WO2020124838A1 - 一种基于实时闭环振动刺激增强的脑机接口方法及系统 - Google Patents

一种基于实时闭环振动刺激增强的脑机接口方法及系统 Download PDF

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WO2020124838A1
WO2020124838A1 PCT/CN2019/079096 CN2019079096W WO2020124838A1 WO 2020124838 A1 WO2020124838 A1 WO 2020124838A1 CN 2019079096 W CN2019079096 W CN 2019079096W WO 2020124838 A1 WO2020124838 A1 WO 2020124838A1
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time
real
eeg signal
brain
phase
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宋爱国
张文彬
曾洪
徐宝国
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Southeast University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/375—Electroencephalography [EEG] using biofeedback
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/377—Electroencephalography [EEG] using evoked responses
    • A61B5/378—Visual stimuli
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/7455—Details of notification to user or communication with user or patient; User input means characterised by tactile indication, e.g. vibration or electrical stimulation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier transforms
    • G06F17/142—Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/016—Input arrangements with force or tactile feedback as computer generated output to the user
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2203/00—Indexing scheme relating to G06F3/00 - G06F3/048
    • G06F2203/01—Indexing scheme relating to G06F3/01
    • G06F2203/011—Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns

Definitions

  • the invention relates to a brain-computer interface method and system based on real-time closed-loop vibration stimulation enhancement, belonging to the field of brain-computer interface technology.
  • the first brain-computer interface international conference held in 1999 gave the definition of the brain-computer interface, that is, the brain-computer interface is a communication system that does not depend on the normal output pathway composed of peripheral nerves and muscles.
  • the biological principle of the brain-computer interface is when the brain is performing thinking activities, generating action awareness, or being stimulated by the outside world. Nerve cells will produce tens of millivolts of micro-electric activity, and a large number of nerve cells' electrical activity will be transmitted to the surface of the scalp to form brain waves.
  • the Brain-Computer Interface (BCI) is an advanced technology based on EEG signals or other related technologies that converts brain activity characteristics into predefined commands to communicate with the outside world or control other external devices.
  • the EEG signal extracted by the method of placing electrodes on the scalp is called the scalp electroencephalogram (EEG). Since there is a barrier between the brain and the scalp, the meninges, the skull, and multiple layers of tissue, the signal-to-noise ratio of the EEG signal is very low and it is difficult to extract To a stable and reliable signal, this is a more serious problem in the control of external devices, which seriously affects the application range of the brain-computer interface. At the same time, there are differences in EEG signals among different individuals. Studies have shown that about 30% of people use the brain-computer interface with a low decoding rate, making it difficult to communicate and control the brain and the external environment. We call this type of person ''BCI blind''. Therefore, how to improve the actual performance such as the decoding rate of BCI is a key problem of the brain-computer interface.
  • BCI users can initiate brain control by simply performing imaginary left-hand or right-hand movements.
  • the kinesthetic imagination of hand movement produces event-related synchronization (ERD/ERS) in the subject's sensorimotor cortex.
  • Event-related synchronization is defined as a decrease or increase in power in a specific frequency band (eg, alpha band 8-13 Hz).
  • motor imaging-induced ERD activities are lateral contralateral hemispheres, that is, motor imaging induces ERD in the contralateral sensorimotor cortex. These lateral cortical activities constitute the neurophysiological basis of brain-computer interfaces based on motor imaging.
  • the potential solution to the problems faced by the development of BCI is the use of touch. Compared with visual stimulation, tactile sensation produces less visual fatigue, so that the user will not be excessively fatigued.
  • the human tactile receptors are mainly Mesna bodies distributed in the superficial skin, which can feel the light pressure stimulation of the skin; theusement body is mainly responsible for feeling the tactile sense; the deeper Basini's ring-shaped body mainly feels the pressure sense; also There are Pincus bodies and many nerve endings, touch pads and so on. Among them, Mayer's body is located at the tip adjacent to the main ridge and closest to the skin surface. These are particularly effective in low-frequency vibration (1-40 Hz) signal transmission, and therefore play an important role in detecting sensory vibration.
  • Vibration stimulation may potentially be in phase with natural oscillations, but due to the complexity and time-varying nature of EEG signals, matching spontaneous oscillations of the human body is a challenging issue.
  • the technical problem to be solved by the present invention is to overcome the shortcomings of the prior art, and to provide a brain-computer interface method based on real-time closed-loop vibration stimulation enhancement and to solve the problems of non-linear, non-stationary, non-Gaussian processes and other characteristics of the EEG signal itself
  • the system improves the signal-to-noise ratio of the brain-computer interface system and enhances brain-derived signals.
  • a brain-computer interface method based on real-time closed-loop vibration stimulation enhancement includes the following steps:
  • the time-frequency characteristics of the digital EEG signal are calculated using fast Fourier transform and the frequency value with the highest frequency energy is extracted as its main frequency;
  • the band-pass filtered digital EEG signal is calculated by Hilbert transform to obtain the instantaneous phase of the digital EEG signal;
  • the main frequency and instantaneous phase of the digital EEG signal of the segment are used as the frequency and initial value of the sine wave, respectively.
