CN116531661A - Closed-loop electric stimulation system based on brain electric network guidance - Google Patents

Closed-loop electric stimulation system based on brain electric network guidance Download PDF

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CN116531661A
CN116531661A CN202310490412.7A CN202310490412A CN116531661A CN 116531661 A CN116531661 A CN 116531661A CN 202310490412 A CN202310490412 A CN 202310490412A CN 116531661 A CN116531661 A CN 116531661A
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network
signals
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electrical
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卜俊杰
刘家芳
袁野
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Anhui Medical University
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Abstract

The invention relates to a closed-loop electrical stimulation system based on brain electrical network guidance, which comprises: an electroencephalogram signal acquisition and preprocessing module: the brain electrical activity detection device is used for collecting brain electrical signals of a brain network of a subject and brain electrical activity modes thereof, and preprocessing the brain electrical activity signals to obtain preprocessed brain electrical signals; the brain electric network construction and analysis module: the method is used for extracting the characteristics of the preprocessed brain electrical signals, constructing and analyzing brain electrical networks, and screening out indexes capable of clearly distinguishing the activity states of different brain electrical networks; outputting a brain network index value; closed loop electrical stimulation module: the method is used for continuously detecting the brain network index value, and when the brain network index value is detected to be abnormal, the electrical stimulation is started, and otherwise, the electrical stimulation is stopped. The system provided by the invention can accurately regulate the activity state of the brain network and improve the personalized treatment effect.

