WO2018081980A1 - 基于脑电与功能近红外光谱技术的神经血管耦合分析方法 - Google Patents

基于脑电与功能近红外光谱技术的神经血管耦合分析方法 Download PDF

Info

Publication number
WO2018081980A1
WO2018081980A1 PCT/CN2016/104455 CN2016104455W WO2018081980A1 WO 2018081980 A1 WO2018081980 A1 WO 2018081980A1 CN 2016104455 W CN2016104455 W CN 2016104455W WO 2018081980 A1 WO2018081980 A1 WO 2018081980A1
Authority
WO
WIPO (PCT)
Prior art keywords
signal
blood oxygen
response function
evoked potential
extracting
Prior art date
Application number
PCT/CN2016/104455
Other languages
English (en)
French (fr)
Inventor
蒋田仔
张鑫
左年明
司娟宁
Original Assignee
中国科学院自动化研究所
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 中国科学院自动化研究所 filed Critical 中国科学院自动化研究所
Priority to PCT/CN2016/104455 priority Critical patent/WO2018081980A1/zh
Priority to US16/961,700 priority patent/US11944447B2/en
Publication of WO2018081980A1 publication Critical patent/WO2018081980A1/zh

Links

Images

Classifications

    • 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
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A61B5/0261—Measuring blood flow using optical means, e.g. infrared light
    • 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
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
    • 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
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
    • A61B5/14553—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases specially adapted for cerebral tissue
    • 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
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts

Definitions

  • the invention relates to the field of electroencephalogram and functional near-infrared spectroscopy, in particular to a neurovascular coupling analysis method based on electroencephalogram and functional near-infrared spectroscopy.
  • Brain function activities include multiple processes such as neuronal activity and local energy metabolism. Complex functional activities cause the brain to collect multiple modal information, the most important of which are the electrical activity of neurons and the changes of blood oxygen metabolism in the activated area. The relationship between the two is called Neurovascular Coupling. This complex coupling process requires a series of activities between neurons, astrocytes, and capillaries. However, the basis for estimating neuronal activity using blood oxygenation in neuroscience research is still unclear, which affects the judgment of neuron activity to a certain extent.
  • fMRI functional magnetic resonance
  • EEG electrospray
  • fNIRS Functional near-infrared spectroscopy
  • HbR deoxygenated hemoglobin
  • HbO oxygenated hemoglobin
  • the optical signal of fNIRS and the electrical signal of EEG have almost no interference, and it is favored by many researchers because of its low cost, portability, non-invasiveness and convenient operation.
  • the combination of EEG and fNIRS technology enables simultaneous extraction of neuronal electrical activity and blood oxygen metabolism information in the activation zone, and provides active technical support for the study of neurovascular coupling with high synchronization requirements.
  • An in-depth study of the mechanisms of brain neuronal activity provides an important approach.
  • the present invention provides a neurovascular coupling analysis method based on EEG and functional near infrared spectroscopy.
  • a neurovascular coupling analysis method based on EEG and functional near-infrared spectroscopy characterized in that the method comprises:
  • the collecting the EEG signal and the cerebral blood oxygen signal specifically includes:
  • the electroencephalogram signal and the cerebral blood oxygen signal are simultaneously acquired by using a photoelectric synchronous brain activity detecting system.
  • extracting the event evoked potential signal from the EEG signal specifically includes:
  • the event evoked potential signal is obtained by filtering the EEG signal, removing EO component, detrending, data segmentation, removing artifacts, and superimposing averaging.
  • extracting the event evoked potential signal from the EEG signal further comprises: down sampling processing.
  • extracting the time characteristic of the event evoked potential signal specifically includes:
  • the temporal characteristics of the event evoked potential signal are extracted using a method of mode inversion visual stimulation.
  • the time characteristic is a delay.
  • extracting the blood oxygen response function signal from the cerebral blood oxygen signal specifically includes:
  • the blood oxygen signal is subjected to concentration conversion, filtering, data segmentation, artifact removal, and superposition averaging processing to extract the blood oxygen response function signal.
  • the amplitude characteristic of the blood oxygen response function signal is a peak amplitude;
  • the time characteristic of the blood oxygen response function signal is a rising delay time, a peak time, and a half width.
  • analyzing the influence of the temporal characteristic of the event evoked potential signal on the amplitude characteristic and the time characteristic of the blood oxygen response function signal, and obtaining a coupling result specifically comprising:
  • the invention provides a neurovascular coupling analysis method based on EEG and functional near infrared spectroscopy.
  • the method comprises synchronously collecting EEG signals and cerebral blood oxygen signals; extracting event evoked potential signals from EEG signals; extracting temporal characteristics of event evoked potential signals; extracting blood oxygen response function signals from cerebral blood oxygen signals; The amplitude characteristics and time characteristics of the blood oxygen response function signal; the influence of the temporal characteristics of the event evoked potential signal on the amplitude and time characteristics of the blood oxygen response function signal is obtained, and the coupling result is obtained.
  • the embodiment of the present invention utilizes the influence of the temporal characteristics of the event evoked potential signal on the amplitude characteristics and temporal characteristics of the blood oxygen response function signal, and describes the relationship between the electrical activity of the neuron and the blood oxygen response from the time characteristic, and overcomes the traditional Only based on the analysis of the relationship between the two angles of magnitude, it is possible to simultaneously analyze the electrical activity of neurons and activate the changes of blood oxygen metabolism in the brain area to assist in the analysis of brain function activities. It can be used for clinical research of many major neuropsychiatric diseases, including epilepsy. Clinical studies of special groups such as children.
  • FIG. 1 is a flow chart showing a method for analyzing a neurovascular coupling based on an electroencephalogram and a functional near-infrared spectroscopy technique according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of a visual evoked potential corresponding to a visual mode inversion experiment according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a blood oxygen response function according to an embodiment of the present invention.
  • 4a is a schematic diagram of an experimental paradigm (25 seconds checkerboard flipping stimulation and 30 second recovery period) of a visual black and white checkerboard inversion experiment according to an embodiment of the present invention
  • 4b is a schematic view showing the arrangement of a near-infrared light pole and an electroencephalogram electrode in a visual black-and-white checkerboard inversion experiment according to an embodiment of the present invention
  • VEP waveform 5a is a schematic diagram of a VEP waveform at different contrasts in accordance with an embodiment of the present invention.
  • 5b is a schematic diagram showing the relationship between VEP delay and contrast according to an embodiment of the present invention.
  • FIG. 6a is a schematic diagram of a HRF waveform at different contrasts in accordance with an embodiment of the present invention.
  • 6b is a schematic diagram showing the relationship between HRF activation intensity and contrast according to an embodiment of the present invention.
  • FIG. 7 is a schematic diagram of a neurovascular coupling relationship according to an embodiment of the present invention.
  • FIG. 8 is a schematic flow chart of a neurovascular coupling analysis method based on EEG and functional near-infrared spectroscopy according to another embodiment of the present invention.
  • the basic idea of the embodiment of the present invention is based on the photoelectric synchronous brain activity detection system, and the electrophysiological activity and the blood oxygen metabolism change of the same brain region are synchronously collected by the EEG technology and the function near infrared technology, and the two modes of light and electricity are utilized. Data, neurovascular coupling analysis of EEG and cerebral blood oxygen bimodal fusion from the perspective of temporal characteristics.
  • the relationship between the electrical activity of the neurons and the blood oxygen response is described in terms of temporal characteristics.
  • Embodiments of the present invention provide a neurovascular coupling analysis method based on EEG and functional near-infrared spectroscopy, as shown in FIG. 1 , the method includes:
  • S100 Collecting EEG signals and cerebral blood oxygen signals.
  • this step can collect EEG signals and cerebral blood oxygen signals at the same time.
  • the photoelectric synchronous brain activity detection system can be used to synchronously collect brain electrical signals and cerebral blood oxygen signals.
  • the subject was subjected to multi-channel simultaneous acquisition of EEG signals and cerebral blood oxygen signals.
  • the EEG signals and cerebral blood oxygen signals of all the configured channels were collected at the same time. This ensures signal synchronization between the configured channels.
  • this step may perform processing such as filtering, downsampling, removing EO component, detrending, data segmentation, removing artifacts, and superimposing average on the collected EEG signals to extract event-induced Potential signal (ERP).
  • processing such as filtering, downsampling, removing EO component, detrending, data segmentation, removing artifacts, and superimposing average on the collected EEG signals to extract event-induced Potential signal (ERP).
  • EEP event-induced Potential signal
  • the filtering may be band pass filtering to extract the band of interest of the EEG signal, such as: 1-100 Hz or extracting rhythm components of the EEG signal, such as Delta rhythm ( ⁇ 4 Hz), Theta rhythm (4-7 Hz), Alpha rhythm (8-15Hz), Beta rhythm (16-30Hz), etc.
  • rhythm components of the EEG signal such as Delta rhythm ( ⁇ 4 Hz), Theta rhythm (4-7 Hz), Alpha rhythm (8-15Hz), Beta rhythm (16-30Hz), etc.
  • the step of removing the ocular electrical interference component may refer to removing components related to the ocular electrical signal based on an Independent Component Analysis (ICA) (eg, removing components interfered by the ocular electrical signal).
  • ICA Independent Component Analysis
  • the above detrending can refer to the removal of drift by detrending processing, taking into account the DC component of the signal.
  • the above data segmentation processing may refer to dividing the data into segments according to the time stamp of the event occurrence.
  • the artifact removal described above may refer to the removal of data segments in which large motion artifacts are present.