  • Phase generate a predicted sine wave, and obtain real-time phase information at the current time according to the predicted sine wave prediction;
  • the vibration motor is controlled to stimulate the sensory channel of the subject according to the control instruction.
  • the motor imagination task in the method includes left-hand or right-hand motor imagination.
  • bandpass filtering of the alpha band is performed on the digital EEG signal intercepted within a preset period.
  • the instantaneous phase of the digital EEG signal is calculated by Hilbert transform, and the formula is used:
  • y(t) is the digital EEG signal after performing Hilbert transform on x(t);
  • x(t) is the digital EEG signal after band-pass filtering;
  • p and v are Cauchy principal values Integration in the sense;
  • ⁇ x (t) is the instantaneous phase at time t.
  • the predicted sine wave f s generated in the method is specifically:
  • f main is the main frequency of the digital EEG signal intercepted in the preset period;
  • t f is the length of the predicted waveform;
  • ⁇ x (t) is the instantaneous phase at time t.
  • a brain-computer interface system based on real-time closed-loop vibration stimulation enhancement proposed by the present invention includes:
  • the human-computer interaction module is used to provide the display of the motion imaging task to the subjects;
  • EEG signal acquisition module used to collect digital EEG signals generated by the subject's motor imagination
  • the real-time phase prediction module is used to read the collected digital EEG signals and determine whether it exceeds the preset time period, if yes, intercept the digital EEG signals within the preset time period, otherwise continue to read the collected digital EEG signals; and It is used to band-pass filter the digital EEG signal within the preset period of time, and calculate the time-frequency characteristics of the digital EEG signal by fast Fourier transform and extract the frequency value with the highest frequency energy as its main Frequency, and the instantaneous phase of the digital EEG signal is calculated by Hilbert transform; the main frequency and the instantaneous phase of the digital EEG signal are used as the frequency and initial phase of the sine wave to generate the predicted sine wave, and Obtain real-time phase information at the current moment according to the predicted sine wave prediction;
  • the electroencephalogram signal analysis module is used to judge whether the vibration stimulation is applied in the phase interval according to the real-time phase information obtained at the predicted current time, and generate and output a control instruction according to the judgment result;
  • the vibration stimulation feedback module is used to control the vibration motor to stimulate the sensory channel of the subject according to the control instruction output by the electroencephalogram analysis module.
  • the EEG signal acquisition module includes an EEG cap, an EEG signal amplifier, a low-pass and band-reject filter, an analog-to-digital conversion module, and a communication module connected in sequence.
  • the real-time phase prediction module uses a band-pass filter to band-pass filter the digital EEG signal within a preset period.
  • the band pass filter uses a tenth order elliptical infinite impulse response filter.
  • the vibration stimulation feedback module sets different vibration frequencies to act on the left hand and the right hand according to control instructions.
  • the method and system of the present invention guide the subject to perform a motor imaging task by displaying, and at the same time use the time-frequency characteristics of the collected EEG signal data segment to generate a predicted sine wave, and then predict the real-time EEG signal phase information based on the predicted sine wave, and use the predicted
  • the instantaneous phase information is used to control the vibration motor to apply vibration stimulation at the tip of a human finger to achieve a real-time closed-loop vibration stimulation effect to enhance the signal-to-noise ratio of EEG signals and improve the decoding rate of motor imaging tasks.
  • the invention can adjust the electroencephalogram rhythm through vibration stimulation feedback, improve the signal-to-noise ratio of the brain-computer interface system, and provide a new scheme for enhancing the brain-source signal. It enhances the recognition rate of motion imaging signals, reduces the "BCI blindness" phenomenon, and makes communication between users and the outside world more effective and convenient.
  • the present invention can improve the real-time nature of vibration stimulation, by focusing the stimulation on the optimal phase for enhancing the EEG signal and by ensuring that this is repeated in multiple cycles in order to maximize the stimulation effect using cumulative effects, Improve the decoding rate of motor imaging tasks and reduce the differences between brain-computer interfaces between individuals.
  • a system may achieve vibration control with lower power requirements and higher specificity, and may reduce the risk of tolerance and rebound.
  • FIG. 1 is a schematic structural diagram of a brain-computer interface system based on real-time closed-loop vibration stimulation enhancement of the present invention.
  • FIG. 2 is a flowchart of real-time phase prediction in the method of the present invention.
  • FIG. 3 is a schematic diagram of a motion imaging experiment paradigm based on real-time closed-loop stimulus feedback.
  • the present invention designs a brain-computer interface system based on real-time closed-loop vibration stimulation enhancement.
  • the system mainly includes: a human-computer interaction module, an EEG signal acquisition module, a real-time phase prediction module, and an EEG signal analysis module 1.
  • a vibration stimulation feedback module wherein the output end of the EEG signal acquisition module is connected to the input end of the real-time phase prediction module, and the output end of the real-time phase prediction module is connected to the input end of the EEG signal analysis module; the EEG signal analysis module The output end of is connected to the vibration stimulation feedback module, and the vibration stimulation feedback module directly acts on the body of the subject. In this embodiment, it acts on the left and right hands of the subject.
