全身麻醉剂、印度大麻和其它草药镇静剂、安眠酮和类似物、肌肉松弛剂、阿片类)，这可以包含某些精神药物，如肾上腺素、去甲肾上腺素再摄取抑制剂、血清素再摄取抑制剂、肽类内分泌激素，如催产素、ACTH片段、胰岛素等。将药物与刺激组合可以减少所期望的药物的剂量和相关联的药物副作用。
因此，目的是提供一种诱导第二受试者的睡眠的方法，所述方法包括：记录已入睡的第一受试者(供者)的脑活动模式；以及通过在受者体内复制供者的脑活动模式来诱导第二受试者(受者)的睡眠。
目的也是提供一种防止第二受试者(受者)的睡眠的方法，所述方法包括：记录清醒的第一受试者(供者)的脑活动模式；以及通过在受者体内复制供者的脑活动模式来防止第二受试者(受者)的睡眠。
另一目的是提供一种防止受试者的睡眠的方法，所述方法包括：记录已入睡的处于训练相位的所述受试者的脑活动模式；确定所述受试者在操作相位期间的脑活动模式；以及通过破坏所述受试者在所述操作相位期间的对应于已入睡的所述受试者的记录的脑活动的脑活动模式来防止所述受试者的睡眠。
另外的目的是提供一种诱导第二受试者(受者)睡眠的方法，所述方法包括：标识第一受试者(供者)的精神状态；如果所述供者已入睡，则记录所述供者的脑活动模式；以及通过在所述受者体内复制所述供者的所述脑活动模式来诱导所述受者的睡眠。所述方法可以进一步包括验证所述受者已入睡。
仍另外的目的是提供一种防止第二受试者(受者)的睡眠的方法，所述方法包括：标识第一受试者(供者)的精神状态；如果所述供者是清醒的，则记录所述第一受试者的脑活动模式；以及通过复制所述第二受试者的所述脑活动模式来防止所述第二受试者睡眠。所述方法可以进一步包括验证所述第二受试者是清醒的。
另一目的是一种将期望的精神状态从第一受试者(供者)移植到第二受试者(受者)的方法，所述方法包括：标识所述供者的精神状态；通过记录脑活动模式来捕获所述供者的精神状态；将所述脑活动模式保存在非易失性存储器中；从所述非易失性存储器检索所述脑活动模式；以及通过在所述受者体内诱导所述脑活动模式来向所述受者移植所述供者的所述期望的精神状态，其中所述期望的精神状态是睡眠状态和清醒状态之一。
另一目的是一种将期望的SS从第一受试者(供者)移植到第二受试者(受者)的方法，所述方法包括：标识所述供者的SS；通过记录脑活动模式来捕获所述供者的SS；将所述脑活动模式保存在非易失性存储器中；从所述非易失性存储器检索所述脑活动模式；以及通过在所述受者体内诱导所述脑活动模式来向所述受者移植所述供者的所述期望的SS，其中所述期望的SS是SS1、SS2和SS3之一。
另一目的是一种将期望的SS从第一受试者(供者)移植到第二受试者(受者)的方法，所述方法包括：标识所述供者的SS；通过记录脑活动模式来捕获所述供者的SS；将所述脑活动模式保存在非易失性存储器中；从所述非易失性存储器检索所述脑活动模式；以及通过在所述受者体内诱导所述脑活动模式来向所述受者移植所述供者的所述期望的SS，其中所述期望的SS是REM SS和非REM SS之一。
另一目的是一种将期望的SS从第一受试者(供者)移植到第二受试者(受者)的方法，所述方法包括：标识所述供者的SS；通过记录脑活动模式来捕获所述供者的SS；将所述脑活动模式保存在非易失性存储器中；从所述非易失性存储器检索所述脑活动模式；以及通过在所述受者体内诱导所述脑活动模式来向所述受者移植所述供者的所述期望的SS，其中所述期望的SS是慢波深度非REM睡眠。
另外的目的是一种通过向受者移植供者的精神状态移来改善所述受者的睡眠的方法，所述方法包括：记录所述供者的脑波；以及通过在所述受者体诱导所述供者的记录的脑波来向所述受者移植所述供者的精神状态，其中所述精神状态是清醒状态和睡眠状态之一。
仍另外的目的是一种向第二受试者移植第一受试者(供者)的期望的精神状态的方法，所述方法包括：标识所述供者的精神状态；记录所述供者在期望的精神状态下的脑波；以及通过在所述第二受试者体内诱导所述第一受试者的所述脑波来向所述受者移植所述供者的所述期望的精神状态，其中所述期望的精神状态是睡眠状态和清醒状态之一。
另一目标是一种通过向受者移植健康睡眠的期望的状态来改善所述受者的睡眠的方法，所述方法包括：标识多个健康供者的精神状态；记录所述多个健康供者在睡眠状态下的脑波；将所述脑波保存在非易失性存储器中；从所述非易失性存储器检索所述脑波；以及通过在所述受者体内诱导所述供者的所述脑波来向所述受者移植来自所述多个健康供者的健康睡眠的状态。所述方法可以包括标识所述受者的精神状态以验证所述受者具有所述期望的精神状态。所述脑波可以使用EEG、qEEG或MEG记录。所述方法可以进一步包括从噪声中对记录的脑波进行滤波和/或执行PCA以确定主导频率和次级(以及可能地更高的)谐波。
另外的目的是一种用于将期望的精神状态从第一受试者(供者)移植到第二受试者(受者)的系统，所述系统包括：第一设备，所述第一设备用于记录所述供者在期望的精神状态下的脑波；非易失性存储器，所述非易失性存储器与所述第一设备耦合，以用于存储所述脑波的记录；以及第二设备，所述第二设备用于在所述受者体内诱导所述脑波以向所述受者移植所述供者的所述期望的精神状态，所述第二设备被配置成从所述非易失性存储器接收所述供者的所述脑波的所述记录，其中所述期望的精神状态是睡眠状态和清醒状态之一。所述第一设备可以是脑电图仪和脑磁图仪之一。所述第二设备可以是tDCS装置、tACS装置、HD tDCS装置、TMS装置、深度TMS装置、振荡tDCS、被配置成在光信号或声音信号上调制供者的脑波频率的光信号或声音信号的源。
另一目的是一种向第二受试者(受者)移植第一受试者(供者)的期望的精神状态的方法，所述方法包括：标识所述供者的精神状态；记录所述供者的EEG和MEG中的至少一个，所述供者处于期望的精神状态；处理所述EEG或MEG信号；将处理后的信号保存在非易失性存储器中；从所述非易失性存储器检索所述处理后的信号；在至少一个刺激上调制所述处理后的信号；以及通过利用所述至少一个刺激刺激所述第二受试者来向所述第二受试者移植所述第一受试者的所述期望的精神状态，其中所述期望的精神状态是睡眠状态或清醒状态。所述处理可以包括从所述EEG或MEG信号中去除噪声；和/或压缩所述EEG或MEG信号。从所述非易失性存储器检索到的所述EEG或MEG信号可以被解压缩。所述刺激可以是光信号、声音信号、电信号、磁场或其组合。所述电信号可以是直流电信号或交流电信号。所述经颅电刺激可以是tDCS、高清tDCS或tACS。所述经颅磁刺激可以是深度磁刺激。所述光刺激可以是环境光或直射光。所述声音刺激可以是双耳位或等时音调。
仍另一目的是一种用于向第二受试者(受者)移植第一受试者(供者)的期望的精神状态的系统，所述系统包括：脑电图仪或脑磁图仪，所述脑电图仪或所述脑磁图仪用于记录所述供者的脑波，所述供者处于期望的精神状态；处理器，所述处理器与脑电图仪或脑磁图仪耦合，所述处理器被配置成执行信号分析并且计算所述供者的所述脑波的至少一个主导频率；非易失性存储器，所述非易失性存储器与所述第一处理器耦合，用于存储所述供者的所述脑波的所述至少一个频率；第二设备，所述第二设备用于在所述受者体内诱导所述脑波以向所述受者移植所述供者的所述期望的精神状态，所述第二设备被配置成从所述非易失性存储器接收所述供者的所述脑波的所述至少一个主导频率，其中所述期望的精神状态是睡眠状态和清醒状态之一。
所述第二设备可以是tDCS装置、高清tDCS装置、tACS装置、TMS装置、深度TMS装置、振荡tDCS、能够在光上调制所述至少一个主导频率的光源、能够在声音上调制所述至少一个主导频率的声源或其组合。所述声源可以是双耳节拍源或等时音调源。