Description

Closed-loop electric stimulation system based on brain electric network guidance
Technical Field
The invention relates to the fields of cognitive neuroscience and biomedical engineering, in particular to a closed-loop electric stimulation system based on brain network guidance.
Background
Phase-phase interferometry electrical stimulation (Temporal Interference Stimulation, TI) is a novel non-invasive brain nerve regulation technique, and the basic principle is that an interference pattern is formed in a specific space range by utilizing the time-phase difference of two high-frequency alternating electric fields, so that a local electric field is generated in the brain, and the accurate activation of neurons is realized. Studies have shown that phase-phase interferential electrical stimulation can be used to regulate brain electrical activity, improving cognitive and motor abilities of patients. Meanwhile, the TI technology achieves a certain achievement in the aspects of nerve regulation, brain network function improvement, cognition enhancement, clinical treatment and the like. The background of the TI technology can be traced back to 2005, and a researcher Karl deisseoth et al firstly proposes a optogenetic regulation method based on time domain control, namely Optogenetics, which enables the researcher to accurately control the activities of neurons through light control. Thereafter, researchers began exploring whether there is a non-invasive cranial nerve modulation technique similar to Optogenetics, and TI techniques were proposed. With the development of the technology, the TI technology has been widely focused and studied, and becomes one of the hot research directions in the fields of cerebral neuroscience and nerve engineering.
The core technology in traditional closed-loop electrical stimulation is a machine learning-based neural feedback technology, and Neural Feedback (NF) is a non-invasive self-brain training technology. It is usually necessary to train a large amount of sample data to build a model such as a classifier or a regressor, and then output a corresponding stimulation command through the model according to the current electroencephalogram signal input. The disadvantage of this approach is that it requires a lot of offline training and has limited generalization capability between different subjects.
Disclosure of Invention
In order to solve the technical problems, the invention provides a closed-loop electric stimulation system based on brain network guidance.
The technical scheme of the invention is as follows: a closed loop electrical stimulation system based on brain network guidance, comprising:
an electroencephalogram signal acquisition and preprocessing module: the brain electrical activity detection method comprises the steps of acquiring brain electrical signals of a brain network of a subject and brain electrical activity modes of the brain electrical signals, and preprocessing the brain electrical activity signals to obtain preprocessed brain electrical signals;
the brain electric network construction and analysis module: the method is used for extracting the characteristics of the preprocessed brain electrical signals, constructing and analyzing brain electrical networks, and screening out indexes capable of clearly distinguishing different brain electrical network activity states; outputting a brain network index value;
closed loop electrical stimulation module: and the method is used for continuously detecting the brain network index value, and when the brain network index value is detected to be abnormal, the electrical stimulation is started, and otherwise, the electrical stimulation is stopped.
Compared with the prior art, the invention has the following advantages:
the invention discloses a closed-loop electric stimulation system based on brain electric network guidance, which can accurately regulate specific brain networks according to information of brain electric signals, avoid stimulating brain networks which do not need to be regulated, and can obtain more accurate treatment effect. Meanwhile, the method has stronger individuation, can analyze the brain electrical signals of each person and provide individuation stimulation modes and parameters, so that the treatment requirements of different persons can be better met.
Drawings
Fig. 1 is a block diagram of a closed-loop electrical stimulation system based on brain network guidance in an embodiment of the invention.
Detailed Description
The invention provides a closed-loop electric stimulation system based on brain network guidance, which can accurately regulate the activity state of brain network and improve personalized treatment effect.
The present invention will be further described in detail below with reference to the accompanying drawings by way of specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
Example 1
As shown in fig. 1, a closed-loop electrical stimulation system based on brain network guidance provided by an embodiment of the present invention includes the following modules:
an electroencephalogram signal acquisition and preprocessing module: the brain electrical activity detection device is used for collecting brain electrical signals of a brain network of a subject and brain electrical activity modes thereof, and preprocessing the brain electrical activity signals to obtain preprocessed brain electrical signals;
the brain electric network construction and analysis module: the method is used for extracting the characteristics of the preprocessed brain electrical signals, constructing and analyzing brain electrical networks, and screening out indexes capable of clearly distinguishing the activity states of different brain electrical networks; outputting a brain network index value;
closed loop electrical stimulation module: the method is used for continuously detecting the brain network index value, and when the brain network index value is detected to be abnormal, the electrical stimulation is started, and otherwise, the electrical stimulation is stopped.
In one embodiment, the electroencephalogram signal acquisition and preprocessing module: the brain electrical activity signal preprocessing method is used for acquiring brain electrical signals of a brain network of a subject and brain electrical activity modes thereof, preprocessing the brain electrical activity signals to obtain preprocessed brain electrical signals, and specifically comprises the following steps:
the acquisition of the electroencephalogram signals in the embodiment of the invention is to record by using a SynAmps RT amplifier provided by a Neuroscan company, and send data to a PC end in real time through a tcp/ip protocol; then preprocessing the electroencephalogram signal data acquired in real time through an EEGLAB kit, and comprising the following steps:
and (3) filtering: the cut-off frequency of a conventional band-pass filter is typically between 0.5 and 100Hz, while the cut-off frequency of a band-stop filter depends on the type of noise that needs to be removed.
Denoising: various noises in the electroencephalogram signals, such as electrode motion artifacts, electromyographic signals, electrooculography signals, and the like, are removed. Common denoising methods include: ICA, wavelet transform, root mean square thresholding, etc.
And (3) signal segmentation: the long-time electroencephalogram signals are divided into a plurality of time periods, so that subsequent analysis is facilitated. Segmentation is typically performed in a fixed time window or event triggered fashion.
Signal calibration: zero calibration and reference electrode calibration are carried out on the electroencephalogram signals, so that signals of all channels have the same reference point and offset, and subsequent analysis and resampling are facilitated: if it is desired to integrate the electroencephalogram signal with other physiological signals, or to synchronize with other data sources, it is often necessary to resample the sampling rate of the electroencephalogram signal.
In one embodiment, the above-mentioned brain network construction and analysis module specifically includes the following steps:
step S1: selecting different channels of the preprocessed electroencephalogram signals, and calculating a coherence matrix or a correlation matrix among the channels to express the interrelation among the electroencephalogram signals so as to construct the electroencephalogram network, wherein the method specifically comprises the following steps of:
calculating a coherence matrix between channels:
according to the embodiment of the invention, four channels C3, C4, P3 and P4 are selected from the acquired electroencephalogram signals, frequency domain feature extraction is firstly carried out, and the power spectral density of each channel on different frequency bands is calculated. Then, the Coherence (Coherence) between the different channels over each frequency band is calculated and a Coherence matrix is constructed, each element in the matrix representing the Coherence between the corresponding electrodes. Next, we can construct an undirected weighted brain network using this matrix. In this network, each channel corresponds to a node, and the coherence value corresponds to the weight of the edges between the nodes. For some coherence values less than the threshold, it may be converted to an edge with a weight of 0, or the edge may be completely removed. The coherence matrix may be used to calculate the coherence between two signals, the calculation formula of which is as follows:
wherein ,Gxx(f) and Gyy (f) The power spectral densities of the two preprocessed electroencephalogram signals are respectively G xy (f) Is the cross power spectral density of the two preprocessed brain electrical signals; the coherence value Coh (f) represents the degree of coherence between the two preprocessed electroencephalograms at the frequency f, and according to the definition of coherence, the coherence value is between 0 and 1,1 represents that the two signals are completely synchronized, 0 represents that the two signals are not presentAny association.
Alternatively, a correlation matrix between channels is calculated:
the embodiment of the invention can also calculate the correlation among four channels of C3, C4, P3 and P4 of the brain electrical signals, and the correlation coefficient (correlation coefficient) is used for measuring. Common correlation coefficients are pearson correlation coefficient (Pearson correlation coefficient), spearman rank correlation coefficient (Spearman's rank correlation coefficient), and the like. The pearson correlation coefficient of the embodiment of the invention is taken as an example, and the preprocessed brain electrical signals of any two channels x and y at n time points are obtained and are respectively x 1 ,x 2 ,...,x n and y1 ,y 2 ,...,y n The pearson correlation coefficient between them can be calculated using the following formula:
wherein ,the average of x and y, respectively, and n is the number of sample points. r is (r) xy The value range of (C) is [ -1,1]When (when) r is (r) xy When the number is positive, the two variables are positively correlated; when r is xy When negative, it means that the two variables are inversely related.
After calculating the correlation coefficients between different channels, the correlation coefficients can be formed into a correlation coefficient matrix, each element r in the matrix xy Representing the correlation coefficient between the x-th channel and the y-th channel. The matrix can be used to represent the correlations between the brain electrical signals, thereby constructing a brain electrical network.
Meanwhile, because a large amount of invalid information exists in the correlation coefficient matrix, effective connection needs to be screened out by setting a threshold value. Common thresholds include global thresholds and local thresholds. The global threshold value is that after the correlation numbers are ordered, the correlation coefficient of the first k% is taken as effective connection; the local threshold value is that after the correlation coefficient of each brain region and other brain regions is ordered, the first k% is taken as effective connection. And constructing the screened effective connection into a brain network. The embodiment of the invention adopts an adjacent matrix representation method, namely, the effective connection is represented as 1, the ineffective connection is represented as 0, and an n multiplied by n adjacent matrix is formed. It should be noted that in calculating the correlation coefficient, analysis using the above-described preprocessed data is required instead of the original electroencephalogram data. The invention does not limit the selection of the electroencephalogram reference, and can adopt different correlation coefficient calculation methods according to specific requirements.
Finally, a metric analysis of the brain network is performed, such as calculating the average degree, average cluster coefficient, average path length, etc. of the network to understand the properties of the brain network.
Step S2: periodically calculating PLV matrix of the brain network, comparing PLV value with preset baseline value, and outputting brain network index value;
PLV＝|1/N*Σexp(i(θ 1 -θ 2 ))|
wherein N represents the length of the preprocessed electroencephalogram signal, and theta 1 and θ2 Respectively representing the phases of the two preprocessed brain electrical signals; i is an imaginary unit.
PLV is an index for analyzing the synchronicity of brain electrical signals: for the electroencephalogram signals of different channels, their PLV values can be calculated by calculating their phase differences. And calculating a PLV matrix at intervals according to the brain electrical signals acquired in real time, and updating the PLV matrix into a closed-loop electrical stimulation system for judging the synchronism condition of the brain electrical network. The normal brain electrical signal data of the test subject are collected in advance, the average value is calculated as the baseline value of the test subject, the PLV value is compared with the baseline value, and the condition that the PLV value is higher or lower than the baseline value is indicated as abnormal condition under different disease conditions.
In one embodiment, the closed loop electrical stimulation module described above: the method is used for continuously detecting the index value of the brain network, and when the index value of the brain network is detected to be abnormal, the electric stimulation is started, otherwise, the electric stimulation is closed, and the method specifically comprises the following steps:
and monitoring the output of the brain network construction and analysis module in real time, and starting electrical stimulation intervention when an abnormal signal, namely, a brain network index value and a brain network index value of a patient under normal conditions are found to be different, wherein the output current form depends on the used electrical stimulator. The electric stimulator adopted by the embodiment of the invention is phase-phase interference electric stimulation, the electric stimulation is started when the abnormal brain activity of the patient is identified, and the stimulus is closed after the normal brain activity state is identified to be restored, so that a closed loop system of detection, stimulation and re-detection is formed.
The invention discloses a closed-loop electric stimulation system based on brain electric network guidance, which can accurately regulate specific brain networks according to information of brain electric signals, avoid stimulating brain networks which do not need to be regulated, and can obtain more accurate treatment effect. Meanwhile, the method has stronger individuation, can analyze the brain electrical signals of each person and provide individuation stimulation modes and parameters, so that the treatment requirements of different persons can be better met.
The above examples are provided for the purpose of describing the present invention only and are not intended to limit the scope of the present invention. The scope of the invention is defined by the appended claims. Various equivalents and modifications that do not depart from the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (3)