  • the human-computer interaction module is composed of a display screen of the content of the motor imaging task displayed on the screen, and is used to provide the motor imaging task to the subject; the subject is guided through visual channels to complete the left-hand or right-hand motor imaging task.
  • the EEG signal acquisition module is used to collect the digital EEG signals generated by the subject's motor imagination; it mainly includes the EEG cap, EEG signal amplifier, low-pass and band-reject filter, analog-to-digital conversion module and Communication module, the EEG cap is worn on the head of the subject, and the EEG signal generated when the subject's movement is imagined is sequentially amplified by the EEG signal amplifier, low-pass and band-pass filter for low-pass filtering, After the analog-to-digital conversion module performs analog-to-digital conversion, a digital brain electrical signal is obtained, and the digital brain electrical signal is transmitted to the real-time phase prediction module through the communication module.
  • Real-time phase prediction module used to receive the digital EEG signal transmitted by the EEG signal collection module through the USB interface, read the collected digital EEG signal and judge whether it exceeds the preset time period, or intercept the digital brain within the preset time period Electrical signals, otherwise continue to read the collected digital EEG signals; and used to band-pass filter the digital EEG signals within the preset period of time, and then obtain the digital EEG signals by fast Fourier transform calculation Time-frequency feature and extract the frequency value with the highest frequency energy as its main frequency, and calculate the instantaneous phase of the digital EEG signal by Hilbert transform; use the main frequency and instantaneous phase of the digital EEG signal of the segment As the frequency and initial phase of the sine wave, respectively, a predicted sine wave is generated, and the real-time phase information at the current time is obtained according to the predicted sine wave prediction, thereby realizing the prediction of the instantaneous phase of the real-time EEG signal.
  • the EEG signal analysis module is used to determine whether the real-time phase information obtained by the real-time phase prediction module at the current time is in the phase interval where vibration stimulation is applied, and generate and output a control instruction according to the judgment result;
  • the vibration stimulation feedback module is used to control the vibration motor to vibrate the sensory channel of the subject according to the control instructions output by the electroencephalogram signal analysis module, stimulate the fingertips of the subject to affect the sensory channel of the subject, and adjust the rhythm of the brain signal.
  • the digital EEG signal includes an imaginary hand fist movement signal, etc.
  • the real-time phase prediction module of the system of the present invention uses a band-pass filter to band-pass filter the digital EEG signal within a preset period, and the band-pass filter
  • the frequency band is the alpha band of brain activity.
  • the band-pass filter uses a tenth-order elliptical infinite impulse response filter, that is, 500ms is used as a data segment for real-time analysis. First, a tenth-order elliptical infinite impulse response filter is used.
  • the EEG signal data band is band-pass filtered to obtain the required digital EEG signals in the alpha band and in the range of 8-12hz; the passband ripple is set to 0.5dB, and the stopband attenuation is set to 40dB. Because the selected data segment is short, if the finite impulse response FIR filter is used, the order will be limited, and the infinite impulse response IIR filter is selected.
  • the vibration stimulation feedback module in the system of the present invention can set different vibration frequencies to act on the left hand and the right hand according to the control instructions.
  • the vibration stimulation signal set on the left hand in the vibration stimulation module is a sine wave with a vibration frequency of 22hz.
  • the vibration stimulation signal acting on the right hand is a sine wave with a vibration frequency of 26hz. Due to the different sensitivity of the human sensory system to the left and right, setting different vibration frequencies can assist in generating the best stimulation effect.
  • the invention also proposes a brain-computer interface method based on real-time closed-loop vibration stimulation enhancement, which is used for signal processing by the above system, and specifically includes the following steps:
  • the human-computer interaction module provides the display of the motor imaging task to the subject, wherein the motor imaging task may include left-hand or right-hand motor imaging actions; the display in the human-computer interaction module shows that the stimulation includes three modes, specifically :
  • the real-time vibration stimulation module is activated, and the vibration stimulation module is mainly used to generate stimulation feedback according to the brain electrical signal acquired in real time, so as to realize the function of enhancing the brain electrical signal.
  • the subject then sat on a comfortable chair as required, wearing a 64-lead EEG cap on their head, and their eyes were about one meter away from the display.
  • the real-time EEG signal data of the user is obtained through the 64-lead EEG acquisition cap in the EEG signal acquisition module, which is processed and collected by each component in turn to generate the movement imagination.
  • Digital EEG signal data is obtained through the 64-lead EEG acquisition cap in the EEG signal acquisition module, which is processed and collected by each component in turn to generate the movement imagination.
  • Step 2 The real-time phase prediction module executes the real-time EEG signal phase prediction algorithm at the same time as the motor imaging task begins. As shown in the figure, the specific process is as follows:
  • the real-time phase prediction module reads the collected digital EEG signals and judges whether they exceed the preset period. If it exceeds 500ms, if it exceeds 500ms, it intercepts the digital EEG signals within the preset period of 500ms for analysis and prediction. Then continue to read the collected digital EEG signals.