另外的目的是一种向第二受试者(受者)移植第一受试者(供者)的昼夜节律的方法，所述方法包括：记录所述供者的EEG或MEG，所述供者具有所述昼夜节律的期望相位；对记录的EEG或MEG进行处理以去除噪声；将处理后的EEG或MEG保存在非易失性存储器中；从所述非易失性存储器检索所述处理后的EEG或MEG；以及借助于通过经颅刺激或在其上对所述供者的EEG或MEG进行调制的其它一个或多个刺激向所述受者“回放”所述供者的所述处理后的EEG或MEG来向所述受者移植所述供者的所述昼夜节律的期望的相位。所述方法可以进一步包括在将所述记录的EEG或MEG保存在所述非易失性存储器中之前，对其进行压缩；以及在从所述非易失性存储器检索经压缩的EEG或MEG之后，对所述记录的EEG或MEG进行解压缩。所述经颅刺激可以是tDCS、HD-tDCS、TMS、深度TMS和振荡tDCS。
又另一目的是一种用于向第二受试者(受者)移植第一受试者(供者)的昼夜节律的系统，所述系统包括：脑电图仪或脑磁图仪，所述脑电图仪或脑磁图仪用于分别记录EEG或MEG；第一处理器，所述第一处理器耦合到所述脑电图仪或所述脑磁图仪并且被配置成用于数字信号处理，以用于从所述记录的EEG或MEG中去除噪声；非易失性存储器，所述非易失性存储器与所述处理器耦合，以用于存储处理后的EEG或MEG；以及刺激装置，所述刺激装置耦合到所述非易失性存储器，以用于向所述受者回放所述处理后的EEG或MEG，以向所述受者诱导所述供者的所述昼夜节律。所述刺激装置可以是经颅刺激装置、光源或声音源，所述光源或声音源各自能够分别在光信号或声音信号上调制记录的EEG或MEG。所述经颅刺激装置可以是tDCS、HD-tDCS、TMS、深度TMS和振荡tDCS之一。所述第一处理器可以被进一步配置成压缩所述处理后的EEG或MEG。被配置为对压缩的EEG或MEG进行解压缩的第二处理器可以耦合到所述非易失性存储器以及所述经颅刺激装置或另一刺激装置。
另一目的是提供一种用于控制具有可编程处理器的脑刺激器的计算机可读介质，所述计算机可读介质包括：用于分析来自受试者的脑活动数据以确定所述脑活动数据中表示的睡眠-清醒状态的指令；用于相对于所述睡眠-清醒状态对所述脑活动数据分类的指令；用于基于睡眠-清醒状态的至少一个周期模型确定所述脑活动数据中表示的所述睡眠-清醒状态的期望的变化的指令；用于控制所述脑刺激器的脑刺激模式，以在基本上不用通过刺激使所述受试者直接苏醒的情况下达到所述睡眠-清醒状态的所述期望的变化的指令。所述脑刺激器可以包括听觉刺激器和视觉刺激器中的至少一种，所述听觉刺激器或所述视觉刺激器向所述受试者呈现基本上没有语义、音乐或对象内容的信号。所述脑刺激模式可以被调整成使脑波模式与调制的波形同步。睡眠-清醒状态的所述期望的变化可以是脑半球特定的。所述计算机可读介质可以进一步包括用于对所述脑活动数据对所述脑刺激模式的反应建模，并且调整所述脑刺激模式以最优地达到所述睡眠-清醒状态的所述期望的变化的指令。所述计算机可读介质可以进一步包括用于相对于群体常值归一化所述脑活动数据，并且取决于所述群体常值来访问刺激模式数据库的指令。所述计算机可读介质可以进一步包括用于取决于所述受试者的所述脑活动数据与所述群体常值之间的差异对从所述刺激模式数据库存取的刺激模式进行去归一化的指令。所述计算机可读介质可以进一步包括用于将具有随机分量的噪声模式引入到所述脑刺激模式中的指令。
另外的目的是提供一种诱导受试者的对应于预定顺序的精神状态的方法，所述方法包括：确定精神状态的所述预定顺序和所述受试者的当前精神状态；基于精神状态的所述预定顺序和所述受试者的精神状态的过去历史对来自数据库的至少一个记录进行处理，以生成用于达到所述受试者的目标精神状态的最优脑刺激模式；以及选择性地取决于所述最优脑刺激模式，利用直接脑刺激器和间接感觉输入脑刺激器中的至少一个刺激所述受试者。
本技术可以用于修改或改变受试者的精神状态(例如，从睡眠到清醒或反之亦然)。典型地，如通过EEG、MEG、观察、刺激响应幅度和/或延迟等来评估起始精神状态、脑状态或脑波模式。在不受控的环境中，特别关注的是自动化精神状态评估，所述自动化精神状态评估不依赖于人类观察或EEG信号，而是可以通过MEG(例如SQID、光学泵送的磁力计)、EMG、MMG(肌磁图)、机械(例如，加速度计、陀螺仪等)、来自生理传感器(例如AKG、心脏速率、呼吸速率、温度、电脱脂电位等)的数据或自动化相机获取。
例如，皮层刺激响应通路和反射可以被自动行使，以在大致连续的基础上确定其特性。这些特性可以包含，例如，刺激与观察到的中央响应(例如，EEG)或外周响应(例如，EMG、肢体加速度计、视频)之间的延迟。典型地，将使用相同的模态来评估刺激前状态、刺激响应和刺激后状态，但是这不是限制性的。
为了改变所述精神状态，以被设计成以期望的方式改变所述精神状态的方式施加刺激。可以采用状态转变表或算法来优化从起始精神状态到期望的精神状态的转变。可以基于观察到的所监测变量的变化，以开环(预定刺激方案)或闭环(反馈适应性刺激方案)的形式提供所述刺激。
有利地，刺激的施加与响应的确定之间的特征延迟随脑或精神状态而变化。例如，一些精神状态可能导致延迟增加或延迟变化更大，而其它一些可能导致变化减少或更小。进一步地，一些状态可能导致响应衰减，而其它可能导致响应增大。另外，不同精神状态可能与定量地不同的响应相关联。典型地，仅对脑或精神状态进行的评估本身不能改变状态，但是在一些情况下，评估和转变影响可以组合。例如，在寻求复制达到深度睡眠状态时，禁忌了干扰睡眠的激励。
在通过EEG(可能限于相对受控的环境)确定脑波模式本身的情况下，表示所述模式的脑波表示对定义相位的神经元群的相干发射。改变状态的一种方式是提前或阻碍对神经元激励的触发，所述神经元激励可以是直接或间的激发或抑制，可以是例如通过电、磁、机械或感觉刺激引起的。此刺激可以与检测到的(例如，通过EEG)脑波时间同步，例如相对于检测到的模式具有相位超前或滞后。进一步地，所述激励可以通过持续地推进到期望状态来操纵脑波信号，通过持续相位旋转，所述期望的状态表示不同的频率。在达到期望的新状态之后，刺激可以停止或者以相位锁定的方式维持，以保持所述期望的状态。
可以使用预测模型来确定当前精神状态，当受试者已达到期望的精神状态时向所述期望的精神状态的最佳转变以及如何维持所述期望的精神状态。所述期望的精神状态本身可以表示动态顺序(例如，
Figure BDA0003065971300000891
等)，使得在限定的条件下将所述受试者的精神状态保持期望的时间段。因此，可以相对于测量的脑波模式使刺激时间同步。
不一定需要直接测量或确定脑波或其相位关系。而是，所述系统可以确定震颤或反射模式。典型地，所关注的反射模式涉及中央通路以及更优选地脑反射通路，而不是较少取决于瞬时脑状态的脊髓介导的反射。中央反射模式可以反映刺激与运动响应之间的时间延迟、运动响应的幅度、响应通过各种传入通路的分布、响应的可变性、震颤或对运动活动的其它调制等。可以采用这些特性的组合，并且可以在不同时间处或为了反映不同状态而采用不同子集。与诱发的电位类似，刺激可以是任何感觉，尤其是视觉、声音、触摸/本体感受/疼痛等，但是也可以行使其它感觉，如味道、气味、平衡等。直接电或磁激励也是可能的。如所讨论的，响应可以通过EEG、MEG或外周传入通路来确定。
另外的目的是提供一种用于增强深度非REM睡眠的系统和方法，所述方法包括将慢波睡眠分量与获取的脑波模式在统计学上分开；基于在统计学上分开慢波睡眠分量定义刺激模式；以及利用定义的刺激模式刺激受试者。所述神经刺激器包括：存储器，所述存储器被配置成存储获取的脑波模式；至少一个处理器，所述至少一个处理器被配置成：将慢波非REM睡眠分量与所述获取的脑波模式在统计上分开；并且基于在统计学上分开慢波非REM深度睡眠分量定义脑刺激模式；以及输出信号发生器，所述输出信号发生器被配置成定义脑刺激模式。
仍另外的目标提供了一种用于增强深度睡眠的系统和方法，所述方法包括：从至少一个受试者的本地脑活动EEG记录提取表示包括慢波睡眠的深度睡眠状态的脑波模式；使用统计处理算法对所提取脑波模式进行处理，以将慢波睡眠分量与所述至少一个受试者的所述本地脑活动EEG记录分开；反转处理后的所提取脑波模式；以及利用反转的处理后的所提取脑波模式刺激受试者。用于增强深度睡眠的对应系统包括：存储器，所述存储器被配置成存来自至少一个受试者的本地脑活动EEG记录的表示包括慢波睡眠的深度睡眠状态的脑波模式；至少一个处理器，所述至少一个处理器被配置成使用统计处理算法对所提取脑波模式进行处理，以将慢波睡眠分量与所述至少一个受试者的所述本地脑活动EEG记录分开；以及刺激器，所述刺激器被配置成基于处理后的所提取脑波模式来生成刺激信号。所述刺激器可以包括经颅交流电刺激器。为了格式化用于刺激脑的信号，可以将其反转。
