CN113419902A - Multichannel electroencephalogram signal correlation analysis and data recovery method based on long-time and short-time memory network - Google Patents

Multichannel electroencephalogram signal correlation analysis and data recovery method based on long-time and short-time memory network Download PDF

Info

Publication number
CN113419902A
CN113419902A CN202110723809.7A CN202110723809A CN113419902A CN 113419902 A CN113419902 A CN 113419902A CN 202110723809 A CN202110723809 A CN 202110723809A CN 113419902 A CN113419902 A CN 113419902A
Authority
CN
China
Prior art keywords
data
channel
time
correlation
long
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
CN202110723809.7A
Other languages
Chinese (zh)
Inventor
任彬
潘韫杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Shanghai for Science and Technology
Original Assignee
University of Shanghai for Science and Technology
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 University of Shanghai for Science and Technology filed Critical University of Shanghai for Science and Technology
Priority to CN202110723809.7A priority Critical patent/CN113419902A/en
Publication of CN113419902A publication Critical patent/CN113419902A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/14—Error detection or correction of the data by redundancy in operation
    • G06F11/1402—Saving, restoring, recovering or retrying
    • G06F11/1446—Point-in-time backing up or restoration of persistent data
    • G06F11/1448—Management of the data involved in backup or backup restore
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Molecular Biology (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Biophysics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Computing Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Quality & Reliability (AREA)
  • Dermatology (AREA)
  • Neurology (AREA)
  • Neurosurgery (AREA)
  • Human Computer Interaction (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

The invention discloses a multi-channel electroencephalogram signal correlation analysis and data recovery method based on a long-time and short-time memory network, and belongs to the field of brain-computer interface data processing. Collecting required data by using brain-computer interface equipment; preprocessing an electroencephalogram signal: reducing sampling frequency, performing baseline drift correction and band-pass filtering by utilizing wavelet transformation, and positioning data with abnormal positions; acquiring correlation information between channels by using a time-frequency co-fusion method; inputting the channel with the abnormal data and the related channel into a long-time memory network to obtain a data prediction value of the abnormal position; performing feature extraction by using a common space mode; carrying out classification model training by using a support vector machine, inputting a test set into a classification model, and obtaining classification accuracy data; and analyzing results, and obtaining the classification accuracy results of the recovered data and the data which is not recovered according to the result information. The method has important reference significance for the aspects of electroencephalogram signal real-time processing optimization and electroencephalogram signal data recovery.