  • the real-time phase prediction module analyzes and predicts the process:
  • the fast-Fourier transform is used to calculate the time-frequency characteristics of the digital EEG signal and extract the highest frequency value of the frequency energy as the main frequency of the digital EEG signal;
  • the frequency value with the highest frequency energy in the signal segment is extracted as the main frequency of the signal in the segment.
  • the instantaneous phase of the digital EEG signal x(t) is calculated by using Hilbert transform; the instantaneous phase extraction is calculated by Hilbert transform to obtain
  • the digital EEG signal y(t) of x(t) after performing Hilbert transform adopts the following formula:
  • ⁇ x (t) is the instantaneous phase at time t
  • x(t) is the digital EEG signal after band-pass filtering
  • p and v are integrals in the sense of Cauchy's principal value.
  • f s is the predicted sine wave
  • f main is the main frequency of the digital EEG signal intercepted in the preset period
  • t f is the length of the predicted waveform, set here to 50 ms, and the EEG can be obtained according to the selected current time point
  • the time difference of the instantaneous phase of the signal is determined
  • ⁇ x (t) is the instantaneous phase at time t.
  • Step 3 The EEG signal analysis module determines whether the real-time phase information obtained by the real-time phase prediction module predicts the real-time phase information at the current time t and determines whether the vibration stimulation is applied in the phase interval. If the judgment result is in the phase interval, the control command is generated and output.
  • the vibration stimulation feedback The module controls the vibration motor to stimulate the sensory channel of the subject according to the control instructions. Otherwise, when the judgment result is not in the phase interval, the control command is not output, and no vibration stimulation is required.
  • the present invention adopts the above method, and the test result is shown in FIG. 3. It takes about 3ms from the generation of the EEG signal to the recording by the EEG cap, and then to the real-time phase prediction module through the amplifier and USB transmission.
  • the computer executes the prediction algorithm to process the data. 5ms, it takes about 2ms to get the instantaneous phase value and convert it into a command to control the vibration of the vibration motor, so the 30ms of the predicted sine wave can be approximated as the EEG signal at the current time point, and the instantaneous phase at 30ms is taken as the vibration motor control trigger signal So as to achieve the purpose based on real-time phase stimulation.