另一目的是提供一种在第二受试者体内诱导期望的精神醒觉状态的方法，所述方法包括：确定具有相应精神醒觉状态的第一受试者的脑活动模式；以及通过用所述第一受试者的已确定脑活动模式刺激所述第二受试者来在所述第二受试者体内诱导对应精神醒觉状态。所述期望的精神醒觉状态可以是例如睡眠或清醒。所述确定可以包括确定脑磁图活动和脑电图活动中的至少一个。所述用所述已确定脑活动刺激所述第二受试者可以包括根据所述第一受试者的频率相关性脑波模式对所述第二受试者进行的视觉刺激和听觉刺激中的至少一个。所述期望的精神醒觉状态可以包括包含至少一个睡眠周期的一系列精神状态。所述刺激可以选择性地响应于所述第二受试者在刺激之前或期间的已确定精神状态。视所述第二受试者的预测精神状态而定，可以向所述第二受试者提供所述刺激。
另外的目的是提供一种在第二受试者体内复制第一受试者的期望的精神状态的方法，所述方法包括：标识所述第一受试者的精神状态；通过记录脑活动模式来捕获所述第一受试者的精神状态；将所述脑活动模式保存在非易失性存储器中；从所述非易失性存储器检索所述脑活动模式；以及通过在所述第二受试者体内诱导所述脑活动模式来在所述第二受试者体内复制所述第一受试者的所述期望的精神状态。所述期望的精神状态可以是睡眠状态和清醒状态之一。所述第一受试者的所述精神状态可以通过自动化脑活动分类来标识，并且所述脑活动模式以脑磁图活动和脑电图活动之一的形式记录。所述脑活动模式可以以压缩波形组的形式记录在所述非易失性存储器中，所述压缩波形组保留多个信号获取通道的频率和相位关系特征。所述通过在所述第二受试者体内诱导所述脑活动模式来在所述第二受试者体内复制所述第一受试者的所述期望的精神状态可以包括选择性地取决于所述第二受试者在视觉刺激和听觉刺激中的所述至少一个之前或与所述至少一个同时发生的已确定脑活动模式，对所述第二受试者的视觉刺激和听觉刺激中的至少一个。
仍另外的目的是提供一种用于在第二受试者体内复制第一受试者的期望的精神状态的系统，所述系统包括：非易失性数字数据存储介质，所述非易失性数字数据存储介质被配置成存储表示所述第一受试者的脑波的多个通道的频率和相位模式的数据；刺激器，所述刺激器被配置成在所述第二受试者体内诱导脑波模式，当所述第一受试者的所述脑波被获取时，所述脑波模式模仿所述第一受试者的精神状态；传感器，所述传感器被配置成确定所述第二受试者的与所述刺激器的刺激并行的脑波模式；以及控件，所述控件被配置成：读取所述非易失性存储器，并且选择性地取决于存储的数据和所述第二受试者的已确定脑波模式来控制所述刺激器。所述精神状态可以是其范围包括睡眠和清醒的精神醒觉状态。所述存储的数据可以源自脑磁图传感器和脑电图传感器中的至少一个。所述刺激器可以被配置成根据所述第一受试者的所述脑波的频率相关性脑波模式提供对所述第二受试者的视觉刺激和听觉刺激中的至少一个。所述传感器可以被配置成确定所述第二受试者在刺激期间的精神状态。所述控件可以被配置成控制所述刺激器以在所述第二受试者体内诱导包括至少一个睡眠周期的一系列精神状态。视所述第二受试者的预测精神状态而定，可以向所述第二受试者提供所述刺激。脑活动信息的归一化可以是空间的和/或时间的。脑活动信息的归一化可以是空间的和/或时间的。
附图说明
参照附图描述了详细描述。在附图中，附图标记的最左边的数字标识所述附图标记首次出现的附图。在不同附图中使用相同的附图标记表示相似或相同的项。
图1示出了根据本发明的一个实施例的流程图，所述流程图展示了将睡眠状态从一个受试者复制到另一个受试者的过程。
图2示出了根据本发明的一个实施例的流程图，所述流程图展示了根据本发明的一个实施例的通过记录和复制与清醒阶段相关联的脑波来将清醒阶段从一个受试者复制到另一个受试者的过程。
图3示出了根据本发明的一个实施例的流程图，所述流程图展示了根据本发明的一个实施例的如下的过程：通过记录至少一个第一受试者的脑电图(EEG)从所述至少一个第一受试者向另一个受试者复制SS，从EEG提取至少一个主导频率，并且通过利用其主导频率与期望的SS相关联的刺激刺激第二受试者来在第二受试者体内复制所述至少一个第一受试者的SS。
图4示出了根据本发明的一个实施例的流程图，所述流程图展示了借助于记录健康供者的EEG或MEG并且通过经颅刺激将其“回放”给受者来改善受者的睡眠的方法。
图5示出了根据本发明的一个实施例的流程图，所述流程图展示了创建SS和其相关联的频率的数据库，以用于稍后进行脑夹带。
图6示出了根据本发明的一个实施例的流程图，所述流程图展示了使用神经网络创建SS和其相关联的频率的数据库，以用于稍后进行脑夹带。
图7示出了根据本发明的一个实施例的流程图，所述流程图展示了如下方法：记录第一受试者在受试者的昼夜节律的期望状态下的精神状态，并且将此精神状态移植到另一受试者体内以复制昼夜节律的期望状态。
图8示出了根据本发明的另外的实施例的流程图。
图9示出了根据本发明的一个实施例的流程图，所述流程图展示了将期望的SS从一个受试者复制到另一个受试者的过程。
图10示出了根据本发明的一个实施例的流程图，所述流程图展示了将来自期望的SS的具有同步相位的主导脑波从一个受试者转移到另一个受试者的过程。
具体实施方式
在下文中，将参考附图详细描述本公开的实施例，使得本领域技术人员可以容易地实施本公开。然而，应当注意，本公开不限于实施例，而是可以以各种其它方式来体现。
图1示出了根据本发明的第一实施例的流程图。询问、观察或感测具有精神状态的第一受试者(供者)，以确定或标识其精神状态100。第一受试者典型地是人类，但是这不是对本技术的限制，并且受试者可以是动物。在此实施例中，过程试图标识特征睡眠模式，并且因此对第一受试者的精神状态进行监测，直到睡眠状态发生为止110。如果第一受试者(供者)已入睡，捕获反映或表征睡眠状态的脑活动模式120。此步骤可以通过记录第一受试者(供者)的EEG或MEG来完成，并且脑活动模式存储在非易失性存储器130中。这些存储的模式可以任选地进行处理、在统计学上聚合、针对扰动或异常进行分析、滤波、压缩等。可以确定睡眠阶段。应当注意，脑活动模式在睡眠期间从阶段到阶段随时间而变化，并且因此存储的模式可以涵盖一个或多个睡眠阶段。
然后，使用来自第一受试者(供者)的存储的数据以通过在第二受试者(受者)体内复制第一受试者(供者)的脑活动模式(或一系列脑活动模式)来诱导第二受试者(受者-典型地也是人类，但是可以是动物)的睡眠150。取决于存储的模式，脑活动模式的复制典型地试图通过以与处于睡眠状态的第一受试者(供者)的脑活动模式中表示的频率、相位和/或波形模式同步的方式调制刺激(或几种刺激)来刺激或诱导第二受试者(受者)的脑。典型地，当第二受试者(受者)达到睡眠状态160(假定第一受试者和第二受试者在生理上是相容的-供者和受者都应是人类或动物两者或任一种)时，第一受试者和第二受试者的脑活动模式将是对应的。
根据本技术，刺激的调制，所述刺激为例如经颅直流电刺激(tDCS)，其波形被调制成与第一受试者(供者)的与刺激电极相关联的脑区域的原始或处理后的脑波模式相对应。
例如，通过EEG电极测量第一受试者(供者)的脑活动模式。在睡眠状态下，可以假定范围在<1Hz到约25Hz的各种波模式，所述各种波模式在幅度、频率、空间位置和相对相位方面有所不同。例如，睡眠的第一阶段最初地由频率为8Hz到13Hz的α脑波主导。典型地，来自第一受试者(供者)的脑活动模式测量的空间分辨率比第二受试者(受者)的刺激器更高，例如为64或128个电极EEG，并且刺激电极趋于大于EEG电极。因此，使用维数(或空间)降低算法对第二受试者(受者)的刺激进行处理，以解决这些差异，这将趋于对刺激信号进行滤波。例如，tDCS刺激典型地使用最少两个电极以及最多32个电极，这需要维数降低。tDCS刺激将趋于对接近电极的皮层细胞的静息膜电位进行去极化或超极化，并且治疗可能会调制离子通道或细胞激励性。tDCS典型地以避免直接刺激皮层神经元的动作电位的强度施加。因此，通过施加利用第一受试者(供者)的脑活动调制的刺激，使得第二受试者(受者)易于与第一受试者(供者)的脑活动模式同步。例如，通过临时调制电极附近的细胞的极化水平，细胞将更好地偶联到具有第一受试者(供者)的脑活动模式的特性的第二受试者(受者)的脑中的激励刺激。