  • the method may be adopted to set a certain threshold. When the amplitude of the EEG signal exceeds the threshold, the data segment is not included in the subsequent superimposed average.
  • the above-mentioned superimposition averaging process may refer to synthesizing the processed data segments and superimposing the average extracted ERP signals.
  • VEP Visual evoked potential
  • amplitude characteristics magnitude
  • delay characteristics delay characteristics
  • the amplitude represents the amount of information reaching the visual cortex
  • the delay represents the amount of information needed to receive visual stimuli from the retina and conduct it to the visual cortex. time. Because VEP provides important clinical diagnostic information for the functional integrity of the vision system. In 1970, VEP was used to assess vision in infants and children. Since then, more and more researchers have applied VEP to the clinic to measure the visual pathway integrity and the diagnosis of vision-related diseases by measuring the amplitude and delay of each component caused by visual stimuli.
  • VEP is also often used to study the neurovascular coupling relationship of the visual cortex. This research is mainly based on the amplitude of VEP, focusing on the VEP amplitude and blood oxygenation. The relationship between the responses. However, the effect of VEP time characteristics (latency) on blood oxygen response is poorly understood. There is no report on the relationship between VEP delay as an indicator and the relationship with blood oxygenation.
  • VEP amplitude mainly reflects the type and shape information of the stimulus
  • VEP delay mainly reflects the contrast and brightness information of the stimulus
  • some visual information is encoded in the VEP delay, that is, visual stimulation
  • the contrast and brightness are mainly encoded in the delay information
  • the type and shape of the stimulus are mainly encoded in the amplitude information
  • the time coding time feature
  • the neuron discharge rate amplitude characteristics
  • this step can use the method of mode inversion visual stimulation to obtain the amplitude and time characteristics of the event evoked potential signal.
  • FIG. 2 exemplarily shows a schematic diagram of a visual evoked potential corresponding to a visual mode inversion experiment.
  • the horizontal axis represents latency and the vertical axis represents amplitude (amplitude).
  • Figure 2 mainly shows the first negative peak (N75) appearing around 75ms, the first positive peak (P100) appearing around 100ms, and the second negative peak (N135) appearing around 135ms.
  • the embodiment of the present invention can be extracted, for example, by the peaks and troughs of the VEP.
  • each extreme value represents a component.
  • this component contains two main features, namely, the amplitude characteristic is about 7.5 ⁇ V, and the time characteristic (for example, delay) is 100 ms.
  • the step may perform a process such as concentration conversion, filtering, data segmentation, artifact removal, and superimposition averaging on the cerebral blood oxygen signal to extract a blood oxygen response function signal (HRF).
  • a process such as concentration conversion, filtering, data segmentation, artifact removal, and superimposition averaging on the cerebral blood oxygen signal to extract a blood oxygen response function signal (HRF).
  • HRF blood oxygen response function signal
  • the above Concentration calculation may refer to a concentration change signal that converts a raw voltage signal into HbO, HbR, and HbT according to Modified Beer-Lambert Law (MBLL).
  • MBLL Modified Beer-Lambert Law
  • the above filtering may refer to the elimination of noise signals that are not related to the task, such as high frequency instrument noise and low frequency physiological noise (eg, breathing, heartbeat, Mayer wave, etc.).
  • high frequency instrument noise and low frequency physiological noise eg, breathing, heartbeat, Mayer wave, etc.
  • the above data segmentation may refer to dividing the data into segments according to the time stamp of the event occurrence.
  • the above artifact removal refers to eliminating data segments where there are large motion artifacts.
  • the above block average refers to combining the processed data segments to obtain a blood oxygen response function (HRF).
  • HRF blood oxygen response function
  • Fig. 3 exemplarily shows a schematic diagram of a blood oxygenation response function. It mainly includes rising section, continuous section and descending section; wherein ⁇ C represents the change of blood oxygen concentration, PA represents the peak amplitude, PT represents the peak time, RD represents the rising delay time, FWHM represents the half width, and Peak represents the blood oxygen response.
  • the peak value, Onset represents the starting point of stimulation, and Stimulation represents the length of stimulation time.
  • the amplitude characteristic of the blood oxygen response function can be: peak amplitude (PA).
  • the temporal characteristics of the blood oxygen response function can be, for example:
  • RD rising delay
  • FWHM full-width-at-half-maximum
  • This step analyzes the effect of the temporal characteristics (ie, latency) of the ERP component on the blood oxygen response (eg, amplitude of blood oxygen response, rise delay time, peak time, and half-width), and explores the intrinsic relationship between the two modal signals. contact.
  • temporal characteristics ie, latency
  • blood oxygen response eg, amplitude of blood oxygen response, rise delay time, peak time, and half-width
  • the step may include:
  • S152 Analyze the relationship between the time characteristic of the individual components of ERP (Latency) and the rise delay time (RD), peak time (PT) and half-width (FWHM) of HbO/HbR of blood oxygen response, and obtain a coupling relationship. .
  • the embodiment of the invention investigates the influence of the temporal characteristics (latency period) of the EEG evoked potential signal on the peak amplitude, peak time, rise delay time and half-width of the activated blood region, and describes the neuron power from the time characteristics.
  • the visual cortex has unique structural and functional features.
  • the present invention will be described in detail below by taking a classic visual black and white checkerboard inversion experiment as an example.
  • the preferred embodiment is based on a visual black and white checkerboard inversion experiment with different contrasts, using the time characteristics of the ERP individual components (Latency) and the HbO amplitude characteristics (PA) of the blood oxygen response to illustrate the process of coupling analysis.
  • the experimental paradigm of 7 blocks (7Blocks) is used. Each chunk contains 25 seconds of checkerboard inversion stimulation and a 30 second recovery period (Resting), three contrast (1%, 10%, and 100%) visual stimuli appear in random order, see Figure 4a, Baseline indicates the baseline, Stimulation indicates the stimulation period, Resting indicates the recovery period, and Reversal indicates the black and white checkerboard reversal stimulus.
  • the near-infrared photoelectrode and the electroencephalogram electrode are arranged in the manner shown in FIG. 4b, wherein the black and gray circles respectively represent a near-infrared source (Source) and a detector (Detector); the hexagonal representation represents an electroencephalogram electrode (Electrode); The line represents the light channel.
  • S2 Performing filtering, downsampling, removing EO component, detrending, data segmentation, removing artifacts, and superimposing averaging on the collected EEG signals to obtain VEP under different contrasts, as shown in FIG. 5a. .
  • the horizontal axis represents time and the vertical axis represents voltage.
  • Figure 5b exemplarily shows the relationship between VEP delay and Contrast, where P1 represents the first positive peak in the VEP waveform and N1 represents the first negative peak in the VEP waveform.
  • Fig. 6b exemplarily shows the relationship between HRF activation intensity and contrast (Contrast).
  • Fig. 7 exemplarily shows a coupling relationship curve obtained by performing joint analysis of the EEG signal and the features extracted by the fNIRS signal.
  • ⁇ C represents a change in blood oxygen concentration.
  • the photoelectric synchronous brain activity acquisition system synchronously acquires the EEG signal and the fNIRS signal, and then can simultaneously perform data preprocessing of the EEG signal and the fNIRS signal, simultaneously perform feature extraction of the EEG signal and the fNIRS signal, and finally perform coupling analysis.
  • the neuron typically causes a large response in a shorter period of time. That is, as the contrast increases (stimulus enhancement), the neuron activity intensity increases and the required synchronization time decreases.
  • the neuronal activity requires a large amount of energy, the supply of energy is mainly delivered through the bloodstream. Therefore, for a strong stimulus, the neuron needs to reach a synchronous discharge in a short period of time, which requires more energy supply, resulting in a larger activation response of the blood oxygenation.
  • This time-based mechanism (the shorter the neuronal activity delay time, the greater the amplitude of the blood oxygen response) and the traditional activation-based mechanism (strong neuronal activity) The higher the degree, the greater the level of blood oxygen response caused) and does not conflict. More importantly, this time-based feature provides more informative information for a more comprehensive and in-depth study and interpretation of the relationship between neuronal electrical activity and blood oxygen response.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Surgery (AREA)
  • Public Health (AREA)
  • Pathology (AREA)
  • Veterinary Medicine (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Psychiatry (AREA)
  • Signal Processing (AREA)
  • Physiology (AREA)
  • Psychology (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Hematology (AREA)
  • Cardiology (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