Description

Multichannel electroencephalogram signal correlation analysis and data recovery method based on long-time and short-time memory network
Technical Field
The invention relates to a multi-channel electroencephalogram signal correlation analysis and data recovery method based on a long-time and short-time memory network, which is applied to the field of brain-computer interface data processing.
Background
The brain-computer interface is used as a new form of control mode, can provide remote control equipment for technicians working in dangerous scenes, and can also detect the working state of workers to avoid accidents caused by fatigue work. In the non-invasive BCI system, because the electrode is far away from the neuron, the proportion of a useful signal reflecting the subjective action consciousness of a user in the obtained electroencephalogram data to EEG background and other interference noise is small, namely the signal to noise ratio is small, so that all information needs to be kept as far as possible, and the signal to noise ratio can be further improved.
In the actual production process of an enterprise, a large number of interference sources including an external circuit, weather conditions, an environmental electromagnetic field, man-made interference and the like exist, and the data transmission precision of an electronic instrument and the operation stability inside the instrument are seriously influenced under the condition of not carrying out effective processing. The noise environment in a factory interferes with the acquisition of the electroencephalogram signals, and the data are deviated or even lost. The electroencephalogram signals are typical non-stationary random signals, the time when important electroencephalogram signal characteristics appear is extremely short, and if information loss or interference occurs in individual important channels at the moment, the accuracy of subsequent electroencephalogram signal processing is greatly influenced. Therefore, how to recover the information of the abnormal channel is very important.
Disclosure of Invention
The invention aims to provide a multi-channel electroencephalogram correlation analysis and data recovery method based on a long-time memory network, aiming at the problem of electroencephalogram signal acquisition in complex environments such as factories and vehicles. The method solves the difficulty that the disturbed or lost signal data is recovered without an effective method. In practical application, the number of required relevant channels can be flexibly determined according to the performance of different platforms, the size of data volume and the requirement of calculation time, and the relevant channels are sequentially selected as input data of a long-time memory network from high to low according to the calculated correlation degree.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-channel electroencephalogram signal correlation analysis and data recovery method based on a long-time and short-time memory network comprises the following operation steps:
a. collecting required data by utilizing brain-computer interface equipment;
b. preprocessing an electroencephalogram signal: reducing sampling frequency, performing baseline drift correction and band-pass filtering by utilizing wavelet transformation, and positioning abnormal positions of data;
c. acquiring correlation information between channels by using a time-frequency co-fusion method;
d. inputting the channel with the abnormal data and the related channel into a long-time memory network to obtain a data prediction value of the abnormal position;
e. performing feature extraction by using a common space mode;
f. carrying out classification model training by using a support vector machine, inputting a test set into a classification model, and obtaining classification accuracy data;
g. and analyzing results, and respectively obtaining classification accuracy results of the recovered data and the data which is not recovered according to the result information.
Preferably, in the step a, the brain-computer interface device is used for collecting required data, an experimental scheme is formulated, the device is debugged, and the brain-computer interface device is worn by a tester; linking computers to prepare experiments;
designing a motor imagery experiment with an operation numerical control machine tool as a background; the experimental original EEG data is collected by 64-lead equipment, and the sampling rate is 1000 Hz; the experimental contents are that the buttons of the numerical control machine tool operation panel are pressed by the left hand and the right hand, the emergency stop button on the left side of the operation panel is pressed by the left hand, and the cycle start button of the operation panel is pressed by the right hand; one complete experiment is one block, and each experimenter completes 6 blocks in 3 days; the experimental data takes blocks as a unit, each block is continuously acquired EEG data and comprises 20 random motor imagery tasks of left and right hands, each imagination is a trial, the trial is called a trial, and the time length of each trial is 7.5 seconds; in a single block, the left hand and the right hand imagine the tasks 10 times respectively; when the experimental trial begins, firstly entering a target prompting stage, wherein a prompt tone of 'Beep' is set, and meanwhile, one button is highlighted on a screen to prompt a subject that the motor imagery task of the trial is a left hand or a right hand for 1.5 seconds; then, in a motor imagery stage, the button can keep a normally-on state in the motor imagery process, and the subject starts to perform left-hand or right-hand motor imagery; after the motor imagery stage is finished, the subject has a rest time of 2 seconds; a marking signal is recorded at the beginning and the end of the motor imagery of a single trial, and is used as an identification for arranging experimental data.
Preferably, in the step b, the acquired electroencephalogram data is preprocessed: resampling and reducing the frequency of all the acquired electroencephalogram data according to the requirement; then, performing baseline drift correction by using wavelet transformation, and selecting a filtering passband for filtering according to research content; the location of the anomalous data is located.
Preferably, in the step c, the time-frequency co-fusion method obtains the correlation information as follows:
integrating time and frequency components of the electroencephalogram signal; using a time-frequency analysis method based on wavelet transformation; performing wavelet transformation on the preprocessed data to obtain time-frequency power information of each channel and each frequency, and then averaging the power of the frequency band selected by each channel to obtain the change condition of the power on a specific frequency band along with time; the corresponding wavelet transform:
Figure BDA0003137726040000021
wherein,
Figure BDA0003137726040000024
represents the power density of the ith channel at time t at frequency f; λ is a wavelet parameter; phi is at，f(x) In order to be a basis function of the wavelet,
Figure BDA0003137726040000022
wherein
Figure BDA0003137726040000023
Is smallComplex conjugation of wave basis functions;
calculating linear and nonlinear correlation between any two channels by using an interactive information method; taking the average power of each channel as a random variable, and calculating interaction information through entropy and joint entropy; by random variables FiRepresenting the average power signal of the ith channel by ps(Fi，b) Representing the probability density function of the average power signal of the ith channel at the block b; fiEntropy, i.e. the average amount of information reflecting its uncertainty, in H (F)i) Represents; h (F)i) Expressed as:
Figure BDA0003137726040000031
where b is 1, …,50 denotes a block index used to construct the approximate probability density function to avoid underestimating the entropy of larger samples and overestimating the entropy of smaller samples; its joint entropy H (F)i，Fj) Expressed as:
Figure BDA0003137726040000032
in the formula, pc(Fi，b，Fj，b) A joint probability density function representing the average power signal of the ith channel and the average power of the jth channel at block b;
the time-frequency co-fusion method for calculating the two random channels is as follows:
Figure BDA0003137726040000033
the time-frequency co-fusion value (TFCMI) is an index that evaluates the relationship between two channels based on their average power variation and signal-to-noise ratio over a selected frequency band; and obtaining the relation between each channel through the time-frequency co-fusion value, wherein the higher the value is, the higher the correlation is.
Preferably, in the step d, a channel with an abnormal data and a related channel are input into a long-term memory network, and a channel through which the data is recovered is acquired:
after the correlation information of the abnormal channel is collected, acquiring data of the channel with high correlation and data of the abnormal channel; taking data in a period of time before the abnormal point and normal data of a channel related to the abnormal point at the same time as the abnormal point as prediction characteristics together; inputting data before the missing point into a Long Short Term memory Network (LSTM) Network for prediction, and inputting a prediction result and normal data of a related channel into a Neural Network (NN) for prediction to obtain a prediction value of the abnormal point;