应当注意，可以使用不同于tDCS的刺激，如脉冲电磁场(PEMF)、tACS、视觉刺激、听觉刺激、惯性模拟等。在任何情况下，目的是将第二受试者的脑活动模式与第一受试者的睡眠模式脑活动模式偶联，以促进第二受试者的睡眠。
本领域的技术人员应当理解，任何数量的经颅电刺激(TES)或经颅磁刺激(TMS)。例如，TES可以是经颅直流电刺激(tDCS)、高清经颅直流电刺激(HD-tDCS)、经颅振荡直流电刺激(振荡tDCS)、经颅直流电脉冲刺激(“电睡眠”)、经颅交替刺激(tACS)以及其它不太受欢迎类型的TES。在极端情况下(如帕金森氏症和癫痫症患者)，可以向植入脑中的电极施加电流刺激。还可以使用经颅磁刺激(TMS)。
除了TES或TMS之外，还可以在可进行频率调制的光、声音、振动或任何数量的其它刺激上对供者的本地脑波进行调制。例如，可以在环境光、在双耳节拍或等时音调上对供者的脑波进行调制。
可以任选性地通过视觉观察、通过EEG、EKG、测量心脏速率和/或呼吸频率、身体温度或本领的域技术人员将容易地理解的任何数量的其它生理参数来完成对受者已达到期望的睡眠状态的验证。这些测量优选地应通过生物传感器自动完成。
图2示出了根据本发明的第二实施例的流程图。询问、观察或感测具有精神状态的第一受试者(供者)，以确定或标识其精神状态100。第一受试者典型地是人类，但是这不是对本发明的限制(其同等地适用于任何动物)。在此实施例中，询问试图标识特征警觉/清醒模式，并且因此对第一受试者的精神状态进行监测，直到警觉状态发生为止111。如果第一受试者(供者)是清醒的，捕获反映或表征清醒状态的脑活动模式120并且将其存储在非易失性存储器中130。例如，可以试图捕获表示清醒的模式，并且因此监测开始于正在睡觉的受试者。这些存储的模式可以任选地进行处理、在统计学上聚合、针对扰动或异常进行分析、滤波、压缩等。可以确定清醒阶段。应当注意，脑活动模式在清醒期间随时间而变化，并且因此存储的模式可以涵盖清醒过程的一个或多个阶段。
然后从非易失性存储器140检索来自第一受试者(供者)的存储的数据，并且使用所述存储的数据以在第二受试者(受者-典型地但不一定还是人类)体内通过复制第一受试者(供者)的清醒脑活动模式或第二受试者(受者)体内的一系列脑活动模式来“移植”警觉状态以防止睡眠或维持警觉170。取决于存储的模式，脑活动模式的复制典型地试图通过以与处于醒着或清醒状态的第一受试者(供者)的脑活动模式中表示的频率以及优选地相位和/或波形模式同步的方式在刺激上调制供者的本地脑波来刺激或诱导第二受试者(受者)的脑。典型地，当第二受试者清醒或苏醒时180，第一受试者和第二受试者的脑活动模式将是对应的。
根据第三实施例，对本技术进行了概括，如图3的流程图中示出的。询问、观察或感测具有精神状态的第一受试者(供者)，以确定或标识其精神状态190。对第一受试者的精神状态进行监测，直到达到期望的状态为止200。当第一受试者达到所述状态时，通过例如记录第一受试者的EEG或MEG来捕获210反映或表征状态的脑活动模式，并且任选地将其存储在非易失性存储器中。脑活动模式是例如脑波(例如，EEG)210。
使用统计数据挖掘技术，如主分量分析(PCA))对脑波进行分析，以确定一组线性不相关的变量-主分量。对记录的脑波中的至少一个主导频率进行标识220。任选地，也可以对二次谐波和更高的谐波进行标识。本领域的技术人员将很好地理解，可以使用任何数量的类似统计数据分析技术，如信号处理、独立分量分析、网络分量分析、对应性分析、多重对应性分析、因子分析、规范相关、功能主分量分析、独立分量分析、奇异谱分析、加权PCA、稀疏PCA、主测地线分析、基于特征向量的多元分析等。
然后检索来自第一受试者的存储的数据，在至少一个刺激上对至少主导频率进行调制并且使用所述至少主导频率以通过试图复制第一受试者(供者)的脑活动模式或第二受试者(受者)240的一系列脑活动模式来在第二受试者(受者)体内“移植”供者的期望的精神状态。然后对第二受试者(受者)进行监测，以诱导期望的精神状态250。
根据图4的流程图中所反映的第四实施例，在处于睡眠状态时对第一受试者(健康供者)的EEG或EMG进行记录260，任选地进行处理以去除噪声270，并且存储280。可以任选地对数据进行压缩。检索290存储的数据并且如需要的进行解压缩。然后，使用经颅电刺激或磁刺激来改善睡眠质量，以向第二受试者(受者)回放所述数据300。
根据如图5的流程图所示的第五实施例，记录310第一受试者(供者)的多通道EEG/EMG，并且对其进行处理以去除噪声(和/或伪像)和/或压缩数据320。任选地将其存储在非易失性存储器中。对数据执行PCA分析，以确定与SS 330相关联的特征频率。创建数据库，存储记录的EEG/MEG、相关联的特征频率和对应SS，使得可以检索任何给定SS 340的特征频率。如本领域的技术人员将容易理解的，此数据库可以是关系数据库或任何其它类型的可搜索数据库。根据底第六实施例，记录310第一受试者(供者)的多通道EEG/EMG，并且对其进行处理以去除噪声(和/或伪像)和/或压缩数据320。任选地将其存储在非易失性存储器中。以此数据对人工神经网络进行训练，以确定与SS相关联的特征频率350。如本领域的技术人员将容易理解的，可以使用深度神经网络以及其它AI机器学习工具。创建数据库，存储EEG/MEG的记录、相关联的特征频率和对应SS，使得可以检索任何给定SS 340的特征频率。
图7示出了根据本发明的另外的实施例的流程图，所述流程图展示了如下过程：相对于第一受试者(供者)的昼夜节律对其进行监测，其中将其EEG或EMG记录360，进行处理以去除噪声(和/或伪像)，并且任选地进行压缩270，并且然后存储在非易失性存储器中280。在此情况下，利用昼夜周期相位对存储的信号加标签，除非捕获了仅一个相位，或者使用了模式识别来标识周期阶段。然后，使用经颅电或磁刺激或其它刺激来检索存储的数据290，对其进行解压缩370并回放给第二受试者(受者)380，以诱导期望的昼夜节律状态。在此情况下，本技术也可以用于延长第二受试者的状态，或加快从一个状态转变到另一个状态。其还可以用于通过增强具有另外异常周期的第二受试者的健康或正常昼夜节律模式来治疗昼夜节律病症。本领域的技术人员将很好地理解，除了TES或TMS之外，供者的昼夜节律可以在要用作刺激的光、声音或其它信号上进行调制以刺激受者，以在受者体内诱导期望的昼夜节律相位。
图8示出了根据本发明的另外的实施例的流程图，所述流程图展示了将期望的SS从一个受试者(供者)复制到另一个受试者(受者)的过程。通常，以传统方式确定源受试者的SS，这可以包含脑信号分析、其它生物测定和/或观察。可以在一个或多个睡眠周期内以及在不同类型的环境条件或刺激期间或之后获取400数据。例如，可以播放各种类型的音乐，以试图夹带有意识或潜意识的节律。灯光可以闪烁，并且各种其它感觉刺激可以发生。对脑信号读数进行同步并且用刺激参数加标签410，从而使刺激与其相应作用相关联。类似地，在睡眠之前，可以向受试者呈现某些经历，使得在睡眠期间脑内的记忆处理取决于这些经历。
在从受试者400获取各种数据连同关于睡眠前经历和/或上下文410的信息以及睡眠期间的感觉刺激之后，生成存储器、数据库、统计模型、基于规则的模型和/或对神经网络进行训练，从而反映受试者(供者)。可以从多个受试者(供者)聚合数据，但是典型地，在聚合之前针对特定受试者对这些数据进行处理。基于单个或多个受试者数据，可以使归一化过程发生420。归一化可以是空间的和/或时间的。例如，会话之间或针对不同受试者的EEG电极可能处于不同位置，从而导致多通道空间布置失真。进一步地，不同个体的头部大小和形状是不同的，并且这也需要进行归一化和/或编码。头部/头骨和/或脑的大小和形状也可能导致信号的时间差异，如特征时间延迟、共振或特征频率等。