一种基于脑电与功能近红外光谱技术的神经血管耦合分析方法。其中，该方法包括采集脑电信号与脑血氧信号（S100）；从脑电信号中提取事件诱发电位信号（S110）；提取事件诱发电位信号的时间特征（S120）；从脑血氧信号中提取血氧响应函数信号（S130）；提取血氧响应函数信号的幅值特征与时间特征（S140）；分析事件诱发电位信号的时间特征对血氧响应函数信号的幅值特征与时间特征的影响，得到耦合结果（S150）。本方法从时间特征方面描述神经元电活动与血氧响应之间的关系，克服了传统神经血管耦合分析中，缺乏对神经元电活动与血氧响应时间特征研究的不足。

Description

基于脑电与功能近红外光谱技术的神经血管耦合分析方法 技术领域
本发明涉及脑电与功能近红外光谱技术领域，具体而言涉及一种基于脑电与功能近红外光谱技术的神经血管耦合分析方法。
背景技术
脑功能活动包括神经元活动和局部能量代谢等多个过程，复杂的功能活动使得大脑汇集了多个模态的信息，其中最为重要的是神经元的电活动和激活区域的血氧代谢变化，两者之间的关系称为神经血管耦合(Neurovascular Coupling)。该复杂的耦合过程需要神经元、星形胶质细胞和毛细血管之间的一系列活动。然而，目前在神经科学研究中采用血氧变化推测神经元活动的基础尚不明确，这在一定程度上影响了对神经元活动的判断。
采用多模态脑功能成像融合技术研究神经血管耦合是近年来研究的热点之一。虽然功能磁共振(fMRI)的空间分辨率较高，可以与时间分辨率较高的脑电技术(EEG)结合形成优势互补，但由于它的磁场信号和EEG的电信号形成强烈的噪声干扰，此外fMRI对被试和环境有较为苛刻的要求，加上它的设备沉重、价格昂贵，一定程度上限制了EEG-fMRI融合技术的发展。而功能近红外光谱技术(fNIRS)是近年来发展起来的新技术，与fMRI相比，fNIRS不仅能够获取脱氧血红蛋白(HbR)的变化信息，而且还能获取含氧血红蛋白(HbO)以及总血红蛋白(HbT＝HbO+HbR)的变化信息。fNIRS的光信号和EEG的电信号几乎没有任何干扰，加上它和EEG设备的低成本、便携性、无创、操作方便，受到了很多研究者的青睐。将EEG和fNIRS技术相结合，实现对神经元的电活动和激活区的血氧代谢信息的同步提取，对开展同步性要求较高的神经血管耦合的研究，可提供积极的技术支持，从而为深入研究大脑神经元活动的机理提供重要的途径。这可以从功能区入手，建立血氧信号与神经元活动的对应关系；在此基础上，研究不同脑区之间耦合的差异性，对差异进行量化研究。然而，这方面的研究主要是基于EEG与fNIRS的幅值特征(激活强度)来展开的。
有鉴于此，特提出本发明。
发明内容
为了克服传统神经血管耦合分析中，缺乏对神经元电活动与血氧响应时间特征研究的不足，本发明提供一种基于脑电与功能近红外光谱技术的神经血管耦合分析方法。
为了实现上述目的，提供以下技术方案：
一种基于脑电与功能近红外光谱技术的神经血管耦合分析方法，其特征在于，所述方法包括：
采集脑电信号与脑血氧信号；
从所述脑电信号中提取事件诱发电位信号；
提取所述事件诱发电位信号的时间特征；
从所述脑血氧信号中提取血氧响应函数信号；
提取所述血氧响应函数信号的幅值特征与时间特征；
分析所述事件诱发电位信号的时间特征对所述血氧响应函数信号的幅值特征与时间特征的影响，得到耦合结果。
进一步地，所述采集脑电信号与脑血氧信号具体包括：
利用光电同步脑活动检测系统同步采集所述脑电信号与所述脑血氧信号。
进一步地，从所述脑电信号中提取事件诱发电位信号具体包括：
对所述脑电信号滤波、去除眼电干扰成分、去趋势、数据分段、移除伪迹及叠加平均处理，得到所述事件诱发电位信号。
进一步地，从所述脑电信号中提取事件诱发电位信号还包括：降采样处理。
进一步地，提取所述事件诱发电位信号的时间特征具体包括：
利用模式反转视觉刺激的方法提取所述事件诱发电位信号的所述时间特征。
进一步地，所述时间特征为延迟。
进一步地，从所述脑血氧信号中提取血氧响应函数信号具体包括：
对所述脑血氧信号进行浓度转换、滤波、数据分段、伪迹移除以及叠加平均处理，来提取所述血氧响应函数信号。
进一步地，所述血氧响应函数信号的幅值特征为峰值幅度；所述血氧响应函数信号的时间特征为上升延迟时间、峰值时间、以及半峰宽。
进一步地，分析所述事件诱发电位信号的时间特征对所述血氧响应函数信号的幅值特征与时间特征的影响，得到耦合结果，具体包括：
分析所述事件诱发电位信号单个成分的所述时间特征与所述血氧响应函数信号的峰值幅度之间的关系，并得到耦合关系；
分析所述事件诱发电位信号单个成分的所述时间特征分别与所述血氧响应函数信号的上升延迟时间、峰值时间和半峰宽之间的关系，并得到耦合关系。
本发明提供一种基于脑电与功能近红外光谱技术的神经血管耦合分析方法。其中，该方法包括同步采集脑电信号与脑血氧信号；从脑电信号中提取事件诱发电位信号；提取事件诱发电位信号的时间特征；从脑血氧信号中提取血氧响应函数信号；提取血氧响应函数信号的幅值特征与时间特征；分析事件诱发电位信号的时间特征对血氧响应函数信号的幅值特征与时间特征的影响，得到耦合结果。本发明实施例利用事件诱发电位信号的时间特征对血氧响应函数信号的幅值特征与时间特征的影响，从时间特征方面描述神经元电活动与血氧响应之间的关系，克服了传统的仅基于幅值角度分析二者关系的局限性，可以通过同步采集神经元电活动与激活脑区血氧代谢变化，辅助分析脑功能活动，可用于许多重大神经精神疾病的临床研究，其包括癫痫、儿童等特殊群体的临床研究。
附图说明
图1为根据本发明实施例的基于脑电与功能近红外光谱技术的神经血管耦合分析方法的流程示意图；
图2为根据本发明实施例的视觉模式反转实验对应的视觉诱发电位的示意图；
图3为根据本发明实施例的血氧响应函数示意图；
图4a为根据本发明实施例的视觉黑白棋盘格反转实验的实验范式(25秒棋盘格翻转刺激以及30秒恢复期)示意图；
图4b为根据本发明实施例的视觉黑白棋盘格反转实验中近红外光极与脑电电极排布示意图；
图5a为根据本发明实施例的不同对比度下的VEP波形示意图；
图5b为根据本发明实施例的VEP延迟与对比度的关系示意图；
图6a为根据本发明实施例的不同对比度下的HRF波形示意图；
图6b为根据本发明实施例的HRF激活强度与对比度的关系示意图；
图7为根据本发明实施例的神经血管耦合关系示意图；
图8为根据本发明另一实施例的基于脑电与功能近红外光谱技术的神经血管耦合分析方法的流程示意图。
具体实施方式
下面参照附图来描述本发明的优选实施方式。本领域技术人员应当理解的是，这些实施方式仅仅用于解释本发明的技术原理，并非旨在限制本发明的保护范围。
本发明实施例的基本思想是基于光电同步脑活动检测系统，通过脑电技术与功能近红外技术同步采集同一脑区神经元电活动以及血氧代谢变化，并利用光和电这两种模态数据，从时间特征的角度进行脑电与脑血氧双模态融合的神经血管耦合分析。
这里，从时间特征方面描述神经元电活动与血氧响应之间的关系，即神经血管耦合关系。
本发明实施例提供一种基于脑电与功能近红外光谱技术的神经血管耦合分析方法，如图1所示，该方法包括：
S100：采集脑电信号与脑血氧信号。
为了保证信号同步性，本步骤可以在同一时刻采集脑电信号与脑血氧信号。
在实际应用中，可以利用光电同步脑活动检测系统来同步采集脑电信号与脑血氧信号。通过光电同步脑活动检测系统对被试者进行脑电信号与脑血氧信号的多路同步采集，针对配置好的采集位置，在同一时刻采集所有配置通道的脑电信号与脑血氧信号，这样可以保证了配置通道间的信号同步性。
S110：从脑电信号中提取事件诱发电位信号。
具体地，本步骤可以对采集到的脑电信号(EEG)进行诸如滤波、降采样、去除眼电干扰成分、去趋势、数据分段、移除伪迹以及叠加平均等处理，来提取事件诱发电位信号(ERP)。
优选地，滤波可以为带通滤波，以提取EEG信号的感兴趣波段，如：1-100Hz或提取EEG信号的节律成分，如Delta节律(<4Hz)、Theta节律(4-7Hz)、Alpha节律(8-15Hz)、Beta节律(16-30Hz)等。
需要说明的是，在上述步骤中，考虑到采样频率太高、采集时间太长时会导致数据量过大，可根据实际需要进行降采样处理。
上述去除眼电干扰成分步骤可以是指基于独立成分分析方法(Independent Component Analysis，ICA)去除与眼电信号相关的成分(例如：去除受眼电信号干扰的成分)。
上述去趋势可以指的是考虑到信号的直流成分，通过去趋势处理来消除漂移。
上述数据分段处理可以是指按照事件发生的时间标记，将数据分为若干段。
上述伪迹移除可以是指去除存在较大运动伪迹的数据分段。优选地，可以采用如下方法来处理：设定某个阈值，当EEG信号的幅值超出该阈值时，则在后续的叠加平均中不计入该数据分段。
上述叠加平均处理可以是指将已处理好的数据分段综合起来，叠加平均提取ERP信号。
S120：提取事件诱发电位信号的时间特征。
视觉诱发电位(VEP)是大脑皮层枕叶区域受到视觉刺激而产生的电活动，是代表视网膜接收刺激，经视通路传导至枕叶皮层而引起的电位变化。VEP具有两个非常重要的指标，即幅值特征(幅值)和延迟特征(延迟，其视为时间特征)。幅值表示到达视觉皮层的信息量的多少；而延迟表示从视网膜接收视觉刺激并传导至视觉皮层所需要的 时间。由于VEP能够提供视觉系统功能完整性的重要临床诊断信息。1970年，VEP被用来进行婴幼儿以及儿童的视力评估。此后，越来越多的研究者将VEP应用于临床，通过测量视觉刺激引起的各个成分的幅值和延迟，进行视觉通路完整性的评价，以及视觉相关疾病的诊断等方面。
随着脑科学研究的不断发展，采用多模态脑功能成像融合技术研究神经血管耦合是近年来研究的热点之一。由于视觉皮层具有独特的结构和功能特征，VEP也常常被用来研究视觉皮层的神经血管耦合关系，这方面的研究主要是基于VEP的幅值而展开的，重点研究了VEP幅值与血氧响应之间的关系。然而，VEP时间特征(潜伏期)对血氧响应的影响却知之甚少，目前还未见将VEP延迟作为一个指标来研究及与血氧响应关系的相关报道。许多基于不同对比度视觉刺激的研究发现：VEP幅值主要反映刺激的类型及形态信息，而VEP延迟主要反映刺激的对比度及亮度信息；一些视觉信息被编码在VEP延迟中，也就是说，视觉刺激的对比度及亮度主要编码在延迟信息中，而刺激的类型及形态则主要编码在幅值信息中；时间编码(时间特征)主要用于判别微小的对比度差别，而神经元放电率(幅值特征)则主要用于进行总体差异的判别。由此可见，VEP的延迟具有和幅值同样的重要性，在进行神经血管耦合相关的分析中，将VEP延迟作为一个指标具有非常重要的价值。
获取事件诱发电位信号幅值特征和时间特征的方法有很多种，作为示例，本步骤可以利用模式反转视觉刺激的方法来获取事件诱发电位信号的幅值特征和时间特征。
图2示例性地示出了视觉模式反转实验对应的视觉诱发电位的示意图。其中，横轴表示时间(latency)，纵轴表示幅值(amplitude)。图2主要呈现了出现在75ms左右的第一个的负峰(N75)，出现在100ms左右的第一个正峰(P100)，出现在135ms左右的第二个负峰(N135)。
本发明实施例在幅值特征和时间特征的提取过程中，例如可以通过VEP的波峰和波谷来提取。其中，每一个极值代表一个成分。以图2所示为例，对于第二个出现在100ms时刻的成分P100，这个成分同时包含两个主要的特征，即幅值特征约7.5μV，时间特征(例如：延迟)为100ms。
S130：从脑血氧信号中提取血氧响应函数信号。
具体地，在一个优选的实施例中本步骤可以对脑血氧信号进行诸如浓度转换、滤波、数据分段、伪迹移除以及叠加平均等处理，来提取血氧响应函数信号(HRF)。
上述浓度转换(Concentration calculation)可以是指根据比尔朗勃定律(Modified Beer-Lambert law,MBLL)将原始电压信号转换为HbO、HbR以及HbT的浓度变化信号。
上述滤波可以是指消除与任务不相关的噪声信号，如高频仪器噪声以及低频生理噪声(例如：呼吸、心跳、Mayer波等)。
上述数据分段可以是指按照事件发生的时间标记，将数据分为若干段。
上述伪迹移除是指消除存在较大的运动伪迹的数据分段。
上述叠加平均(Block average)是指将已处理好的数据分段综合起来，获取血氧响应函数(HRF)。
S140：提取血氧响应函数信号的幅值特征与时间特征。
图3示例性地示出了血氧响应函数示意图。其中主要包含上升段、持续段和下降段；其中，△C表示血氧浓度的变化，PA表示峰值幅度，PT表示峰值时间，RD表示上升延迟时间，FWHM表示半峰宽，Peak表示血氧响应的峰值，Onset表示刺激起始点，Stimulation表示刺激时间长度。
血氧响应函数的幅值特征可以为：峰值幅度(peak amplitude，PA)。
血氧响应函数的时间特征例如可以为：
a)上升延迟时间(rising delay,RD)，即从刺激开始时刻到响应曲线上升段最大值的10％所需的时间；
b)峰值时间(peak time,PT)，即从刺激起始时刻到响应曲线最大值所需的时间；
c)半峰宽(full-width-at-half-maximum,FWHM)，即响应曲线上升段最大值的50％到下降段最大值的50％持续的时间。
S150：分析事件诱发电位信号的时间特征对血氧响应函数信号的幅值特征与时间特征的影响，得到耦合结果。
本步骤分析ERP成分的时间特征(即潜伏期)对血氧响应的影响(例如：血氧响应的幅值、上升延迟时间、峰值时间以及半峰宽)，挖掘两种模态信号之间的内在联系。
具体地，在一个优选的实施例中本步骤可以包括：
S151：分析ERP单个成分的时间特征(Latency)与血氧响应的HbO/HbR的峰值幅度(PA)之间的关系，并得到耦合关系。
S152：分析ERP单个成分的时间特征(Latency)分别与血氧响应的HbO/HbR的上升延迟时间(RD)、峰值时间(PT)和半峰宽(FWHM)之间的关系，并得到耦合关系。
本发明实施例考察脑电诱发电位信号的时间特征(潜伏期)对激活脑区血氧响应的峰值幅度、峰值时间、上升延迟时间以及半峰宽等参数的影响，从时间特征方面描述神经元电活动与血氧响应之间的关系。
下面结合图4a至图7以一优选实施例来详细说明本发明。
视觉皮层具有独特的结构和功能特征，下面将以一个经典的视觉黑白棋盘格反转实验为例来详细说明本发明。
本优选实施例基于不同对比度的视觉黑白棋盘格反转实验，采用ERP单个成分的时间特征(Latency)与血氧响应的HbO幅值特征(PA)来说明耦合分析的过程。其中采用7个组块(7Blocks)的实验范式。每个组块包含25秒棋盘格反转刺激(Stimulation)以及30秒恢复期(Resting)，三种对比度(1％，10％，以及100％)视觉刺激以随机顺序出现，参见图4a，其中Baseline表示基线，Stimulation表示刺激期，Resting表示恢复期，Reversal表示黑白棋盘格反转刺激。近红外光极与脑电电极按照图4b所示方式排布，其中黑色、灰色圆圈分别表示近红外光源(Source)与探测器(Detector)；六边形表示脑电电极(Electrode)；黑色实线表示光通道。
S1：采集EEG信号和fNIRS信号。
S2：对采集到的EEG信号进行滤波、降采样、去除眼电干扰成分、去趋势、数据分段、移除伪迹以及叠加平均等预处理，得到不同对比度下的VEP，如图5a所示。图5a中的横轴表示时间，纵轴表示电压。
S3：根据VEP信号进行延迟特征提取。
图5b示例性地示出了VEP延迟与对比度(Contrast)的关系，其中P1表示VEP波形中第一个正峰，N1表示VEP波形中第一个负峰。
S4：对采集到的fNIRS信号进行浓度转换、滤波、数据分段、伪迹移除、以及叠加平均等预处理，得到不同对比度下的血氧响应函数HRF，如图6a所示，其中△C表示血氧浓度的变化，实线表示含氧血红蛋白(HbO)的浓度变化，虚线表示脱氧血红蛋白(HbR)的浓度变化。
S5：根据HRF进行幅值特征提取。
图6b示例性地示出了HRF激活强度与对比度(Contrast)的关系。
S6：将EEG信号与fNIRS信号提取的特征做联合分析，得到耦合关系。
图7示例性地示出了将EEG信号与fNIRS信号提取的特征做联合分析所得到的耦合关系曲线。其中△C表示血氧浓度的变化。
这里需要说明的是，上述实施例中虽然将各个步骤按照上述先后次序的方式进行了描述，但是本领域技术人员可以理解，为了实现本实施例的效果，不同的步骤之间不必按照这样的次序执行，其可以同时(并行)执行或以颠倒的次序执行，这些简单的变化都在本发明的保护范围之内。例如：如图8所示，对脑电信号的处理和对血氧响应的处理流程可以是并列的关系，二者之间没有先后顺序和级联关系。在图8中，光电同步脑活动采集系统同步采集EEG信号和fNIRS信号，之后可以同时进行EEG信号和fNIRS信号的数据预处理，同时进行EEG信号和fNIRS信号特征提取，最后进行耦合分析。
通过上述实施例可知：对于一个很强的刺激(如本例中100％对比度的刺激)，神经元通常能在较短的时间内引起较大的响应。也就是说，随着对比度的增加(刺激增强)，神经元活动强度增加且所需同步时间降低。此外，由于神经元活动需要消耗大量的能量，而能量的供应主要是通过血流来输送的。因此，对于一个强刺激，神经元需要在很短的时间内达到同步放电，就需要更多的能量供应，从而引起较大的血氧响应激活幅度。这个基于时间特征的机理(神经元活动延迟时间越短，引起的血氧响应幅度越大)与传统基于激活强度的机理(神经元活动强 度越高，引起的血氧响应幅度越大)并不冲突。更为重要的是，这个基于时间特征的机理，为更加全面深入的研究和解释神经元电活动与血氧响应之间的关系提供了更加丰富的信息。
至此，已经结合附图所示的优选实施方式描述了本发明的技术方案，但是，本领域技术人员容易理解的是，本发明的保护范围显然不局限于这些具体实施方式。在不偏离本发明的原理的前提下，本领域技术人员可以对相关技术特征作出等同的更改或替换，这些更改或替换之后的技术方案都将落入本发明的保护范围之内。