the LSTM network is one of deep learning networks, and the established LSTM network consists of an input layer, an LSTM unit layer, a Dropout layer, a full connection layer and a linear regression layer; the NN network consists of an input layer, a ReLU layer, a full connection layer and a regression layer; wherein, the LSTM unit layer of the LSTM network consists of 1000 LSTM units, and the discarding rate of the Dropout layer is 50%; the LSTM network trains for 200 rounds, the initial learning rate is 0.01, and the learning rate is reduced by half in every 50 rounds of training; the gradient threshold value is 1, and the optimizer is an adam optimizer;
LSTM is used to process and predict significant events in time series that are very long-spaced and delayed, and generally performs better than recurrent neural networks and hidden markov models HMMs; the LSTM network is one of the recurrent neural networks RNN, which is reliable for long-time sequence prediction; the key of the LSTM network is to Forget a gate Forget gate, then an Input gate, and finally an Output gate; the forgetting gate determines what is discarded from the cell state, the input gate determines to put new information in the cell state, and the output gate determines the output state at this time.
Preferably, in the step e, feature extraction is performed by using a common space mode:
the CSP is a space domain filtering feature extraction algorithm under two classification tasks, and can extract space distribution features of each type from multi-channel brain-computer interface data; the basic principle of the common space mode algorithm is that a group of optimal space filters are found for projection by utilizing diagonalization of a matrix, so that the variance value of two types of signals reaches the maximum difference, and the feature vector with higher discrimination is obtained.
Preferably, in step f, a support vector machine is used to perform classification model training, and the test set is input into a classification model to obtain classification accuracy data:
dividing the recovered channel data into a training set and a test set, inputting the training set features subjected to CSP feature extraction into a support vector machine for classification model training; and inputting the test set into a classification model to obtain classification accuracy.
Preferably, in the step g, result analysis is performed, and according to result information, classification accuracy results of the recovered data and the data that is not recovered are respectively obtained:
according to the experimental result, obtaining the classification accuracy results of the recovered data and the data which are not recovered, specifically comprising a recovered electroencephalogram signal data graph and a classification accuracy comparison table;
if the original correct data exist, in order to evaluate the effectiveness of a multi-channel electroencephalogram correlation analysis and data recovery method based on a long-time and short-time memory network, three performance indexes, namely, correlation of a Root Mean Square Error (RMSE), an absolute error (MAE) and a spearman grade are used; the root mean square error RMSE, absolute error MAE are defined as follows:
Figure BDA0003137726040000041
Figure BDA0003137726040000042
wherein m is the number of samples, ytestIn order to test the actual value of the set,
Figure BDA0003137726040000043
predicting values for the test set;
the spearman grade correlation belongs to a non-parameter statistical method, and does not require the distribution of original variables; the method is suitable for the data which do not follow normal distribution, the data which are unknown in overall distribution and represented by the original data by grades, and the electroencephalogram signal calculation formula is as follows:
Figure BDA0003137726040000044
wherein d isq＝(xq-yq)，xqAnd yqThe rank of two variables in order according to size is respectively, and n is the sample capacity;
the value range of the Spearman grade correlation coefficient is as follows: [ -1,1]The larger the absolute value is, the stronger the correlation is; r issIf the number is positive, the correlation of positive grades is considered to exist; r issIf the number is negative, the negative rank correlation is considered to exist; r issWhen the value is 1, the two variables are completely same in grade, and complete positive correlation exists; and vice versa.
Compared with the prior art, the invention has the following obvious prominent substantive characteristics and remarkable advantages:
1. the multichannel electroencephalogram signal correlation analysis and data recovery method based on the long-time memory network solves the problem that data in the acquisition phase of the electroencephalogram signal in practical application cannot be recovered due to loss, and fills up the blank of research in related fields;
2. in practical application, the method flexibly determines the number of the required channels according to the performance of different platforms, the size of data volume and the requirement of calculation time, and selects the relevant channels as input data in turn according to the relevance from high to low, thereby achieving the purposes of shortening the calculation time and improving the stability of the system.
Drawings
FIG. 1 is a block flow diagram of the process of the present invention.
FIG. 2 is a process of the correlation analysis, long and short term memory network, of the present invention.
Fig. 3 is an architecture diagram of a long-term memory network according to the present invention.
FIG. 4 is a graph comparing recovered data with correct data according to the present invention.
Detailed Description
The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings:
the first embodiment is as follows:
in this embodiment, referring to fig. 1, a method for analyzing correlation between multi-channel electroencephalograms and recovering data based on a long-time and short-time memory network includes the following steps:
a. and collecting required data by utilizing a brain-computer interface device.
b. Preprocessing an electroencephalogram signal: and reducing the sampling frequency, and performing baseline drift correction, band-pass filtering and positioning data of abnormal positions by using wavelet transformation.
c. And acquiring the correlation information between channels by using a time-frequency co-fusion method.
d. And inputting the channel with the abnormal data and the related channel into a long-time memory network to obtain a data prediction value of the abnormal position.
e. And performing feature extraction by using a common space mode.
f. And (4) carrying out classification model training by using a support vector machine, inputting the test set into a classification model, and obtaining classification accuracy data.
g. And analyzing results, and respectively obtaining classification accuracy results of the recovered data and the data which is not recovered according to the result information.
The method solves the difficulty that the abnormal channel data is lost and cannot be repaired after being interfered, and simultaneously avoids the influence of interference information contained in the channel irrelevant to the task on the prediction result.
Example two:
this embodiment is substantially the same as the first embodiment, and is characterized in that:
in this embodiment, referring to fig. 1, a method for analyzing correlation between multi-channel electroencephalogram signals and recovering data based on a long-time and short-time memory network includes the following operation steps:
a. acquiring required data by utilizing a brain-computer interface device:
and (5) establishing an experimental scheme. And the debugging equipment is used for wearing brain-computer interface equipment for a tester. Linking computers, and preparing experiments.
b. Preprocessing an electroencephalogram signal:
preprocessing the acquired electroencephalogram data: resampling and reducing the frequency of all the acquired electroencephalogram data according to the requirement; then, performing baseline drift correction by using wavelet transformation, and selecting a filtering passband for filtering according to research content; locating the position of the abnormal data;
c. obtaining relevant information by a time-frequency co-fusion method:
integrating time and frequency components of the electroencephalogram signal; using a time-frequency analysis method based on wavelet transformation; performing wavelet transformation on the preprocessed data to obtain time-frequency power information of each channel and each frequency, and then averaging the power of the frequency band selected by each channel to obtain the change condition of the power on a specific frequency band along with time; corresponding wavelet transform
Figure BDA0003137726040000061
Wherein,
Figure BDA0003137726040000066
represents the power density of the ith channel at time t at frequency f; λ is a wavelet parameter; phi is at，f(x) In order to be a basis function of the wavelet,
Figure BDA0003137726040000062
wherein
Figure BDA0003137726040000063
Is the complex conjugation of wavelet basis functions;
calculating linear and nonlinear correlation between any two channels by using an interactive information method; taking the average power of each channel as a random variable, and calculating interaction information through entropy and joint entropy; by random variables FiRepresenting the average power signal of the ith channel by ps(Fi，b) Representing the probability density function of the average power signal of the ith channel at the block b; fiEntropy, i.e. the average amount of information reflecting its uncertainty, ofH(Fi) Represents; h (F)i) Expressed as:
Figure BDA0003137726040000064