解决这些影响的一种方法是通过使用时间空间变换，如小波类型的变换。应当注意，以对应方式，统计过程通过傅里叶变换进行频率分解分析，其也通过小波变换进行时间-频率分解。典型地，小波变换是离散小波变换(DWT)，但是可以采用更复杂且不太规则的变换。如以上所讨论的，可以使用主分量分析(PCA)和空间PCA来分析信号，从而推测分量的线性(线性叠加)和统计独立性。然而，这些推测在技术上不适用于脑波数据，并且实际上，通常期望脑波分量之间相互作用(非独立性)并且缺乏线性(因为“神经网络”本质上是非线性的)，因此会击败未修改的PCA或空间PCA的用途。然而，非线性维数降低的领域提供了各种技术，以允许在非线性和非独立性的推测下进行相应分析。参见en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction。
因此，统计方法可用于将EEG信号与其它信号分开并且用于分析EEG信号本身的分量。根据本发明，保留了在其它上下文中，例如根据现有技术可能被视为噪声的各种分量，如脑波的调制模式。同样，保留了重要脑波事件之间的相互作用和特征延迟。此信息既可以与其发生的脑波模式整合在一起，也可以作为然后可以与未经调制的脑波模式重新组合以近似原始受试者的单独调制模式而存储。
根据本技术，可以采用脑波的有损“感知”编码(即，相对于主观响应在功能上被优化)来处理、存储和传达脑波信息。在测试场景中，可以测试“感知”特征，使得将重要信息保留在与有效信号不强烈对应的信息上。因此，尽管可能不知道先验地哪些分量表示有用的信息，但是遗传算法可以经验地确定哪些特征或数据减少算法或参数集优化了有用信息的保留与信息效率。应当注意，受试者在其对信号分量的响应方面可能有所不同，并且因此“感知”编码相对于受者可以是主观的。另一方面，不同供者可能具有不同的信息模式，并且因此每个供者也可能需要单独处理。结果，成对的供者和受者可能需要优化以确保相关信息的准确和有效传达。根据本发明，寻求转移睡眠/清醒精神状态和其对应模式。在受者体内，这些模式具有特征脑波模式。因此，在各种替代处理方案下，可以使用供者来刺激受者,并且基于客观标准，如所产生的脑波模式或专家观察员报告或主观标准(如受者的自我报告、调查或反馈)来确定受者的睡眠/清醒响应。因此，在训练阶段之后，可以采用对供者的优化处理，所述优化处理可以包含滤波、主导频率的再合成、特征提取等，这是针对供者和受者两者进行优化。在其它情况下，可以充分归一化供者特性，使得需要补偿仅受者特性。在琐碎的情况下，存在仅一个范例供者，并且对信号过采样并无损记录，留下仅受者变化作为重要因素。
由于主导频率往往具有较低的信息含量(相比于这些频率的调制以及脑内的各个源的相互作用)，因此编码主要频率的一种有效方式是通过位置、频率、相位和幅度。波的调制也可以表示为一组参数。通过根据功能属性分解脑波，在刺激期间变得可能的是修改来自供者的一系列“事件”，使得受者不需要以相同的次序和相同的持续时间经历与供者相同的事件。而是，高水平控制可以基于供者脑波的分类的模式来选择状态、驻留时间以及状态之间的转变。可以使用统计过程来执行对供者的脑波以及受者的响应的提取和分析：如主分量分析(PCA)、独立分量分析(ICA)和相关技术；聚类、分类、维数降低和相关技术；神经网络和其它已知技术。这些算法可以在通用CPU、如GPU等阵列处理器和其它技术上实施。
实际上，可以通过考虑脑波信号的非线性和非独立性的PCA技术来分析第一受试者的脑波模式，以提取主要循环分量、其相应调制模式和其相应相互关系。主要循环分量可以通过波形合成器来重新合成，并且因此可以有效地进行编码。进一步地，波形合成器可以基于归一化和受者特征参数来修改频率或来自供者的分量的关系。例如，第二受试者(受者)的脑的特征分类脑波频率可以比供者低3％(或者每种类型的波都可以分别参数化)，并且因此重新合成可以考虑到此差异。然后可以对重新合成的模式施加调制模式和相关关系。调制模式和相互关系的归一化可能与潜在主要循环分量不同，并且也可以进行此校正，并且将归一化的调制模式和相互关系包含在重新合成中。如果时间修改不相等，则可以抽取或内插调制模式和相互关系以提供刺激器的正确连续时间顺序。刺激器可以包含一个或多个刺激通道，所述一个或多个刺激通道可以被实施为电、磁、听觉、视觉、触觉或其它刺激和/或组合。
刺激器优选地是反馈控制的。反馈可以与受者的脑波模式和/或上下文或辅助生物特征基础相关。例如，如果第二受试者(受者)开始从不同于第一受试者(供者)睡眠模式的睡眠中清醒，则刺激器可以基于此发现重新同步。也就是说，刺激器控制将进入与受者的实际状态相对应的模式，并且尝试使用可用范围和可用组的刺激参数来将受者从当前状态引导到期望的状态。还可以使用反馈来调谐刺激器，以基于先前和当前刺激来最小化来自受者受试者的预测的或期望的状态的误差。
刺激器的控制优选地是适应性的，并且可以采用遗传算法以随着时间推移来改善性能。例如，如果存在多个第一受试者(供者)，则可以将第二受试者(受者)与来自其脑波信号(或其算法修改版本)的受者体内的预测的响应最佳的那些供者相匹配，并且与来自其脑波信号的受者受试者体内的预测的响应对应性最差的那些供者区别开。类似地，如果供者的脑波模式在一定时间和上下文范围内确定并存储在数据库中，则可以优化从数据库中选择替代方案，以确保受者受试者对期望的响应的最佳对应性。
应当注意，如果适当地对应于受者并且准许期望的响应与实际响应之间由足够低的误差的来自供者的信号模式可用，则不需要基于再合成器的刺激器。例如，如果供者和受者是不同时间的同一受试者，则可能不需要大型数据库，并且刺激信号可以是同一受试者在较早时间的经最少处理的记录。同样，在一些情况下，偏差是可容忍的，并且可以利用相对慢的定期校正来发射范例信号。例如，睡眠信号可以源自单个受试者并且可以以90分钟或180分钟的周期性重播，如光或声音信号，所述光或声音信号可用于单独反馈不可用或无用的宿舍环境中。
在一些情况下，有用的是在供者身上提供刺激器和基于反馈的控制器。这将更好地匹配供者和受者的条件，并且进一步允许确定供者的脑波模式以及还有供者对反馈的响应性。供者与受者之间的一个区别在于，在供者体内，试图维持自然睡眠模式并且不打断自然睡眠模式。因此，适应性多受试者数据库可以包含来自所有受试者的数据记录，无论是否在开头选择的ab是有用的范例还是不是都如此。因此，问题在于是否可以在来自数据库记录的受者体内诱导可预测且有用的响应，并且如果如此，则可以采用所述记录。如果记录将产生不可预测的结果或不可用的结果，则应避免使用所述记录。响应的可预测性和有用性可以通过遗传算法或其它参数空间搜索技术来确定。
延伸睡眠信号照明实例，照明器(例如，红色LED灯泡)的强度可以基于供者的脑波模式来调制。照明器可以具有带有数十个或数百个不同的可用脑波模式的闪速存储器模块。照明器可以进一步包含如照相机或非成像光学或红外传感器等传感器以及与AmazonAlexa类似的语音控制。照明器还可以包含用于播放同步的声音或音乐的相关联的扬声器。当睡眠周期开始时，照明器开始以程序形式显示(并且播放和关联音频)脑波模式，以试图诱导预定睡眠模式。可以使用传感器以基于程序来确定受者是否处于预测的睡眠状态。如果受者的睡眠状态偏离程序，则可以将程序重置为与受者的实际状态相对应的部分，或者重置为试图将受者的睡眠状态引导回到期望的程序的引导状态。如果不能对目标受试者有效地进行同步或引导，则照明器可以采用不同源受试者脑波模式。在此情况下，不采用电刺激或电反馈，并且整个操作可以是非接触式的。