  • the present invention designs a brain-computer interface method and system that can perform fingertip vibration stimulation based on the real-time phase of EEG signals to enhance the decoding rate of the motor imaging task.
  • the decoding rate for the current motor imaging brain-computer interface is not high.
  • Individual differences are too large, "BCI blind" phenomenon and other issues, designed a method to enhance EEG signals.
  • the present invention maximizes the stimulation effect by focusing the stimulation on the optimal phase for enhancing the EEG signal and by ensuring that this is repeated in multiple cycles in order to utilize the cumulative effect In this way, vibration control can be achieved with lower power requirements and higher specificity, and the risk of tolerance and rebound can be reduced.
  • the invention regulates the electroencephalogram rhythm through vibration stimulation feedback, improves the signal-to-noise ratio of the brain-computer interface system, and enhances the recognition rate of the motor imaging signal.

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Abstract

本发明公开了一种基于实时闭环振动刺激增强的脑机接口方法及系统，包括：将运动想象任务显示提供于受试者，并采集产生的数字脑电信号；读取数字脑电信号并判断是否超过预设时段,是则截取，否则继续读取；进行带通滤波，采用快速傅里叶变换计算得到数字脑电信号的时频特征并提取频率能量最高的频率值作为主频率；并采用希尔伯特变换计算得到数字脑电信号的瞬时相位；以主频率和瞬时相位分别作为正弦波的频率和初相，生成预测正弦波且预测获取实时相位信息；判断是否在施加振动刺激的相位区间，并生成和输出控制指令，根据控制指令控制振动电机振动刺激受试者的感觉通道。本发明提高了脑机接口系统的信噪比，增强了运动想象信号的识别率。

Description

一种基于实时闭环振动刺激增强的脑机接口方法及系统 技术领域
本发明涉及一种基于实时闭环振动刺激增强的脑机接口方法及系统，属于脑机接口技术领域。
背景技术
1999年召开的第一次脑机接口国际会议给出了脑机接口的定义，即脑-机接口是一种不依赖于正常的由外周神经和肌肉组成的输出通路的通讯系统。脑机接口的生物学原理是大脑在进行思维活动、产生动作意识或受外界刺激时。神经细胞将产生几十毫伏的微电活动，大量神经细胞的电活动传到头皮表层形成脑电波。脑-机接口(BCI)是以脑电信号或其他相关技术为基础，将大脑活动特征转化为预定义的命令，从而实现与外界交流或者控制其他外部设备的先进技术。
由头皮上放置电极方法提取的脑电信号称为头皮脑电信号(EEG)，由于从大脑到头皮之间存在脑膜、颅骨和多层组织的阻隔，所以EEG信号的信噪比很低，难以提取到稳定可靠的信号，这在对外部设备的控制中是一个较严重的问题，严重影响了脑机接口的应用范围。同时不同个体间的脑电信号也存在差异，研究表明大约有30％的人使用脑机接口时解码率较低，以至于难以实现大脑与外界环境的交流和控制，我们把这一类人称为‘’BCI盲‘’。所以如何提高BCI的解码率等实际表现是脑机接口的一个关键问题。
BCI用户可以通过简单地执行想象左手或右手运动来启动大脑控制。手部运动的动觉想象在受试者的感觉运动皮层中产生事件相关同步(ERD/ERS)。事件相关同步定义为特定频带(例如α波段8-13Hz)的功率减少或增加。通常，运动想象诱导的ERD活动侧向对侧半球，即运动想象诱导对侧感觉运动皮层中的ERD，这些侧向皮质活动构成了基于运动想象的脑机接口的神经生理学基础。
解决BCI发展所面临问题的潜在方法是采用触觉。与视觉刺激相比，触觉感觉产生较少的视觉疲劳，让使用者不会过度疲劳。人的触觉感受器主要是分布于表层皮肤中的麦斯纳小体，感受皮肤的轻压刺激；梅氏小体主要负责感受触觉；较为深层的巴西尼环层小体，主要感受压觉；还有品库斯小体及许多神经末梢、触盘等。其中，梅氏小体位于与主脊相邻并且最靠近皮肤表面的尖端。这些在低频振动(1-40Hz)信号传导中特别有效，因此在检测感觉振动中起着重要作用。