where b is 1, …,50 denotes a block index used to construct the approximate probability density function to avoid underestimating the entropy of larger samples and overestimating the entropy of smaller samples; its joint entropy H (F)i，Fj) Expressed as:
Figure BDA0003137726040000065
in the formula, pc(Fi，b，Fj，b) A joint probability density function representing the average power signal of the ith channel and the average power of the jth channel at block b;
the time-frequency co-fusion method for calculating the two random channels is as follows:
Figure BDA0003137726040000071
the time-frequency co-fusion value (TFCMI) is an index that evaluates the relationship between two channels based on their average power variation and signal-to-noise ratio over a selected frequency band; obtaining the relation between each channel through the time-frequency co-fusion value, wherein the higher the value is, the higher the correlation is;
d. inputting the channel with the abnormality of the related channel and the data into a long-time memory network, and acquiring the channel of the data after recovery:
after the correlation information of the abnormal channel is collected, the data of the channel with high correlation and the data of the abnormal channel are obtained. And taking data in a period of time before the missing point and normal data of channels related to the missing point at the same time as the predicting characteristic together. Inputting data before the missing point into a Long Short Term memory Network (LSTM) Network for prediction, and inputting a prediction result and normal data of a related channel into a Neural Network (NN) for prediction to obtain a prediction value of the missing point.
The LSTM network is one type of deep learning network. The established LSTM network consists of an input layer, an LSTM unit layer, a Dropout layer, a full connection layer and a linear regression layer. The NN network consists of an input layer, a ReLU layer, a full connection layer and a regression layer. Wherein, the LSTM unit layer of the LSTM network consists of 1000 LSTM units, and the discarding rate of the Dropout layer is 50%. The LSTM network is trained for 200 rounds, the initial learning rate is 0.01, and the learning rate is reduced by half every 50 rounds of training. The gradient threshold is 1 and the optimizer is an adam optimizer.
LSTM is suitable for processing and predicting significant events of very long intervals and delays in time series, and generally performs better than recurrent neural networks and Hidden Markov Models (HMMs). The LSTM network is one of the Recurrent Neural Networks (RNNs), which has proven reliable for long-term sequence prediction in many studies. The key to the LSTM network is the forgetting gate (Forget gate), followed by the Input gate (Input gate), and the Output gate (Output gate) the first time. The forgetting gate determines what is discarded from the cell state, the input gate determines to put new information in the cell state, and the output gate determines the output state at this time.
e. And (3) common spatial mode feature extraction:
the common space mode (CSP) is an algorithm for extracting spatial filtering features under two classification tasks, and can extract spatial distribution features of each class from multi-channel brain-computer interface data; the common space mode algorithm has the basic principle that a group of optimal space filters are found for projection by utilizing diagonalization of a matrix, so that the variance value of two types of signals reaches the maximum difference, and the characteristic vector with higher discrimination is obtained;
f. and (3) support vector machine feature classification:
dividing the preprocessed data into a training set and a test set, inputting the training set features after CSP feature extraction into a support vector machine for classification model training; inputting the test set into a classification model to obtain classification accuracy;
g. and (4) analyzing results:
and obtaining the classification accuracy results of the recovered data and the data which is not recovered according to the experimental result. The method specifically comprises a recovered electroencephalogram data graph and a classification accuracy comparison table.
In the embodiment, the method for analyzing the correlation of the multi-channel electroencephalogram signals and recovering the data based on the long-time and short-time memory network selects the required data channel in a targeted manner, and inputs the related data channel and the abnormal data channel into the long-time and short-time memory network together to obtain the recovered data. Performing feature extraction by adopting a common space mode, and performing feature classification by using a support vector machine; in practical application, the method flexibly determines the number of the required channels according to the performance of different platforms, the size of data volume and the requirement of calculation time, and selects the relevant channels as input data in sequence according to the relevance from high to low, thereby achieving the purposes of shortening the calculation time and improving the stability of the system.
Example three:
this embodiment is substantially the same as the above embodiment, and is characterized in that:
in this embodiment, referring to fig. 1, a method for analyzing correlation between multi-channel electroencephalogram signals and recovering data based on a long-time and short-time memory network includes the following steps:
a. collecting required data by brain-computer interface equipment
And (5) establishing an experimental scheme. And the debugging equipment is used for wearing brain-computer interface equipment for a tester. Linking computers, and preparing experiments.
A motor imagery experiment with the background of operating a numerical control machine tool is designed. Experimental raw EEG data was collected by a 64-lead device with a sample rate of 1000 Hz. The experimental contents are that the left hand and the right hand are used for pressing the buttons of the numerical control machine tool operation panel, the left hand is used for pressing the emergency stop button on the left side of the operation panel, and the right hand is used for pressing the cycle start button of the operation panel. One complete experiment is one block, and each experimenter completes 6 blocks in 3 days. The experimental data takes blocks as units, each block is continuously acquired EEG data and comprises 20 left-hand and right-hand random motor imagery tasks, each imagery task is a trial, the trial is called a trial, and the time length of each trial is 7.5 seconds. In a single block, the left and right hands imagine the tasks 10 times each. When the experimental trial begins, a target prompting stage is firstly entered, in the stage, a prompt tone of 'Beep' is generated, meanwhile, one button is highlighted on a screen, the motor imagery task of the trial is prompted to be the left hand or the right hand, and the duration is 1.5 seconds. Then, in the motor imagery stage, the button is kept in a normally-on state during the motor imagery, and the subject starts to perform left-hand or right-hand motor imagery. After the motor imagery phase is over, the subject will have a 2 second rest period. Recording a marking signal at the beginning and the end of the motor imagery of a single trial time to be used as an identification for arranging experimental data;
b. electroencephalogram signal preprocessing
According to the content of the invention content a, preprocessing the acquired motor imagery electroencephalogram data; resampling the acquired electroencephalogram data and reducing the frequency to 250 Hz; performing baseline drift correction by using wavelet transformation, and performing band-pass filtering with a passband of 8-30Hz by using a second-order IIR filter based on Butterworth; carrying out 8-30Hz band-pass filtering to reserve the motor imagery signal characteristics contained in alpha wave and beta wave frequency bands and simultaneously removing most electromyographic noise interference; observing the oscillogram or finding the position of abnormal data by using a full-automatic algorithm for identifying the artifact;
c. method for acquiring correlation information by time-frequency co-fusion method
According to the content of the invention content b, analyzing the correlation among the channels by using a time-frequency co-fusion method; firstly, a time-frequency analysis method based on wavelet transformation is applied; firstly, performing wavelet transformation on preprocessed data to obtain time-frequency power information of each channel and each frequency, and then averaging the power of a frequency band selected by each channel to obtain the change condition of the power on a specific frequency band along with time;
calculating linear and nonlinear correlation between any two channels by using an interactive information method; taking the average power of each channel as a random variable, and calculating interaction information through entropy and joint entropy;
and calculating the time-frequency co-fusion value of the two random channels, wherein the higher the numerical value is, the higher the correlation degree is.
d. Inputting the channel with the abnormality of the related channel and the data into a long-time memory network, and acquiring the channel of the data after recovery:
after the correlation information of the abnormal channel is collected, the data of the channel with high correlation and the data of the abnormal channel are obtained. And taking data in a period of time before the missing point and normal data of channels related to the missing point at the same time as the predicting characteristic together. Inputting data before the missing point into a Long Short Term memory Network (LSTM) Network for prediction, and inputting a prediction result and normal data of a related channel into a Neural Network (NN) for prediction to obtain a prediction value of the missing point.