如图10所示，受试者的人脑状态或精神状态被修改或改变。在一些实施方案中，确定受试者的当前脑波模式、受试者的当前脑波模式的特征波的相位、取决于精神状态的刺激响应的特征定时或受试者的受监测神经或运动模式中的时间关系。确定或定义受试者的当前脑波模式的期望的变化。施加可以用于确定当前状态、改变状态或两者的刺激，例如电、磁、声学或超声、感觉等。例如，可以提取取决于精神状态的对刺激响应的特征定时，或者可以确定受试者的神经或运动模式的时间关系。刺激相对于相位状态可以是异步的或时间同步的，或者可以取决于至少已确定时间关系。在闭环激励中，监测受试者在至少一个刺激之后的脑波模式，或者测量或评估响应参数，例如特征定时。可以取决于观察到的或监测到的变化来控制刺激，所述观察到的或监测到的变化指示受试者的脑状态或精神状态的有效改变或修改。
在整个文档中，用于表示一个元件与另一个元件的连接或耦合的术语“连接到”或“耦合到”包含一个元件“直接连接或耦合”到另一个元件的情况以及一个元件通过仍另一个元件“电连接或耦合”到另一个元件的情况。进一步地，应当理解，文档中使用的术语“包括(comprises)或包含(includes)”和/或“包括(comprising)或包含(including)”是指在一个或多个其它组件、步骤、操作和/或元件的存在或添加不排除添加描述的组件、步骤、操作和/或元素，除非上下文另外指出。
在整个文档中，术语“单元”或“模块”包含通过硬件或软件实施的单元以及通过其两者实施的单元。一个单元可以通过两件或更多件硬件来实施，并且两个或更多个单元可以通过一件硬件来实施。
在检查以下附图和详细描述时，本发明的其它装置、设备、系统、方法、特征和优点对于本领域的技术人员将是或将变得显而易见。旨在将所有此类另外的系统、方法、特征和优点包含在此描述内、处于本发明的范围内并且由所附权利要求保护。
在本说明书中，讨论了几个优选的实施例。毫无疑问，本领域的技术人员将具有关于如何可以使用本文所述的系统和方法的其它观念。应当理解，此广泛发明不限于本文所讨论的实施例。而是，本发明只受以下权利要求的限制。
本发明的各方面旨在是可分离的，并且可以以实施例的组合、子组合以及各种排布来实施。因此，在不脱离本发明的精神和范围的情况下，本文的各种公开，包含由公认的现有技术表示的公开，可以根据本文的教导进行组合、子组合和排布。本文引用的所有参考文献和信息源均通过引用以其整体明确并入本文。

Claims (82)

1.一种在第二受试者体内诱导期望的精神醒觉状态的方法，所述方法包括：
a.确定具有相应精神醒觉状态的第一受试者的脑活动模式；以及
b.通过用所述第一受试者的已确定脑活动模式刺激所述第二受试者来在所述第二受试者体内诱导对应精神醒觉状态。
2.根据权利要求1所述的方法，其中所述期望的精神醒觉状态是睡眠。
3.根据权利要求1所述的方法，其中所述期望的精神醒觉状态是清醒。
4.根据权利要求1所述的方法，其中所述确定包括确定脑磁图活动和脑电图活动中的至少一个。
5.根据权利要求1所述的方法，其中所述用所述已确定脑活动刺激所述第二受试者包括根据所述第一受试者的频率相关脑波模式对所述第二受试者进行视觉刺激和听觉刺激中的至少一个。
6.根据权利要求1所述的方法，其中所述期望的精神醒觉状态包括包含至少一个睡眠周期的一系列精神状态。
7.根据权利要求1所述的方法，其中所述刺激选择性地响应于在刺激之前或期间所述第二受试者的已确定精神状态。
8.根据权利要求1所述的方法，其中视所述第二受试者的预测精神状态而定，向所述第二受试者提供所述刺激。
9.一种在第二受试者体内复制第一受试者的期望的精神状态的方法，所述方法包括：
a.标识所述第一受试者的精神状态；
b.通过记录脑活动模式来捕获所述第一受试者的精神状态；
c.将所述脑活动模式保存在非易失性存储器中；
d.从所述非易失性存储器检索所述脑活动模式；以及
e.通过在所述第二受试者体内诱导所述脑活动模式来在所述第二受试者体内复制所述第一受试者的所述期望的精神状态。
10.根据权利要求9所述的方法，其中所述期望的精神状态是睡眠状态和清醒状态之一。
11.根据权利要求9所述的方法，其中通过自动化脑活动分类来标识所述第一受试者的所述精神状态，并且以脑磁图活动和脑电图活动中的至少一个的形式记录所述脑活动模式。
12.根据权利要求9所述的方法，其中所述脑活动模式以一组压缩波形的形式记录在所述非易失性存储器中，所述一组压缩波形保留多个信号获取通道的频率和相位关系特征。
13.根据权利要求9所述的方法，其中所述通过在所述第二受试者体内诱导所述脑活动模式来在所述第二受试者体内复制所述第一受试者的所述期望的精神状态包括选择性地取决于所述第二受试者在视觉刺激和听觉刺激中的至少一个之前的或与其同时的已确定脑活动模式来对所述第二受试者进行视觉刺激和听觉刺激中的所述至少一个。
14.一种用于在第二受试者体内复制第一受试者的期望的精神状态的系统，所述系统包括：
a.非易失性数字数据存储介质，所述非易失性数字数据存储介质被配置成存储表示所述第一受试者的脑波的多个通道的频率和相位模式的数据；
b.刺激器，所述刺激器被配置成在所述第二受试者体内诱导脑波模式，当所述第一受试者的所述脑波被获取时，所述脑波模式模仿所述第一受试者的精神状态；
c.传感器，所述传感器被配置成与所述刺激器的刺激并行地确定所述第二受试者的脑波模式；以及
d.控件，所述控件被配置成读取所述非易失性存储器并且选择性地取决于存储的数据和所述第二受试者的已确定脑波模式来控制所述刺激器。
15.根据权利要求14所述的系统，其中所述精神状态是范围包括睡眠和清醒的精神醒觉状态。
16.根据权利要求14所述的系统，其中所述存储的数据源自脑磁图传感器和脑电图传感器中的至少一个。
17.根据权利要求14所述的系统，其中所述刺激器被配置成根据所述第一受试者的所述脑波的频率相关脑波模式提供对所述第二受试者的视觉刺激和听觉刺激中的至少一个。
18.根据权利要求14所述的系统，其中所述传感器被配置成确定在刺激期间所述第二受试者的精神状态。
19.根据权利要求14所述的系统，其中所述控件被配置成控制所述刺激器以在所述第二受试者体内诱导包括至少一个睡眠周期的一系列精神状态。
20.根据权利要求14所述的系统，其中视所述第二受试者的预测精神状态而定，向所述第二受试者提供所述刺激。
21.一种诱导第二受试者的睡眠的方法，所述方法包括：
a.记录已入睡的第一受试者的脑活动模式；以及
b.通过在所述第二受试者体内复制所述第一受试者的所述脑活动模式来诱导所述第二受试者的睡眠。
22.一种防止第二受试者的睡眠的方法，所述方法包括：
a.记录清醒的第一受试者的脑活动模式；以及
b.通过在所述第二受试者体内复制所述第一受试者的所述脑活动模式来防止所述第二受试者的睡眠。
23.一种防止受试者的睡眠的方法，所述方法包括：
a.记录训练相位中已入睡的所述受试者的脑活动模式；
b.确定操作相位期间所述受试者的脑活动模式；以及
c.通过在所述操作相位期间破坏所述受试者的对应于已入睡的所述受试者的记录的脑活动的脑活动模式来防止所述受试者的睡眠。
24.一种诱导第二受试者的睡眠的方法，所述方法包括：
a.标识所述第一受试者的精神状态；
b.当所述第一受试者的所述精神状态是睡眠时，记录所述第一受试者的脑活动模式；以及
c.通过复制所述第二受试者的脑活动模式来诱导所述第二受试者的睡眠。
25.根据权利要求24所述的方法，其进一步包括验证所述第二受试者已入睡的步骤。
26.一种防止第二受试者的睡眠的方法，所述方法包括：
a.标识第一受试者的精神状态；
b.如果所述第一受试者是清醒的，则记录所述第一受试者的脑活动模式；以及
c.通过复制所述第二受试者的脑活动模式来防止所述第二受试者的睡眠。
27.根据权利要求26所述的方法，其进一步包括验证所述第二受试者清醒的步骤。
28.一种在第二受试者体内复制第一受试者的期望的精神状态的方法，所述方法包括：
a.标识所述第一受试者的精神状态；
b.通过记录脑活动模式来捕获所述第一受试者的精神状态；
c.将所述脑活动模式保存在非易失性存储器中；
d.从所述非易失性存储器检索所述脑活动模式；以及
e.通过在所述第二受试者体内诱导所述脑活动模式来在所述第二受试者体内复制所述第一受试者的所述期望的精神状态，其中所述期望的精神状态是睡眠状态和清醒状态之一。
29.一种在第二受试者体内复制第一受试者的精神状态的方法，所述方法包括：