1. A closed loop electrical stimulation system based on brain network guidance, comprising:
an electroencephalogram signal acquisition and preprocessing module: the brain electrical activity detection method comprises the steps of acquiring brain electrical signals of a brain network of a subject and brain electrical activity modes of the brain electrical signals, and preprocessing the brain electrical activity signals to obtain preprocessed brain electrical signals;
the brain electric network construction and analysis module: the method is used for extracting the characteristics of the preprocessed brain electrical signals, constructing and analyzing brain electrical networks, and screening out indexes capable of clearly distinguishing different brain electrical network activity states; outputting a brain network index value;
closed loop electrical stimulation module: and the method is used for continuously detecting the brain network index value, and when the brain network index value is detected to be abnormal, the electrical stimulation is started, and otherwise, the electrical stimulation is stopped.
2. The brain network guidance-based closed loop electrical stimulation system according to claim 1, wherein the brain network construction and analysis module comprises the following steps:
step S1: selecting different channels of the preprocessed electroencephalogram signals, and calculating a coherence matrix or a correlation matrix among the channels to express the interrelation among the electroencephalogram signals so as to construct an electroencephalogram network;
step S2: periodically calculating PLV matrix of the brain network, comparing PLV value with preset baseline value, and outputting brain network index value;
PLV＝|1/N*Σexp(i(θ 1 -θ 2 ))|
wherein N represents the length of the preprocessed EEG signal, theta 1 and θ2 Respectively representing the phases of the two preprocessed electroencephalogram signals; i is an imaginary unit.
3. The brain network guidance based closed loop electrical stimulation system according to claim 2, wherein the step S1: selecting different channels of the preprocessed electroencephalogram signals, and calculating a coherence matrix or a correlation matrix among the channels to express the interrelation among the electroencephalogram signals so as to construct an electroencephalogram network, wherein the method specifically comprises the following steps of:
calculating a coherence matrix between channels:
selecting a plurality of channels of the preprocessed electroencephalogram signals, extracting frequency domain features, calculating power spectral density of each channel on different frequency bands, calculating coherence of each channel on each frequency band according to the following formula, and constructing a coherence matrix to obtain an electroencephalogram network;
wherein Gxx (f) and Gyy (f) are power spectral densities of the two preprocessed electroencephalograms respectively, and Gxy (f) is a cross power spectral density of the two preprocessed electroencephalograms; the coherence value Coh (f) represents the coherence degree between the two preprocessed electroencephalogram signals at the frequency f;
or calculating a correlation matrix between channels:
acquiring the preprocessed brain electrical signals of any two channels x and y at n time points, wherein the preprocessed brain electrical signals are respectively x 1 ,x 2 ,...,x n and y1 ,y 2 ,...,y n The pearson correlation coefficients for x and y are calculated according to the following formula:
wherein ,the average value of x and y respectively; r is (r) xy The value range of (C) is [ -1,1]When r is xy When positive, x and y are positive correlation; when r is xy When negative, x and y are represented as negative correlations;
calculating the correlation coefficients among all channels to form a correlation coefficient matrix, wherein each element r in the matrix xy Representing a correlation coefficient between the x-th channel and the y-th channel; and screening the effective connection of the correlation coefficient matrix by setting a threshold value, thereby obtaining the brain network.
CN202310490412.7A 2023-05-04 2023-05-04 Closed-loop electric stimulation system based on brain electric network guidance Pending CN116531661A (en)

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CN117298448A (en) * 2023-10-31 2023-12-29 首都医科大学宣武医院 Electroencephalogram abnormality capturing and rhythm regulating method, system, equipment and storage medium

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117298448A (en) * 2023-10-31 2023-12-29 首都医科大学宣武医院 Electroencephalogram abnormality capturing and rhythm regulating method, system, equipment and storage medium
CN117298448B (en) * 2023-10-31 2024-04-19 首都医科大学宣武医院 Electroencephalogram abnormality capturing and rhythm regulating method, system, equipment and storage medium

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