The LSTM network is one type of deep learning network. The established LSTM network consists of an input layer, an LSTM unit layer, a Dropout layer, a full connection layer and a linear regression layer. The NN network consists of an input layer, a ReLU layer, a full connection layer and a regression layer. Wherein, the LSTM unit layer of the LSTM network consists of 1000 LSTM units, and the discarding rate of the Dropout layer is 50%. The LSTM network is trained for 200 rounds, the initial learning rate is 0.01, and the learning rate is reduced by half every 50 rounds of training. The gradient threshold is 1 and the optimizer is an adam optimizer.
e. Common spatial mode feature extraction
According to the content of the invention content d, a common space mode is adopted for feature extraction; if the original correct data exist, dividing the preprocessed data into two types of data sets, wherein the two types of data sets are respectively as follows:
(1) data with no exception is generated;
(2) data which are recovered through the method are abnormal;
dividing the two types of data sets into a training set and a test set respectively, and extracting features by using a common space mode;
f. support vector machine feature classification
According to the content of the invention content e, inputting the training set characteristics of the two types of data sets into a support vector machine for carrying out classification model training; inputting the test set into a classification model to obtain the classification accuracy of two data sets;
g. analysis of results
Three performance indicators, Root Mean Square Error (RMSE), absolute error (MAE), and Spearman Rank Correlation (Spearman Rank Correlation), were used to evaluate the performance of the data recovery model in the presence of the original correct data. The results are shown in FIG. 4. In the embodiment, a motor imagery electroencephalogram data processing experiment is designed by utilizing a multi-channel electroencephalogram signal correlation analysis and data recovery method based on a long-time and short-time memory network, and according to the experimental result, the data recovery method provided by the invention is applied to the environment in which wireless signal transmission is influenced, such as an industrial workshop or a vehicle-mounted system. The channel number and the channels can be selected according to the actual system performance and the real-time requirement, and the method has reference and guidance significance for real-time processing and optimization of the electroencephalogram signals.
Example four:
this embodiment is substantially the same as the above embodiment, and is characterized in that:
in this embodiment, referring to fig. 1, in the step g, result analysis is performed, and according to result information, classification accuracy results of recovered data and unrecoverable data are obtained respectively:
according to the experimental result, obtaining the classification accuracy results of the recovered data and the data which are not recovered, specifically comprising a recovered electroencephalogram signal data graph and a classification accuracy comparison table;
if the original correct data exist, in order to evaluate the effectiveness of a multi-channel electroencephalogram correlation analysis and data recovery method based on a long-time and short-time memory network, three performance indexes, namely, correlation of a Root Mean Square Error (RMSE), an absolute error (MAE) and a spearman grade are used; the root mean square error RMSE, absolute error MAE are defined as follows:
Figure BDA0003137726040000101
Figure BDA0003137726040000102
wherein m is the number of samples, ytestIn order to test the actual value of the set,
Figure BDA0003137726040000103
predicting values for the test set;
the spearman grade correlation belongs to a non-parameter statistical method, and does not require the distribution of original variables; the method is suitable for the data which do not follow normal distribution, the data which are unknown in overall distribution and represented by the original data by grades, and the electroencephalogram signal calculation formula is as follows:
Figure BDA0003137726040000104
wherein d isq＝(xq-yq)，xqAnd yqThe rank of two variables in order according to size is respectively, and n is the sample capacity;
the value range of the Spearman grade correlation coefficient is as follows: [ -1,1]The larger the absolute value is, the stronger the correlation is; r issIf the number is positive, the correlation of positive grades is considered to exist; r issIf the number is negative, the negative rank correlation is considered to exist; r issWhen the value is 1, the two variables are completely same in grade, and complete positive correlation exists; and vice versa.
To sum up, the above embodiment is based on a multi-channel electroencephalogram correlation analysis and data recovery method of a long-and-short-term memory network, and utilizes brain-computer interface equipment to acquire required data; then brain electrical signal pretreatment is carried out: reducing sampling frequency, performing baseline drift correction and band-pass filtering by utilizing wavelet transformation, and positioning abnormal positions of data; acquiring correlation information between channels by using a time-frequency co-fusion method; inputting the channel with the abnormal data and the related channel into a long-time memory network to obtain a data prediction value of the abnormal position; performing feature extraction by using a common space mode; carrying out classification model training by using a support vector machine, inputting a test set into a classification model, and obtaining classification accuracy data; and analyzing results, and respectively obtaining classification accuracy results of the recovered data and the data which is not recovered according to the result information. The embodiment has remarkable innovativeness and feasibility, and has important reference significance for real-time processing optimization of the electroencephalogram signals and data recovery of the electroencephalogram signals.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A multi-channel electroencephalogram signal correlation analysis and data recovery method based on a long-time and short-time memory network is characterized by comprising the following operation steps:
a. collecting required data by utilizing brain-computer interface equipment;
b. preprocessing an electroencephalogram signal: reducing sampling frequency, performing baseline drift correction and band-pass filtering by utilizing wavelet transformation, and positioning abnormal positions of data;
c. acquiring correlation information between channels by using a time-frequency co-fusion method;
d. inputting the channel with the abnormal data and the related channel into a long-time memory network to obtain a data prediction value of the abnormal position;
e. performing feature extraction by using a common space mode;
f. carrying out classification model training by using a support vector machine, inputting a test set into a classification model, and obtaining classification accuracy data;
g. and analyzing results, and respectively obtaining classification accuracy results of the recovered data and the data which is not recovered according to the result information.
2. The multi-channel electroencephalogram signal correlation analysis and data recovery method based on the long-time and short-time memory network as claimed in claim 1, which is characterized in that: in the step a, the brain-computer interface equipment is used for collecting required data, an experimental scheme is formulated, the equipment is debugged, and the brain-computer interface equipment is worn by a tester; linking computers to prepare experiments;
designing a motor imagery experiment with an operation numerical control machine tool as a background; the experimental original EEG data is collected by 64-lead equipment, and the sampling rate is 1000 Hz; the experimental contents are that the buttons of the numerical control machine tool operation panel are pressed by the left hand and the right hand, the emergency stop button on the left side of the operation panel is pressed by the left hand, and the cycle start button of the operation panel is pressed by the right hand; one complete experiment is one block, and each experimenter completes 6 blocks in 3 days; the experimental data takes blocks as a unit, each block is continuously acquired EEG data and comprises 20 random motor imagery tasks of left and right hands, each imagination is a trial, the trial is called a trial, and the time length of each trial is 7.5 seconds; in a single block, the left hand and the right hand imagine the tasks 10 times respectively; when the experimental trial begins, firstly entering a target prompting stage, wherein a prompt tone of 'Beep' is set, and meanwhile, one button is highlighted on a screen to prompt a subject that the motor imagery task of the trial is a left hand or a right hand for 1.5 seconds; then, in a motor imagery stage, the button can keep a normally-on state in the motor imagery process, and the subject starts to perform left-hand or right-hand motor imagery; after the motor imagery stage is finished, the subject has a rest time of 2 seconds; a marking signal is recorded at the beginning and the end of the motor imagery of a single trial, and is used as an identification for arranging experimental data.