a.记录所述第一受试者的脑波；以及
b.通过在所述第二受试者体内诱导所述第一受试者的所述脑波来在所述第二受试者体内复制所述第一受试者的所述精神状态，其中所述精神状态是清醒状态和睡眠状态之一。
30.一种在第二受试者体内复制第一受试者的期望的精神状态的方法，所述方法包括：
a.标识所述第一受试者的精神状态；
b.记录所述第一受试者在期望的精神状态下的脑波；以及
c.通过在所述第二受试者体内诱导所述第一受试者的所述脑波来在所述第二受试者体内复制所述第一受试者的所述期望的精神状态，其中所述期望的精神状态是睡眠状态和清醒状态之一。
31.一种在第二受试者体内复制第一受试者的期望的精神状态的方法，所述方法包括：
a.标识所述第一受试者的精神状态；
b.记录所述第一受试者在期望的精神状态下的脑波；
c.将所述脑波保存在非易失性存储器中；
d.从所述非易失性存储器检索所述脑波；以及
e.通过在所述第二受试者体内诱导所述第一受试者的所述脑波来在所述第二受试者体内复制所述第一受试者的所述期望的精神状态，其中所述期望的精神状态是睡眠状态和清醒状态之一。
32.根据权利要求28到31中任一项所述的方法，其进一步包括标识所述第二受试者的精神状态以验证所述第二受试者具有所述期望的精神状态的步骤。
33.根据权利要求28到32中任一项所述的方法，其中记录脑波是记录EEG、记录qEEG和记录MEG之一。
34.一种用于在第二受试者体内复制第一受试者的期望的精神状态的系统，所述系统包括：
a.第一设备，所述第一设备用于记录所述第一受试者在期望的精神状态下的脑波；
b.非易失性存储器，所述非易失性存储器与所述第一设备耦合以用于存储所述脑波的记录；以及
c.第二设备，所述第二设备用于在所述第二受试者体内诱导所述脑波以在所述第二受试者体内复制所述第一受试者的所述期望的精神状态，所述第二设备被配置成从所述非易失性存储器接收所述第一受试者的所述脑波的所述记录，其中所述期望的精神状态是睡眠状态和清醒状态之一。
35.根据权利要求34所述的系统，其中所述第一设备是脑电图仪和脑磁图仪之一。
36.根据权利要求34所述的系统，其中所述第二设备是以下之一：tDCS装置、tACS装置、HD tDCS装置、TMS装置、深度TMS装置、光信号和声音信号中的一个的来源，所述来源被配置成在光信号和声音信号中的所述一个上调制脑波频率。
37.一种在第二受试者体内复制第一受试者的期望的精神状态的方法，所述方法包括：
a.标识所述第一受试者的精神状态；
b.记录所述第一受试者的EEG和MEG中的至少一个，所述第一受试者处于期望的精神状态；
c.对EEG和MEG信号中的所述至少一个进行处理；
d.将处理后的信号保存在非易失性存储器中；
e.从所述非易失性存储器检索所述处理后的信号；
f.在至少一个刺激上调制所述处理后的信号；以及
g.通过用所述至少一个刺激刺激所述第二受试者来在所述第二受试者体内复制所述第一受试者的所述期望的精神状态，其中所述期望的精神状态是睡眠状态和清醒状态之一。
38.根据权利要求37所述的方法，其中所述处理包括从EEG和MEG信号中的所述至少一个中去除噪声的步骤。
39.根据权利要求37所述的方法，其中所述处理包括压缩EEG和MEG信号中的所述至少一个的步骤。
40.根据权利要求39所述的方法，其进一步包括对从所述非易失性存储器检索到的EEG和MEG信号中的处理后的所述至少一个进行解压缩的步骤。
41.根据权利要求37所述的方法，其中所述至少一个刺激是光信号、声音信号、电信号和磁场中的至少一种。
42.根据权利要求41所述的方法，其中所述电信号是直流电信号和交流电信号之一。
43.根据权利要求41所述的方法，其中所述电信号包括经颅电刺激。
44.根据权利要求43所述的方法，其中所述经颅电刺激是tDCS、高清tDCS和tACS之一。
45.根据权利要求42所述的方法，其中所述电信号包括经颅磁刺激。
46.根据权利要求45所述的方法，其中所述经颅磁刺激是深度磁刺激。
47.根据权利要求41所述的方法，其中光刺激是环境光或直射光之一。
48.根据权利要求41所述的方法，其中声音刺激是双耳位和等时音调之一。
49.一种用于在第二受试者体内复制第一受试者的期望的精神状态的系统，所述系统包括：
脑电图仪和脑磁图仪中的至少一个，用于记录所述第一受试者的脑波，所述第一受试者处于期望的精神状态；
处理器，所述处理器耦合脑电图仪和脑磁图仪之一，所述处理器被配置成执行信号分析并且计算所述第一受试者的所述脑波的至少一个主导频率；
非易失性存储器，所述非易失性存储器与所述第一处理器耦合以用于存储所述第一受试者的所述脑波的所述至少一个频率；以及
第二设备，所述第二设备用于在所述第二受试者体内诱导所述脑波以在所述第二受试者体内复制所述第一受试者的所述期望的精神状态，所述第二设备被配置成从所述非易失性存储器接收所述第一受试者的所述脑波的所述至少一个主导频率，其中所述期望的精神状态是睡眠状态和清醒状态之一。
50.根据权利要求49所述的系统，其中所述第二设备是以下中的至少一个：tDCS装置、高清tDCS装置、tACS装置、TMS装置、深度TMS装置、能够在光上调制所述至少一个主导频率的光源以及能够在声音上调制所述至少一个主导频率的声源。
51.根据权利要求49所述的系统，其中所述光源是双耳节拍源和等时音调源之一。
52.一种在第二受试者体内复制第一受试者的昼夜节律的方法，所述方法包括：
a.记录所述第一受试者的EEG和MEG中的一个，所述第一受试者具有所述昼夜节律的期望相位；
b.对EEG和MEG中的记录的一个进行处理以去除噪声；
c.将EEG和MEG中的处理后的一个保存在非易失性存储器中；
d.从所述非易失性存储器检索EEG和MEG中的所述处理后的一个；以及
e.通过经由经颅刺激向所述第二受试者回放所述第一受试者的EEG和MEG中的所述处理后的一个来在所述第二受试者体内复制所述第一受试者的所述昼夜节律的期望相位。
53.根据权利要求52所述的方法，其进一步包括以下步骤：
a.在将EEG和MEG中的所述记录的一个保存在所述非易失性存储器之前对其进行压缩；以及
b.在从所述非易失性存储器检索EEG和MEG中的经压缩的一个之后对EEG和MEG中的所述记录的一个进行解压缩。
54.根据权利要求52所述的方法，其中所述经颅刺激为tDCS、HD-tDCS、TMS和深度TMS之一。
55.一种用于在第二受试者体内复制第一受试者的昼夜节律的系统，所述系统包括：
a.脑电图仪和脑磁图仪中的至少一个，所述脑电图仪和所述脑磁图仪分别用于记录EEG和MEG中的一个；
b.第一处理器，所述第一处理器耦合到脑电图仪和脑磁图仪中的所述一个并且被配置成用于数字信号处理以从EEG和MEG中的所述记录的一个中去除噪声；
c.非易失性存储器，所述非易失性存储器与所述处理器耦合以用于存储EEG和MEG中的处理后的一个；以及
d.经颅刺激装置，所述经颅刺激装置耦合到所述非易失性存储器以用于向所述第二受试者回放EEG和MEG中的所述处理后的一个以在所述第二受试者体内诱导所述第一受试者的所述昼夜节律。
56.根据权利要求55所述的系统，其中所述经颅刺激装置为tDCS、HD-tDCS、TMS和深度TMS之一。
57.根据权利要求55所述的系统，其中所述第一处理器被进一步配置成压缩所述EEG和所述MEG中的所述处理后的一个。
58.根据权利要求55所述的系统，其进一步包括第二处理器，所述第二处理器耦合到所述非易失性存储器和所述经颅刺激装置，所述第二处理器被配置成对EEG和MEG中的经压缩的一个进行解压缩。
59.一种在人体中诱导脑活动周期的方法，所述方法包括：
记录来自第一受试者的一组脑活动周期并用在所述脑活动周期之前或伴随所述脑活动周期的情景对所述脑活动周期加标签；
对记录的一组脑活动周期进行处理以在所述一组脑活动周期中表示的脑波模式的幅度、频率或时间延迟下进行归一化，同时保留施加在表示神经元群之间的协调的同步脑波模式上的至少一个调制模式；
选择处理后的记录的一组脑活动周期的记录；