越来越多的研究表明，非侵入性的电刺激可以在锁定潜在的大脑节律时更有效地调节 神经活动。振动刺激有可能潜在的与自然震荡同相位，但由于EEG信号的复杂性与时变性，匹配人体自发的震荡是一项具有挑战性的问题。
发明内容
本发明所要解决的技术问题在于克服现有技术的不足，针对脑电信号本身的非线性、非平稳、非高斯过程等特征的问题，提供一种基于实时闭环振动刺激增强的脑机接口方法及系统，提高了脑机接口系统的信噪比，增强脑源信号。
本发明具体采用以下技术方案解决上述技术问题：
一种基于实时闭环振动刺激增强的脑机接口方法，包括以下步骤：
将运动想象任务显示提供于受试者，并采集受试者运动想象时产生的数字脑电信号；
读取所采集的数字脑电信号并判断是否超过预设时段,是则截取预设时段内的数字脑电信号，否则继续读取所采集的数字脑电信号；
对所截取预设时段内的数字脑电信号进行带通滤波后，采用快速傅里叶变换计算得到该段数字脑电信号的时频特征并提取其中频率能量最高的频率值作为其主频率；并对带通滤波后的数字脑电信号采用希尔伯特变换计算得到该段数字脑电信号的瞬时相位；以该段数字脑电信号的主频率和瞬时相位分别作为正弦波的频率和初相，生成预测正弦波，且根据预测正弦波预测获取当前时刻下的实时相位信息；
根据预测获取的当前时刻下的实时相位信息判断是否在施加振动刺激的相位区间，并根据判断结果生成和输出控制指令，根据控制指令控制振动电机振动刺激受试者的感觉通道。
进一步地，作为本发明的一种优选技术方案，所述方法中运动想象任务包括左手或右手运动想象动作。
进一步地，作为本发明的一种优选技术方案，所述方法中对所截取预设时段内数字脑电信号进行α波段的带通滤波。
进一步地，作为本发明的一种优选技术方案，所述方法中采用希尔伯特变变换计算得到该段数字脑电信号的瞬时相位，采用公式：
Figure PCTCN2019079096-appb-000001
Figure PCTCN2019079096-appb-000002
其中，y(t)是对x(t)执行希尔伯特变换后的数字脑电信号；x(t)是带通滤波后的这段数字脑电信号；p和v是柯西主值意义上的积分；θ x(t)是t时刻的瞬时相位。
进一步地，作为本发明的一种优选技术方案，所述方法中生成的预测正弦波f s具体为：
Figure PCTCN2019079096-appb-000003
其中，f main为截取预设时段内数字脑电信号的主频率；t f是预测波形的长度；θ x(t)是t时刻的瞬时相位。
本发明提出的一种基于实时闭环振动刺激增强的脑机接口系统，包括：
人机交互模块，用于将运动想象任务显示提供于受试者；
脑电信号采集模块，用于采集受试者运动想象时产生的数字脑电信号；
实时相位预测模块，用于读取所采集的数字脑电信号并判断是否超过预设时段,是则截取预设时段内的数字脑电信号，否则继续读取所采集的数字脑电信号；及用于对所截取预设时段内的数字脑电信号进行带通滤波后，通过快速傅里叶变换计算得到该段数字脑电信号的时频特征并提取其中频率能量最高的频率值作为其主频率，并通过希尔伯特变换计算得到该段数字脑电信号的瞬时相位；以该段数字脑电信号的主频率和瞬时相位分别作为正弦波的频率和初相，生成预测正弦波，且根据预测正弦波预测获取当前时刻下的实时相位信息；
脑电信号分析模块，用于根据预测获取的当前时刻下的实时相位信息判断是否在施加振动刺激的相位区间，根据判断结果生成和输出控制指令；
振动刺激反馈模块，用于根据脑电信号分析模块输出的控制指令控制振动电机振动刺激受试者的感觉通道。
进一步地，作为本发明的一种优选技术方案所述脑电信号采集模块包括依次连接的脑电帽、脑电信号放大器、低通和带阻滤波器、模数转换模块及通信模块。
进一步地，作为本发明的一种优选技术方案，所述实时相位预测模块采用带通滤波器对预设时段内数字脑电信号进行带通滤波。
进一步地，作为本发明的一种优选技术方案，所述带通滤波器采用十阶椭圆无限脉冲响应滤波器。
进一步地，作为本发明的一种优选技术方案，所述振动刺激反馈模块根据控制指令设置不同的振动频率作用于左手和右手。
本发明采用上述技术方案，能产生如下技术效果：
本发明方法及系统，通过显示指导受试者执行运动想象任务，同时利用采集的脑电信号数据段时频特征生成预测正弦波，再基于预测正弦波预测实时脑电信号相位信息，利用预测的瞬时相位信息来控制振动电机在人手指尖施加振动刺激，达到实时闭环的振动刺激效果以增强脑电信号的信噪比，提高运动想象任务解码率。本发明可以通过振动刺激反馈 调控脑电节律，提高了脑机接口系统的信噪比，为增强脑源信号提供了一种新方案。增强了运动想象信号的识别率，减少了“BCI盲”现象，使使用者与外界沟通更加有效便捷。
因此，本发明可以提高振动刺激的实时性，通过将刺激集中在用于增强脑电信号的最佳相位上并且通过确保在多个循环中重复这一点以便利用累积效应来使刺激效果最大化，提高运动想象任务解码率，减少脑机接口在个体之间的差异。与传统的开环持续刺激方法相比，这样的系统可能以更低的功率需求和更高的特异性实现振动控制，并且可以降低耐受性和反弹的风险。
附图说明
图1为本发明基于实时闭环振动刺激增强的脑机接口系统的结构示意图。
图2为本发明方法中实时相位预测流程图。
图3为本发明基于实时闭环刺激反馈的运动想象实验范式示意图。
具体实施方式
下面结合说明书附图对本发明的实施方式进行描述。
如图1所示，本发明设计了一种基于实时闭环振动刺激增强的脑机接口系统，该系统主要包括：人机交互模块、脑电信号采集模块、实时相位预测模块、脑电信号分析模块、振动刺激反馈模块，其中脑电信号采集模块的输出端与实时相位预测模块的输入端相连，实时相位预测模块的输出端与脑电信号分析模块的输入端相连；所述脑电信号分析模块的输出端与振动刺激反馈模块相连，振动刺激反馈模块直接作用于受试者身体上，本实施例中作用于受试者左右手。