3. The multi-channel electroencephalogram signal correlation analysis and data recovery method based on the long-time memory network as claimed in claim 1, wherein in the step b, the acquired electroencephalogram data are preprocessed: resampling and reducing the frequency of all the acquired electroencephalogram data according to the requirement; then, performing baseline drift correction by using wavelet transformation, and selecting a filtering passband for filtering according to research content; the location of the anomalous data is located.
4. The method for analyzing correlation and recovering data of multi-channel electroencephalogram signals based on the long-and-short-term memory network as claimed in claim 1, wherein in the step c, the method for acquiring correlation information by the time-frequency co-fusion method is as follows:
integrating time and frequency components of the electroencephalogram signal; using a time-frequency analysis method based on wavelet transformation; performing wavelet transformation on the preprocessed data to obtain time-frequency power information of each channel and each frequency, and then averaging the power of the frequency band selected by each channel to obtain the change condition of the power on a specific frequency band along with time; the corresponding wavelet transform:
Figure FDA0003137726030000021
wherein,
Figure FDA0003137726030000022
represents the power density of the ith channel at time t at frequency f; λ is a wavelet parameter; phi is at，f(x) In order to be a basis function of the wavelet,
Figure FDA0003137726030000023
wherein
Figure FDA0003137726030000024
Figure FDA0003137726030000025
Is the complex conjugation of wavelet basis functions;
calculating linear and nonlinear correlation between any two channels by using an interactive information method; taking the average power of each channel as a random variable, and calculating interaction information through entropy and joint entropy; by random variables FiRepresenting the average power signal of the ith channel by ps(Fi，b) Representing the probability density function of the average power signal of the ith channel at the block b; fiEntropy, i.e. the average amount of information reflecting its uncertainty, in H (F)i) Represents; h (F)i) Expressed as:
Figure FDA0003137726030000026
where b is 1, …,50 denotes a block index used to construct the approximate probability density function to avoid underestimating the entropy of larger samples and overestimating the entropy of smaller samples; its joint entropy H (F)i，Fj) Expressed as:
Figure FDA0003137726030000027
in the formula, pc(Fi，b，Fj，b) A joint probability density function representing the average power signal of the ith channel and the average power of the jth channel at block b;
the time-frequency co-fusion method for calculating the two random channels is as follows:
Figure FDA0003137726030000028
the time-frequency co-fusion value (TFCMI) is an index that evaluates the relationship between two channels based on their average power variation and signal-to-noise ratio over a selected frequency band; and obtaining the relation between each channel through the time-frequency co-fusion value, wherein the higher the value is, the higher the correlation is.
5. The method for analyzing correlation of multi-channel electroencephalogram signals and recovering data based on the long-time memory network as claimed in claim 1, wherein in the step d, the channel with the abnormal correlation channel and data is input into the long-time memory network, and the recovered channel of the data is obtained:
after the correlation information of the abnormal channel is collected, acquiring data of the channel with high correlation and data of the abnormal channel; taking data in a period of time before the abnormal point and normal data of a channel related to the abnormal point at the same time as the abnormal point as prediction characteristics together; inputting data before the missing point into a Long Short Term memory Network (LSTM) Network for prediction, and inputting a prediction result and normal data of a related channel into a Neural Network (NN) for prediction to obtain a prediction value of the abnormal point;
the LSTM network is one of deep learning networks, and the established LSTM network consists of an input layer, an LSTM unit layer, a Dropout layer, a full connection layer and a linear regression layer; the NN network consists of an input layer, a ReLU layer, a full connection layer and a regression layer; wherein, the LSTM unit layer of the LSTM network consists of 1000 LSTM units, and the discarding rate of the Dropout layer is 50%; the LSTM network trains for 200 rounds, the initial learning rate is 0.01, and the learning rate is reduced by half in every 50 rounds of training; the gradient threshold value is 1, and the optimizer is an adam optimizer;
LSTM is used to process and predict significant events in time series that are very long-spaced and delayed, and generally performs better than recurrent neural networks and hidden markov models HMMs; the LSTM network is one of the recurrent neural networks RNN, which is reliable for long-time sequence prediction; the key of the LSTM network is that a forgetting gate Forgetgate is used, an input gate is used next, and an output gate is used last; the forgetting gate determines what is discarded from the cell state, the input gate determines to put new information in the cell state, and the output gate determines the output state at this time.
6. The method for analyzing correlation and recovering data of multi-channel electroencephalogram signals based on a long-and-short time memory network as claimed in claim 1, wherein in the step e, a common space mode is utilized for feature extraction:
the CSP is a space domain filtering feature extraction algorithm under two classification tasks, and can extract space distribution features of each type from multi-channel brain-computer interface data; the basic principle of the common space mode algorithm is that a group of optimal space filters are found for projection by utilizing diagonalization of a matrix, so that the variance value of two types of signals reaches the maximum difference, and the feature vector with higher discrimination is obtained.
7. The method for analyzing correlation and recovering data of multi-channel electroencephalogram signals based on the long-and-short-term memory network as claimed in claim 1, wherein in the step f, a support vector machine is utilized to carry out classification model training, a test set is input into a classification model, and classification accuracy data are obtained:
dividing the recovered channel data into a training set and a test set, inputting the training set features subjected to CSP feature extraction into a support vector machine for classification model training; and inputting the test set into a classification model to obtain classification accuracy.
8. The multi-channel electroencephalogram signal correlation analysis and data recovery method based on the long-and-short-term memory network as claimed in claim 1, wherein in the step g, result analysis is performed, and according to result information, classification accuracy results of recovered data and data which are not recovered are respectively obtained:
according to the experimental result, obtaining the classification accuracy results of the recovered data and the data which are not recovered, specifically comprising a recovered electroencephalogram signal data graph and a classification accuracy comparison table;
if the original correct data exist, in order to evaluate the effectiveness of a multi-channel electroencephalogram correlation analysis and data recovery method based on a long-time and short-time memory network, three performance indexes, namely, correlation of a Root Mean Square Error (RMSE), an absolute error (MAE) and a spearman grade are used; the root mean square error RMSE, absolute error MAE are defined as follows:
Figure FDA0003137726030000041
Figure FDA0003137726030000042
wherein m is the number of samples, ytestIn order to test the actual value of the set,
Figure FDA0003137726030000043
predicting values for the test set;
the spearman grade correlation belongs to a non-parameter statistical method, and does not require the distribution of original variables; the method is suitable for the data which do not follow normal distribution, the data which are unknown in overall distribution and represented by the original data by grades, and the electroencephalogram signal calculation formula is as follows:
Figure FDA0003137726030000044
wherein d isq＝(xq-yq)，xqAnd yqThe rank of two variables in order according to size is respectively, and n is the sample capacity;
the value range of the Spearman grade correlation coefficient is as follows: [ -1,1]The larger the absolute value is, the stronger the correlation is; r issIf the number is positive, the correlation of positive grades is considered to exist; r issIf the number is negative, the negative rank correlation is considered to exist; r issWhen the value is 1, the two variables are completely same in grade, and complete positive correlation exists; and vice versa.
CN202110723809.7A 2021-06-29 2021-06-29 Multichannel electroencephalogram signal correlation analysis and data recovery method based on long-time and short-time memory network Pending CN113419902A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110723809.7A CN113419902A (en) 2021-06-29 2021-06-29 Multichannel electroencephalogram signal correlation analysis and data recovery method based on long-time and short-time memory network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110723809.7A CN113419902A (en) 2021-06-29 2021-06-29 Multichannel electroencephalogram signal correlation analysis and data recovery method based on long-time and short-time memory network