生成用于第二受试者的刺激，所述刺激至少包括施加在表示神经元群之间的协调的所述同步脑波模式上的所述至少一个调制模式，用于所述第二受试者的所述刺激基于所述第二受试者的至少脑波状态进行适应性地反馈控制，所述生成被控制以将所述第二受试者的所述脑波状态与所选记录中反映的所述第一受试者的所述同步脑波模式同步。
60.一种修改受试者的精神状态的方法，所述方法包括：
选择期望的精神状态；
确定所述受试者的当前精神波模式；
提取所述受试者的当前脑波模式的特征波的相位状态；
确定所述受试者的所述当前脑波模式的期望变化，以将所述受试者的当前精神状态改变成所述期望的精神状态；
用与所述相位状态时间同步的刺激来刺激所述受试者；
在至少一个刺激之后监测所述受试者的所述脑波模式；以及
基于所述监测来修改所述刺激。
61.一种修改精神状态的方法，所述方法包括：
取决于所述精神状态来提取刺激-响应的特征时机；
施加刺激；以及
在施加的刺激之后监测所述刺激-响应的所述特征时机的变化。
62.一种改变受试者的精神状态的方法，所述方法包括：
确定所述受试者的受监测神经或运动模式的时间关系；
取决于至少已确定时间关系来向所述受试者施加至少一个刺激；以及
基于所述受试者的所述受监测神经或运动模式的所述时间关系的至少变化来确定所述受试者的精神状态变化的改变。
63.一种增强非REM深度睡眠的方法，所述方法包括：
将慢波睡眠分量与获取的脑波模式在统计学上分开；
基于将慢波非REM睡眠分量在统计上分开来定义刺激模式；以及
用定义的刺激模式来刺激受试者。
64.一种增强深度非REM睡眠的方法，所述方法包括：
从至少一个受试者的内源性脑活动EEG记录中提取表示包括慢波睡眠的深度非REM睡眠状态的脑波模式；
使用统计处理算法对所提取脑波模式进行处理，以将慢波非REM睡眠分量与所述至少一个受试者的所述内源性脑活动EEG记录分开；
反转处理后的所提取脑波模式；以及
用反转的处理后的所提取脑波模式刺激受试者。
65.一种神经刺激器，其包括：
存储器，所述存储器被配置成存储获取的脑波模式；
至少一个处理器，所述至少一个处理器被配置成：
将慢波非REM睡眠分量与所述获取的脑波模式在统计上分开；以及
基于所述将慢波非REM睡眠分量在统计上分开来定义脑刺激模式；以及
输出信号发生器，所述输出信号发生器被配置成定义脑刺激模式。
66.一种用于增强深度睡眠的系统，所述系统包括：
存储器，所述存储器被配置成存储来自至少一个受试者的内源性脑活动EEG记录的表示包括慢波非REM睡眠的深度非REM睡眠状态的脑波模式；
至少一个处理器，所述至少一个处理器被配置成使用统计处理算法对所提取脑波模式进行处理，以将慢波非REM睡眠分量与所述至少一个受试者的所述内源性脑活动EEG记录分开；以及
刺激器，所述刺激器被配置成基于处理后的所提取脑波模式来生成刺激信号。
67.根据权利要求66所述的系统，其中所述刺激器包括经颅电刺激器。
68.一种改善第二受试者的睡眠的方法，所述方法包括：
记录在睡觉的第一受试者的脑活动模式；以及
通过在所述第二受试者体内复制所述第一受试者的所述脑活动模式来改善所述第二受试者的睡眠。
69.根据权利要求68所述的方法，其中使用EEG和MEG中的至少一个记录在睡觉的第一受试者的所述脑活动模式。
70.根据权利要求68所述的方法，其中所述复制所述第一受试者的所述脑活动模式通过在至少一个刺激上调制在睡觉的第一受试者的所述脑活动模式的至少一个频率来完成。
71.根据权利要求68所述的方法，其中所述至少一个刺激是光信号、声音信号、电流和磁场之一。
72.根据权利要求71所述的方法，其中所述光信号是环境光或平行光之一。
73.根据权利要求71所述的方法，其中所述声音信号是双耳位和等时音调之一。
74.一种用于控制具有可编程处理器的脑刺激器的计算机可读介质，所述计算机可读介质包括：
用于分析来自受试者的脑活动数据以确定所述脑活动数据中表示的睡眠-清醒状态的指令；
用于相对于所述睡眠-清醒状态对所述脑活动数据进行分类的指令；
用于基于睡眠-清醒状态的至少一个周期模型来确定所述脑活动数据中表示的所述睡眠-清醒状态的期望变化的指令；
用于控制所述脑刺激器的脑刺激模式以在基本上不通过刺激使所述受试者直接苏醒的情况下达到所述睡眠-清醒状态的所述期望变化的指令。
75.根据权利要求74所述的计算机可读介质，其中所述脑刺激器包括听觉刺激器和视觉刺激器中的至少一种，所述听觉刺激器和所述视觉刺激器向所述受试者呈现基本上没有语义、音乐或对象内容的信号。
76.根据权利要求74所述的计算机可读介质，其中所述脑刺激模式被调适成使脑波模式与调制的波形同步。
77.根据权利要求74所述的计算机可读介质，其中睡眠-清醒状态的所述期望变化是脑半球特定的。
78.根据权利要求74所述的计算机可读介质，其进一步包括用于对所述脑活动数据对所述脑刺激模式的响应进行建模并且将所述脑刺激模式调适成最优地达到所述睡眠-清醒状态的所述期望变化的指令。
79.根据权利要求74所述的计算机可读介质，其进一步包括用于相对于群体常值来归一化所述脑活动数据并且取决于所述群体常值来对刺激模式数据库进行存取的指令。
80.根据权利要求79所述的计算机可读介质，其进一步包括用于取决于所述受试者的所述脑活动数据与所述群体常值之间的差异对从所述刺激模式数据库存取的刺激模式进行去归一化的指令。
81.根据权利要求74所述的计算机可读介质，其进一步包括用于将具有随机分量的噪声模式引入到所述脑刺激模式中的指令。
82.一种在受试者体内诱导对应于预定顺序的精神状态的方法，所述方法包括：
确定精神状态的所述预定顺序和所述受试者的当前精神状态；
基于精神状态的所述预定顺序和所述受试者的精神状态的过去历史来对来自数据库的至少一个记录进行处理，以生成用于达到所述受试者的目标精神状态的最优脑刺激模式；以及
选择性地取决于所述最优脑刺激模式用直接脑刺激器和间接感觉输入脑刺激器中的至少一个来刺激所述受试者。
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Ricci et al. Repetitive transcranial magnetic stimulation of the posterior parietal cortex for the treatment of focal dystonia
Virgillito et al. Three-chord harmonic sequences with unexpected out-of-key endings: are they suitable stimuli for eliciting N400 responses?
Virgillito et al. EEG and autonomic response to pleasant touch: an insight in healthy subject and in patients with disorders of consciousness
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Benedetti et al. XXII National Congress of the Italian Society of Psychophysiology
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Tomorgraphy A partial list of peer reviewed journal articles that used Low Resolution Electromagnetic Tomorgraphy (LORETA) as of October 2009
Moran Neuromagnetic correlates of motor system dynamics and dysfunction

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