所述人机交互模块，是由屏幕上显示的运动想象任务内容的显示屏组成，用于将运动想象任务显示提供于受试者；通过视觉通道指导受试者完成左手或右手运动想象任务。
脑电信号采集模块，用于采集受试者运动想象时产生的数字脑电信号；其主要包括依次连接的脑电帽、脑电信号放大器、低通和带阻滤波器、模数转换模块及通信模块，脑电帽佩戴于受试者头部，并对受试者运动想象时产生的脑电信号采集，依次经脑电信号放大器进行放大、低通和带阻滤波器进行低通滤波、模数转换模块进行模数转换后得到数字脑电信号，并通过通信模块将数字脑电信号传输至实时相位预测模块。
实时相位预测模块，用于通过USB接口接收脑电信号采集模块传输的数字脑电信号，读取所采集的数字脑电信号并判断是否超过预设时段,是则截取预设时段内的数字脑电信号，否则继续读取所采集的数字脑电信号；及用于对所截取预设时段内的数字脑电信号进行带通滤波后，通过快速傅里叶变换计算得到该段数字脑电信号的时频特征并提取其中频 率能量最高的频率值作为其主频率，并通过希尔伯特变换计算得到该段数字脑电信号的瞬时相位；以该段数字脑电信号的主频率和瞬时相位分别作为正弦波的频率和初相，生成预测正弦波，且根据预测正弦波预测获取当前时刻下的实时相位信息，实现对实时脑电信号的瞬时相位的预测。
脑电信号分析模块，用于根据实时相位预测模块预测获取的当前时刻下的实时相位信息判断是否在施加振动刺激的相位区间，根据判断结果生成和输出控制指令；
振动刺激反馈模块，用于根据脑电信号分析模块输出的控制指令控制振动电机振动刺激受试者的感觉通道，刺激受试者指尖进而影响受试者的感觉通道，调节脑电信号节律。
优选地，所述数字脑电信号包括想象手握拳运动信号等；本发明系统的实时相位预测模块，采用带通滤波器对预设时段内数字脑电信号进行带通滤波，所述带通滤波的频段为大脑活动的α频段，进一步地所述带通滤波器采用十阶椭圆无限脉冲响应滤波器，即以500ms为一个数据段进行实时分析，首先采用一个十阶椭圆无限脉冲响应滤波器对脑电信号数据段进行带通滤波，滤得所需要的α频段、范围在8-12hz的数字脑电信号；设置通带纹波为0.5dB，阻带衰减设置为40dB。因为所选取的数据段较短，所以若采用有限脉冲响应FIR滤波器会被限制阶数，选用无限脉冲响应IIR滤波器。
并且，本发明系统中振动刺激反馈模块，可根据控制指令设置不同的振动频率作用于左手和右手，如所述振动刺激模块中设置作用于左手的振动刺激信号是振动频率为22hz的正弦波，作用于右手的振动刺激信号是振动频率为26hz的正弦波。由于人体感觉系统对左右敏感程度不同，设置不同的振动频率可辅助产生最佳刺激效果。
本发明还提出一种基于实时闭环振动刺激增强的脑机接口方法，该方法用于上述系统进行信号处理，具体包括以下步骤：
步骤1、人机交互模块将运动想象任务显示提供于受试者，其中，运动想象任务可以包括左手或右手运动想象动作；所述人机交互模块中的显示器显示刺激包括三个模式，具体为：
(1)屏幕正中出现白色十字，受试者放松休息不进行想象；
(2)白色十字左端出现红色箭头，受试者执行左手运动想象任务；
(3)白色十字右端出现红色箭头，受试者执行右手运动想象任务；
在出现箭头的同时，实时振动刺激模块激活，所述振动刺激模块主要用于根据实时获取的脑电信号产生刺激反馈，实现增强脑电信号的作用。
然后，受试者按要求坐在舒服的座椅上，头部佩戴好64导联的脑电帽，双眼与显示 器距离一米左右。根据显示器上显示的箭头指向想象左手或右手握拳动作，通过脑电信号采集模块中64导联的脑电采集帽获取使用者实时脑电信号数据，依次经各部件处理采集得到运动想象时产生的数字脑电信号数据。
步骤2、实时相位预测模块在运动想象任务开始的同时执行实时脑电信号相位预测算法，如图所示，具体过程如下：
首先，实时相位预测模块读取所采集的数字脑电信号并判断是否超过预设时段,如是否超过500ms，若超过500ms则截取预设时段500ms内的数字脑电信号进行分析预测，若没有超过则继续读取所采集的数字脑电信号。
然后，实时相位预测模块进行分析预测过程：
对所截取预设时段500ms内的数字脑电信号进行带通滤波，获得运动想象任务下ERD现象特定的发生频段，如α波段，范围为8-12hz；
对带通滤波后的数字脑电信号数据，采用快速傅里叶变换计算得到该段数字脑电信号的时频特征并提取其中频率能量最高频率值作为该段数字脑电信号的主频率；本实施例中，提取信号段中频率能量最高的频率值作为该段信号的主频率。
对带通滤波后的数字脑电信号数据，采用希尔伯特变换计算得到该段数字脑电信号x(t)的瞬时相位；所述的瞬时相位提取通过希尔伯特变换计算，得到对x(t)执行希尔伯特变换后的数字脑电信号y(t)，采用如下公式：
Figure PCTCN2019079096-appb-000004
则t时刻的瞬时相位可以通过下式计算：
Figure PCTCN2019079096-appb-000005
其中，θ x(t)是t时刻的瞬时相位；x(t)是带通滤波后的这段数字脑电信号；p和v是柯西主值意义上的积分。
最后，利用该段数字脑电信号的主频率和瞬时相位分别作为正弦波的频率和初相，生成预测正弦波，且根据预测正弦波预测获取实时相位信息，实现对实时相位的预测；其中，所述正弦波函数公式如下：
Figure PCTCN2019079096-appb-000006
其中，f s是预测正弦波；f main为截取预设时段内数字脑电信号的主频率；t f是预测波形的长度，这里设置为50ms，可根据所选取的当前时间点与获取脑电信号瞬时相位时间点的时间差确定；θ x(t)是t时刻的瞬时相位。
上述中，由希尔伯特变换求得500ms脑电信号数据中480ms处的瞬时相位，即t＝480ms；由快速傅里叶变换计算得到数据段的主要频率f main；由这些参数可列出正弦波函数式，得到精确的预测正弦波，预测波形的长度t f设为参照信号长度的十分之一，即50ms，其原理是由于脑电信号是非线性不稳定的信号，所以在每50ms段内提取500ms的数据来进行预测，以提高预测的准确率。