Claims (9)

  1. 一种基于脑电与功能近红外光谱技术的神经血管耦合分析方法，其特征在于，所述方法包括：
    采集脑电信号与脑血氧信号；
    从所述脑电信号中提取事件诱发电位信号；
    提取所述事件诱发电位信号的时间特征；
    从所述脑血氧信号中提取血氧响应函数信号；
    提取所述血氧响应函数信号的幅值特征与时间特征；
    分析所述事件诱发电位信号的时间特征对所述血氧响应函数信号的幅值特征与时间特征的影响，得到耦合结果。
  2. 根据权利要求1所述的方法，其特征在于，所述采集脑电信号与脑血氧信号具体包括：
    利用光电同步脑活动检测系统同步采集所述脑电信号与所述脑血氧信号。
  3. 根据权利要求1所述的方法，其特征在于，所述从所述脑电信号中提取事件诱发电位信号具体包括：
    对所述脑电信号滤波、去除眼电干扰成分、去趋势、数据分段、移除伪迹及叠加平均处理，得到所述事件诱发电位信号。
  4. 根据权利要求3所述的方法，其特征在于，所述从所述脑电信号中提取事件诱发电位信号还包括：降采样处理。
  5. 根据权利要求1所述的方法，其特征在于，所述提取所述事件诱发电位信号的时间特征具体包括：
    利用模式反转视觉刺激的方法提取所述事件诱发电位信号的所述时间特征。
  6. 根据权利要求1至5中任一所述的方法，其特征在于，所述事件诱发电位信号的所述时间特征为延迟。
  7. 根据权利要求1所述的方法，其特征在于，所述从所述脑血氧信号中提取血氧响应函数信号具体包括：
    对所述脑血氧信号进行浓度转换、滤波、数据分段、伪迹移除以及叠加平均处理，来提取所述血氧响应函数信号。
  8. 根据权利要求1所述的方法，其特征在于，所述血氧响应函数信 号的幅值特征为峰值幅度；所述血氧响应函数信号的时间特征为上升延迟时间、峰值时间、以及半峰宽。
  9. 根据权利要求8所述的方法，其特征在于，所述分析所述事件诱发电位信号的时间特征对所述血氧响应函数信号的幅值特征与时间特征的影响，得到耦合结果，具体包括：
    分析所述事件诱发电位信号单个成分的所述时间特征与所述血氧响应函数信号的峰值幅度之间的关系，并得到耦合关系；
    分析所述事件诱发电位信号单个成分的所述时间特征分别与所述血氧响应函数信号的上升延迟时间、峰值时间和半峰宽之间的关系，并得到耦合关系。
PCT/CN2016/104455 2016-11-03 2016-11-03 基于脑电与功能近红外光谱技术的神经血管耦合分析方法 WO2018081980A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/CN2016/104455 WO2018081980A1 (zh) 2016-11-03 2016-11-03 基于脑电与功能近红外光谱技术的神经血管耦合分析方法
US16/961,700 US11944447B2 (en) 2016-11-03 2016-11-03 Neurovascular coupling analytical method based on electroencephalogram and functional near-infrared spectroscopy

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2016/104455 WO2018081980A1 (zh) 2016-11-03 2016-11-03 基于脑电与功能近红外光谱技术的神经血管耦合分析方法

Publications (1)

Publication Number Publication Date
WO2018081980A1 true WO2018081980A1 (zh) 2018-05-11

Family

ID=62076462

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/104455 WO2018081980A1 (zh) 2016-11-03 2016-11-03 基于脑电与功能近红外光谱技术的神经血管耦合分析方法

Country Status (2)

Country Link
US (1) US11944447B2 (zh)
WO (1) WO2018081980A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112274144A (zh) * 2019-07-22 2021-01-29 苏州布芮恩智能科技有限公司 近红外脑功能成像数据的处理方法、装置和存储介质
CN113040790A (zh) * 2021-03-29 2021-06-29 中国科学院自动化研究所 基于内源性脑信号的闭环神经调控系统、方法和设备

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115969390A (zh) * 2021-10-15 2023-04-18 中国科学院沈阳自动化研究所 一种基于深度置信网络的非完整运动想象脑电解码方法
CN115999069B (zh) * 2022-12-08 2024-01-05 北京师范大学珠海校区 经颅光刺激的参数确定装置和设备

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101853070A (zh) * 2010-05-13 2010-10-06 天津大学 前额脑电与血氧信息融合的人机交互装置
CN102284137A (zh) * 2011-05-20 2011-12-21 天津大学 一种功能性电刺激的多源信息融合控制方法
CN102715902A (zh) * 2012-06-15 2012-10-10 天津大学 特殊人群的情绪监护方法
US20150310750A1 (en) * 2012-12-03 2015-10-29 Klaus Glaunsinger Method for verifying the validity of reactions of a person
CN105816170A (zh) * 2016-05-10 2016-08-03 广东省医疗器械研究所 基于可穿戴式nirs-eeg的精神分裂症早期检测评估系统
CN106580248A (zh) * 2016-11-03 2017-04-26 中国科学院自动化研究所 基于脑电与功能近红外光谱技术的神经血管耦合分析方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101853070A (zh) * 2010-05-13 2010-10-06 天津大学 前额脑电与血氧信息融合的人机交互装置
CN102284137A (zh) * 2011-05-20 2011-12-21 天津大学 一种功能性电刺激的多源信息融合控制方法
CN102715902A (zh) * 2012-06-15 2012-10-10 天津大学 特殊人群的情绪监护方法
US20150310750A1 (en) * 2012-12-03 2015-10-29 Klaus Glaunsinger Method for verifying the validity of reactions of a person
CN105816170A (zh) * 2016-05-10 2016-08-03 广东省医疗器械研究所 基于可穿戴式nirs-eeg的精神分裂症早期检测评估系统
CN106580248A (zh) * 2016-11-03 2017-04-26 中国科学院自动化研究所 基于脑电与功能近红外光谱技术的神经血管耦合分析方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
SUN, JINYAN: "Attention Orienting and Executive Control Respectively Studied with Event-Related Electroencephalography and a Multi-Modality Optical-Electrophysiology Method", MEDICINE & PUBLIC HEALTH, CHINA DOCTORAL DISSERTATIONS FULL-TEXT DATABASE, 15 December 2014 (2014-12-15), pages 60 - 87, ISSN: 1674-022X *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112274144A (zh) * 2019-07-22 2021-01-29 苏州布芮恩智能科技有限公司 近红外脑功能成像数据的处理方法、装置和存储介质
CN113040790A (zh) * 2021-03-29 2021-06-29 中国科学院自动化研究所 基于内源性脑信号的闭环神经调控系统、方法和设备
CN113040790B (zh) * 2021-03-29 2022-01-28 中国科学院自动化研究所 基于内源性脑信号的闭环神经调控系统、方法和设备

Also Published As

Publication number Publication date
US11944447B2 (en) 2024-04-02
US20210282694A1 (en) 2021-09-16

Similar Documents

Publication Publication Date Title
US20210052182A1 (en) Portable brain activity sensing platform for assessment of visual field deficits
Kim et al. Diagnostic utility of quantitative EEG in un-medicated schizophrenia
Catarino et al. Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis
US7570991B2 (en) Method for real time attitude assessment
CN104755026B (zh) 用于显示存在于eeg记录中的伪影量的方法和系统
Berthouze et al. Human EEG shows long-range temporal correlations of oscillation amplitude in Theta, Alpha and Beta bands across a wide age range
CN106580248B (zh) 基于脑电与功能近红外光谱技术的神经血管耦合分析方法
Milne et al. Visual search performance is predicted by the degree to which selective attention to features modulates the ERP between 350 and 600 ms
WO2018081980A1 (zh) 基于脑电与功能近红外光谱技术的神经血管耦合分析方法
Goodale et al. fMRI-based detection of alertness predicts behavioral response variability
Leite et al. Transfer function between EEG and BOLD signals of epileptic activity
Mayeli et al. Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI
Sergeev et al. Wavelet skeletons in sleep EEG-monitoring as biomarkers of early diagnostics of mild cognitive impairment
CN113288174B (zh) 一种精神分裂患者认知功能的检测方法
Gu et al. An orderly sequence of autonomic and neural events at transient arousal changes
Mahmoodin et al. An analysis of EEG signal power spectrum density generated during writing in children with dyslexia
Bari et al. From neurovascular coupling to neurovascular cascade: a study on neural, autonomic and vascular transients in attention
Manoochehri et al. Cortical light scattering during interictal epileptic spikes in frontal lobe epilepsy in children: a fast optical signal and electroencephalographic study
Unde et al. PSD based coherence analysis of EEG signals for stroop task
Van de Wassenberg et al. Multichannel recording of median nerve somatosensory evoked potentials
Poza et al. Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using Jensen's divergence
Arrubla et al. Methods for pulse artefact reduction: experiences with EEG data recorded at 9.4 T static magnetic field
KR20180099984A (ko) 다중 신경생리신호 기반 사용자 간 상호작용 모니터링 장치 및 방법
Hallez et al. Muscle and eye movement artifact removal prior to EEG source localization
Keles et al. Multimodality mapping approach for evolving functional brain connectivity patterns: A fNIRS-EEG study

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16920551

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 16920551

Country of ref document: EP

Kind code of ref document: A1