Publications (1)

Publication Number Publication Date
CN113419902A true CN113419902A (en) 2021-09-21

Family

ID=77717006

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110723809.7A Pending CN113419902A (en) 2021-06-29 2021-06-29 Multichannel electroencephalogram signal correlation analysis and data recovery method based on long-time and short-time memory network

Country Status (1)

Country Link
CN (1) CN113419902A (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113988400A (en) * 2021-10-22 2022-01-28 重庆工商大学 AQI index prediction method based on PEARSON-LSTM multi-step fusion network
CN114533066A (en) * 2022-04-28 2022-05-27 之江实验室 Social anxiety assessment method and system based on composite expression processing brain network
CN115356694A (en) * 2022-08-26 2022-11-18 哈尔滨工业大学（威海） Anti-impact interference method and system for high-frequency ground wave radar, computer equipment and application
CN116269450A (en) * 2023-03-21 2023-06-23 苏州海臻医疗器械有限公司 Patient limb rehabilitation state evaluation system and method based on electromyographic signals
CN116541766A (en) * 2023-07-04 2023-08-04 中国民用航空飞行学院 Training method of electroencephalogram data restoration model, electroencephalogram data restoration method and device
CN118070002A (en) * 2024-03-21 2024-05-24 北京交通大学 Method and system for recovering track traffic multichannel vibration signal lost data

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104035563A (en) * 2014-06-19 2014-09-10 北京工业大学 W-PCA (wavelet transform-principal component analysis) and non-supervision GHSOM (growing hierarchical self-organizing map) based electrocardiographic signal identification method
CN109959122A (en) * 2019-03-11 2019-07-02 浙江工业大学 A kind of Energy consumption forecast for air conditioning method based on shot and long term memory Recognition with Recurrent Neural Network
CN110464334A (en) * 2019-07-23 2019-11-19 苏州国科视清医疗科技有限公司 A kind of electrocardiographic abnormality detection method based on composite depth learning network
KR102096565B1 (en) * 2018-11-08 2020-04-02 광운대학교 산학협력단 Analysis method of convolutional neural network based on Wavelet transform for identifying motor imagery brain waves
CN111053549A (en) * 2019-12-23 2020-04-24 威海北洋电气集团股份有限公司 Intelligent biological signal abnormality detection method and system
CN112515685A (en) * 2020-11-10 2021-03-19 上海大学 Multi-channel electroencephalogram signal channel selection method based on time-frequency co-fusion