步骤3、脑电信号分析模块根据实时相位预测模块预测获取的当前时刻t下的实时相位信息判断是否在施加振动刺激的相位区间，判断结果是位于相位区间则生成和输出控制指令，振动刺激反馈模块根据控制指令控制振动电机振动刺激受试者的感觉通道。否则判断结果为不位于相位区间时，不输出控制指令，则无需产生振动刺激。
本发明采用上述方法，其试验结果如图3所示，从脑电信号产生到被脑电帽记录，再经过放大器、USB传输进入实时相位预测模块大约需要3ms，电脑执行预测算法处理数据大约需要5ms，得到瞬时相位值并转化为指令控制振动电机振动大约需要2ms，所以预测正弦波的第30ms处可近似看作当前时间点的脑电信号，取30ms处的瞬时相位作为振动电机控制触发信号从而实现基于实时相位刺激的目的。
综上，本发明设计了一种可以基于脑电信号实时相位进行指尖振动刺激以增强运动想象任务的解码率的脑机接口方法及系统，针对当前运动想象脑机接口的解码率不高、个体差异过大、“BCI盲”现象等问题，设计了一种增强脑电信号的方法。相比于过去的持续、闭环的振动刺激，本发明通过将刺激集中在用于增强脑电信号的最佳相位上并且通过确保在多个循环中重复这一点以便利用累积效应来使刺激效果最大化，这样可以更低的功率需求和更高的特异性实现振动控制，并且可以降低耐受性和反弹的风险。本发明通过振动刺激反馈调控脑电节律，提高了脑机接口系统的信噪比，增强了运动想象信号的识别率。
上面结合附图对本发明的实施方式作了详细说明，但是本发明并不限于上述实施方式，在本领域普通技术人员所具备的知识范围内，还可以在不脱离本发明宗旨的前提下做出各种变化。

Claims (10)

  1. 一种基于实时闭环振动刺激增强的脑机接口方法，其特征在于，包括以下步骤：
    将运动想象任务显示提供于受试者，并采集受试者运动想象时产生的数字脑电信号；
    读取所采集的数字脑电信号并判断是否超过预设时段,是则截取预设时段内的数字脑电信号，否则继续读取所采集的数字脑电信号；
    对所截取预设时段内的数字脑电信号进行带通滤波后，采用快速傅里叶变换计算得到该段数字脑电信号的时频特征并提取其中频率能量最高的频率值作为主频率；并对带通滤波后的数字脑电信号采用希尔伯特变换计算得到该段数字脑电信号的瞬时相位；以该段数字脑电信号的主频率和瞬时相位分别作为正弦波的频率和初相，生成预测正弦波，且根据预测正弦波预测获取当前时刻下的实时相位信息；
    根据预测获取的当前时刻下的实时相位信息判断是否在施加振动刺激的相位区间，并根据判断结果生成和输出控制指令，根据控制指令控制振动电机振动刺激受试者的感觉通道。
  2. 根据权利要求1所述基于实时闭环振动刺激增强的脑机接口方法，其特征在于：所述方法中运动想象任务包括左手或右手运动想象动作。
  3. 根据权利要求1所述基于实时闭环振动刺激增强的脑机接口方法，其特征在于：所述方法中对所截取预设时段内数字脑电信号进行α波段的带通滤波。
  4. 根据权利要求1所述基于实时闭环振动刺激增强的脑机接口方法，其特征在于：所述方法中采用希尔伯特变换计算得到数字脑电信号的瞬时相位，采用公式：
    Figure PCTCN2019079096-appb-100001
    Figure PCTCN2019079096-appb-100002
    其中，y(t)是对x(t)执行希尔伯特变换后的数字脑电信号；x(t)是带通滤波后的数字脑电信号；p和v是柯西主值意义上的积分；θ x(t)是t时刻的瞬时相位。
  5. 根据权利要求1所述基于实时闭环振动刺激增强的脑机接口方法，其特征在于：所述方法中生成的预测正弦波f s具体为：
    Figure PCTCN2019079096-appb-100003
    其中，f main为截取预设时段内数字脑电信号的主频率；t f是预测波形的长度；θ x(t)是t时刻的瞬时相位。
  6. 一种基于实时闭环振动刺激增强的脑机接口系统，其特征在于，包括：
    人机交互模块，用于将运动想象任务显示提供于受试者；
    脑电信号采集模块，用于采集受试者运动想象时产生的数字脑电信号；
    实时相位预测模块，用于读取所采集的数字脑电信号并判断是否超过预设时段,是则截取预设时段内的数字脑电信号，否则继续读取所采集的数字脑电信号；及用于对所截取预设时段内的数字脑电信号进行带通滤波后，通过快速傅里叶变换计算得到该段数字脑电信号的时频特征并提取其中频率能量最高的频率值作为其主频率，并通过希尔伯特变换计算得到该段数字脑电信号的瞬时相位；以该段数字脑电信号的主频率和瞬时相位分别作为正弦波的频率和初相，生成预测正弦波，且根据预测正弦波预测获取当前时刻下的实时相位信息；
    脑电信号分析模块，用于根据预测获取的当前时刻下的实时相位信息判断是否在施加振动刺激的相位区间，根据判断结果生成和输出控制指令；
    振动刺激反馈模块，用于根据脑电信号分析模块输出的控制指令控制振动电机振动刺激受试者的感觉通道。
  7. 根据权利要求6所述基于实时闭环振动刺激增强的脑机接口方法，其特征在于：所述脑电信号采集模块包括依次连接的脑电帽、脑电信号放大器、低通和带阻滤波器、模数转换模块及通信模块。
  8. 根据权利要求6所述基于实时闭环振动刺激增强的脑机接口方法，其特征在于：所述实时相位预测模块采用带通滤波器对预设时段内数字脑电信号进行带通滤波。
  9. 根据权利要求8所述基于实时闭环振动刺激增强的脑机接口方法，其特征在于：所述带通滤波器采用十阶椭圆无限脉冲响应滤波器。
  10. 根据权利要求6所述基于实时闭环振动刺激增强的脑机接口方法，其特征在于：所述振动刺激反馈模块根据控制指令设置不同的振动频率作用于左手和右手。
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