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104035563A (en) * 2014-06-19 2014-09-10 北京工业大学 W-PCA (wavelet transform-principal component analysis) and non-supervision GHSOM (growing hierarchical self-organizing map) based electrocardiographic signal identification method
KR102096565B1 (en) * 2018-11-08 2020-04-02 광운대학교 산학협력단 Analysis method of convolutional neural network based on Wavelet transform for identifying motor imagery brain waves
CN109959122A (en) * 2019-03-11 2019-07-02 浙江工业大学 A kind of Energy consumption forecast for air conditioning method based on shot and long term memory Recognition with Recurrent Neural Network
CN110464334A (en) * 2019-07-23 2019-11-19 苏州国科视清医疗科技有限公司 A kind of electrocardiographic abnormality detection method based on composite depth learning network
CN111053549A (en) * 2019-12-23 2020-04-24 威海北洋电气集团股份有限公司 Intelligent biological signal abnormality detection method and system
CN112515685A (en) * 2020-11-10 2021-03-19 上海大学 Multi-channel electroencephalogram signal channel selection method based on time-frequency co-fusion

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
任彬: "Research on time-frequency cross mutual of motor imagination data based on multichannel EEG signal", 《HIGH TECHNOLOGY LETTERS》, no. 28, 31 March 2022 (2022-03-31), pages 21 - 29 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113988400A (en) * 2021-10-22 2022-01-28 重庆工商大学 AQI index prediction method based on PEARSON-LSTM multi-step fusion network
CN113988400B (en) * 2021-10-22 2024-04-09 重庆工商大学 AQI index prediction method based on PEARSON-LSTM multi-step fusion network
CN114533066A (en) * 2022-04-28 2022-05-27 之江实验室 Social anxiety assessment method and system based on composite expression processing brain network
CN114533066B (en) * 2022-04-28 2022-08-19 之江实验室 Social anxiety assessment method and system based on composite expression processing brain network
CN115356694A (en) * 2022-08-26 2022-11-18 哈尔滨工业大学（威海） Anti-impact interference method and system for high-frequency ground wave radar, computer equipment and application
CN115356694B (en) * 2022-08-26 2023-08-22 哈尔滨工业大学（威海） High-frequency ground wave radar anti-impact interference method, system, computer equipment and application
CN116269450A (en) * 2023-03-21 2023-06-23 苏州海臻医疗器械有限公司 Patient limb rehabilitation state evaluation system and method based on electromyographic signals
CN116269450B (en) * 2023-03-21 2023-12-19 苏州海臻医疗器械有限公司 Patient limb rehabilitation state evaluation system and method based on electromyographic signals
CN116541766A (en) * 2023-07-04 2023-08-04 中国民用航空飞行学院 Training method of electroencephalogram data restoration model, electroencephalogram data restoration method and device
CN116541766B (en) * 2023-07-04 2023-09-22 中国民用航空飞行学院 Training method of electroencephalogram data restoration model, electroencephalogram data restoration method and device
CN118070002A (en) * 2024-03-21 2024-05-24 北京交通大学 Method and system for recovering track traffic multichannel vibration signal lost data

Similar Documents

Publication Publication Date Title
CN113419902A (en) Multichannel electroencephalogram signal correlation analysis and data recovery method based on long-time and short-time memory network
CN112515685B (en) Multi-channel electroencephalogram signal channel selection method based on time-frequency co-fusion
CN101259015B (en) Electroencephalogram signal analyzing monitoring method and device thereof
CN103310113B (en) A kind ofly to be separated and the general blood glucose prediction method of data modeling based on frequency band
CN113128552B (en) Electroencephalogram emotion recognition method based on depth separable causal graph convolution network
CN112244873A (en) Electroencephalogram time-space feature learning and emotion classification method based on hybrid neural network
CN114533086B (en) Motor imagery brain electrolysis code method based on airspace characteristic time-frequency transformation
CN101515200B (en) Target selecting method based on transient visual evoked electroencephalogram
CN113536882B (en) Multi-class motor imagery electroencephalogram signal feature extraction and classification method
US20140025715A1 (en) Neural Signal Processing and/or Interface Methods, Architectures, Apparatuses, and Devices
CN112036354A (en) Natural action electroencephalogram recognition method based on Riemann geometry
CN102488517A (en) Method and device for detecting burst suppression state in brain signal
CN112957055B (en) EEMD-PCA-based method and device for removing motion artifacts in EEG signals
CN113017627B (en) Depression and bipolar disorder brain network analysis method based on two-channel phase synchronization feature fusion
CN104490391A (en) Combatant state monitoring system based on electroencephalogram signals
CN111832452B (en) Characteristic optimization and recognition method of special gesture instruction based on brain electricity
CN111067513B (en) Sleep quality detection key brain area judgment method based on characteristic weight self-learning
CN115964655A (en) Method for monitoring error-related potential in brain-computer interface based on mutual information quantity
Shi et al. A Novel Method of sEMG Signal Segmentation
CN117918863A (en) Method and system for processing brain electrical signal real-time artifacts and extracting features
CN115067878A (en) EEGNet-based resting state electroencephalogram consciousness disorder classification method and system
CN115270847A (en) Design decision electroencephalogram recognition method based on wavelet packet decomposition and convolutional neural network
CN110516711B (en) Training set quality evaluation method of MI-BCI system and optimization method of single training sample
CN114841192A (en) Electroencephalogram signal feature enhancement method based on reinforcement learning combined denoising and space-time relation modeling
Murthy et al. Design and implementation of hybrid techniques and DA-based reconfigurable FIR filter design for noise removal in EEG signals on FPGA

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination