CN103258215A - Multi-lead correlation analysis electroencephalo-graph (EEG) feature extraction method - Google Patents
Multi-lead correlation analysis electroencephalo-graph (EEG) feature extraction method Download PDFInfo
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Abstract
The invention relates to a multi-lead correlation analysis electroencephalo-graph (EEG) feature extraction method. In multi-class motor imagery task recognition, EEG signal features of brain areas activated by a specific motor imagery task are effectively extracted, and effectively extracting the EEG signal features of the brain areas activated by the specific motor imagery task is a key problem to improve a recognition rate. With the multi-lead correlation analysis EEG feature extraction method, firstly multi-lead motor imagery EEG signals are extracted, then a correlation coefficient between every two lead EEG signals is analyzed to obtain a correlation parameter matrix, next a row variance of each correlation parameter matrix, the ratio values of the sum of all the row variances, and natural logarithms of all the row variances are calculated, obtained results are used as characteristic vectors of the EEG signals, and finally the characteristic vectors are input into a classifier to complete classifying recognition of multi-class motor imagery tasks. With the multi-lead correlation analysis EEG feature extraction method, not only can the EEG signal features of the brain areas activated by the specific motor imagery task at the same time can be fully extracted, influences on characteristic parameters can be reduced to a large extent, wherein the influences are caused by EEG signal individual differences, and further the problem that insufficience problem of electrode choosing can be solved.
Description
Technical field
The invention belongs to the EEG Processing field, relate to a kind of EEG feature extraction method, particularly a kind of feature extracting method for multi-lead motion imagination EEG signals.
Background technology
(electroencephalogram is the potential change that is caused by cerebral cortex neural cell group cynapse transmission signal EEG) to EEG signals, can reflect the conscious activity that brain is autonomous or bring out, and is closely related with the action behavior of reality.Be recorded to the electrical activity of human brain from nineteen twenty-nine Germany scientist Hans Berger, people attempt by the identification of EEG signals being understood people's thinking activities always.Be that (brain-computer interface BCI) is considered to an important milestone of human knowledge's brain process for the brain-computer interface of important support with it.BCI does not rely on the participation of muscle and nervus peripheralis, directly realizes the communication between human brain and the computing machine, is one of current international forward position research focus.
Up to the present, people still know little about it to the forming process of human thinking, and the various thinking activities that read the people by the brain electricity are also unrealistic.But EEG signals is used for motion imagination identification aspect, has obtained certain progress.The Pfurtscheller of Austria Graz university and the people such as Wolpaw in U.S. Wadsworth research centre are being undertaken doing a lot of work aspect the motor pattern identification by the motion imagination, studies have shown that the motion imagination causes identical neuron activity with the actual motion meeting in brain master sensorimotor function district, by the EEG signals that the electrode that is placed on the sensorimotor area records, the imagination of can moving pattern-recognition.Ten thousand Bai Kun seminar of University Of Tianjin utilize the vision centralized control
The blocking-up phenomenon of the rhythm and pace of moving things is selected in the EEG signals
The rhythm and pace of moving things is as switch controlling signal, realizes man-machine interaction with wheelchair by the direction switch of opening the closed-eye trigger wheelchair, experimental verification the feasibility of system, point out that simultaneously subjective factor is right
The blockage effects of the rhythm and pace of moving things and neighbourhood noise are still waiting further research to the influence of control performance, need the eye muscle synergy during use.Pfurtscheller leader's research centre studies show that, monolateral limb motion or only be that imagination motion can activate main sensorimotor cortex, the generation of brain offside
With
The ERD of the rhythm and pace of moving things, homonymy produces
With
The ERS of the rhythm and pace of moving things.Combined with virtual reality subsequently, a C4/C5 level spinal cord injury patient can move in virtual streets by enough imagination motion control wheelchairs, and experiment shows that average success ratio is 90%.Japan Tanaka etc. has designed a kind of experimental system based on motion imagination EEG signals control electric wheelchair, and the control wheelchair has correctly moved to target B from target A.Switzerland, Spain and Belgian research team are at the instability characteristics of EEG signals and the shortcoming of current self-adaptation solution method, study asynchronous non-intrusion type BCI and be used for wheelchair control, obtained achievement in research preferably in stable brain electrical feature selection and shared control, experimental result shows virtual, the experimenter can both be by the motion of imagination left hand under the actual environment, related three generic tasks with word of having a rest are controlled wheelchair respectively by asynchronous non-intrusion type BCI interface, advance and turn right motion, the target arrival rate is 100% under the virtual environment, the target arrival rate is 80% under the actual environment, point out that simultaneously the training burden is heavier, nicety of grading has much room for improvement.The cursor that triumphant seminar has carried out based on the motion imagination on Tsing-Hua University's height moves; the rehabilitation supplemental training; robot dog BCI systematic study such as play soccer; in the experiment of imagination kinematic system; 10 experimenters are by imagination right-hand man; the pin motion produces EEG signals system is controlled; experimental result shows that the average accuracy of online and off-line analysis of imagination right-hand man two class classification task is 94.92% and 92.86%; the average accuracy of online and off-line analysis of imagination right-hand man and pin three class classification task is 85% and 79.48%; point out that simultaneously the less thinking task of difference is difficult to extract from the lower EEG signals of spatial resolution, but the kind of increase system identification mission can directly cause the decline of recognition correct rate usually.
Comprehensive domestic and international research finds that the single spontaneous brain electricity that the motion imagination produces does not need the outside stimulus signal, is the source that causes limb motion, by the brain bio-electrical information of eeg signal acquisition, has comprised the control information of the brain motion imagination.Yet also there are some subject matters in motion imagination EEG research: the one, and by the EEG signals that the thinking task is extracted, resolution is lower, particularly for the less motion imagination task of difference; The 2nd, the kind that increases identification mission can directly cause the decline of recognition correct rate.One of key issue that wherein influences discrimination is, from ground unrest by force, at random and extract the corresponding feature of different motion imagination task the feeble computer signals of non-stationary effectively.The researcher adopts various method to extract effective brain electrical feature, as Fourier transform, autoregressive model, power spectrum and adaptive regression model, fourth order cumulant, wavelet transformation, wavelet package transforms, Hilbert-Huang transform, analysis of complexity method, tensor analysis method, public space pattern etc., and then identify different motion imagination tasks, obtained abundant achievement in research.Yet brain electrical feature extracting method is only analyzed few way channel information at present, and the benefit of doing like this is apparent, and required electrode is few, not only shortens setup time, and low volume data needs little information processing cost.Corresponding with it, also there are scholars such as Blankertz, Sannelli, Schroder, Barachant to point out, the a small amount of passage that adopts the nervous physiology priori to select might not produce the result better than full tunnel collection, and electrode is chosen deficiency also can reduce classification accuracy rate.How at different experimenters, the EEG signals feature in a plurality of brains district that complete extraction is activated simultaneously by special exercise imagination task, the present invention's problem of trying hard to solve just.
Though relatively independent function is finished in the different zone of cerebral cortex, but finish a certain specific motion imagination task, participate in the time of the functional areas that need to separate on one or several space, carry out different motion imagination tasks simultaneously, the zone on the motor cortex of activation also is not quite similar.
Summary of the invention
Purpose of the present invention is exactly the deficiency that exists at existing brain electrical feature extracting method, and a kind of brain electrical feature extracting method based on correlation analysis between multi-lead is provided.
For the multi-lead EEG signals, (passage) the corresponding measured zone definitions of electrode of often each being led is a node, and its electrical activity is the some time sequence.At first calculate related coefficient between these time serieses to obtain correlation matrix, calculate then the capable variance of correlation matrix and all row variance and between ratio and natural logarithm thereof, with the proper vector as EEG signals, utilize these features to identify multiclass motion imagination task at last.
In order to realize above purpose, the inventive method mainly may further comprise the steps:
Step (1) is obtained hyperchannel motion imagination EEG signals sample data.At first adopt multi-lead electrode cap collection campaign imagination EEG signals, adopt band-pass filtering method to carry out pre-service then.
Step (2) related coefficient is calculated.Calculate (passage) EEG signals related coefficient between any two of respectively leading according to the time series similarity measure method shown in the formula (1), obtain one
Correlation matrix
,
Wherein,
With
Be passage
With
EEG signals numerical value constantly,
Represent passage
With passage
Between facies relationship numerical value,
Be length of time series,
The port number of EEG signals is gathered in expression.
Step (3) brain electrical feature extracts.On the basis of step (2) correlation matrix, respectively the variance of variance of each row of compute matrix and all row and, calculate the proper vector of EEG signals then according to formula (2)
Wherein,
(
) expression correlation matrix each the row,
The computing of expression natural logarithm,
The computing of expression variance.
Compare with existing motion imagination brain electrical feature extraction algorithm, EEG signals feature in a plurality of brains district that the method that the present invention proposes not only can complete extraction be activated simultaneously by special exercise imagination task, reduce the individual difference of EEG signals to a great extent to the influence of feature extraction parameter, and can overcome the problem that electrode is chosen deficiency, simultaneously simple.
The inventive method can satisfy the requirements for extracting features in the multi-mode identification mission preferably, has broad application prospects at brain-computer interface, cerebral disease diagnostic field.
Description of drawings
Fig. 1 is implementing procedure figure of the present invention.
Embodiment
Describe the brain electrical feature method that the present invention is based on correlation analysis between multi-lead in detail below in conjunction with accompanying drawing, Fig. 1 is implementing procedure figure.
As Fig. 1, the enforcement of the inventive method mainly comprises following step: (1) obtains hyperchannel motion imagination EEG signals sample data, comprises collection and the pre-service of EEG signals under several motion thought experiment normal forms; (2) calculate the EEG signals relative coefficient between any two that respectively leads according to time series similarity measure method; (3) the capable variance of calculating correlation matrix and all capable variances and between ratio and natural logarithm thereof, with the gained result as the distinguishing feature of portraying EEG signals; (4) brain electrical feature input support vector machine classifier is carried out training and testing, finish the classification of multiple motion imagination task.
One by one each step is elaborated below.
Step 1: obtain hyperchannel motion imagination EEG signals sample data
Adopt 40 conduction polar caps in the U.S. Neuro Scan Scan4.3 of the company collecting device imagination process eeg signal acquisition that moves.The experimenter has worn brain electricity cap recoil on request on wheelchair, keeps quite, nature, watches the sight prompting of setting in the experimental situation attentively.Adopt following several motion thought experiment normal form: the right hand control the wheelchair control bar forward, left hand control the wheelchair control bar backward, left foot hop and both hands push boat chair is moved to the left, right crus of diaphragm hop and both hands push boat chair are moved to the left, the corresponding wheelchair controlled motion form of advancing, brake, turn left, turning right also can experimental concrete condition be done suitable correction to the design of experiment model in implementation process respectively.After having gathered data, adopt band-pass filtering method to carry out Signal Pretreatment.
Step 2: related coefficient is calculated
Calculate the EEG signals relative coefficient between any two that respectively leads according to Euclidean distance (Euclidean distance), mahalanobis distance (Mahalanobis distance) equal time sequence similarity measure.The present invention adopts lead similarity between the EEG signals time series of euclidean distance metric two.Calculate the EEG signals related coefficient between any two of respectively leading according to formula (1), obtain one
Correlation matrix
,
Wherein,
With
Be passage
With
EEG signals numerical value constantly,
Represent passage
With passage
Between facies relationship numerical value,
Be length of time series,
The port number of EEG signals is gathered in expression.
Step 3: the brain electrical feature extracts
At the step 2 correlation matrix
The basis on, calculate respectively
In variance of each row and all row variance and, calculate to calculate the proper vector of EEG signals then according to formula (2)
Wherein,
(
) expression correlation matrix each the row,
The computing of expression natural logarithm,
The computing of expression variance.
Step 4: based on the motion imagination classification of task of support vector machine
The brain electrical feature vector that step 3 is obtained carries out training and testing as the input of support vector machine classifier, finishes the classification of multiple motion imagination task.
Claims (1)
1. the brain electrical feature extracting method of correlation analysis between a multi-lead is characterized in that this method comprises the steps:
Step (1) is obtained hyperchannel motion imagination EEG signals sample data, specifically: at first adopt multi-lead electrode cap collection campaign imagination EEG signals, adopt band-pass filtering method to carry out pre-service then;
Step (2) related coefficient is calculated, specifically: calculate the EEG signals related coefficient between any two of respectively leading according to the time series similarity measure method shown in the formula (1), obtain one
Correlation matrix
,
Wherein,
With
Be passage
With
EEG signals numerical value constantly,
Represent passage
With passage
Between facies relationship numerical value,
Be length of time series,
The port number of EEG signals is gathered in expression;
Step (3) brain electrical feature extracts, specifically: on the basis of step (2) correlation matrix, respectively the variance of variance of each row of compute matrix and all row and, calculate the proper vector of EEG signals then according to formula (2)
(2)
Wherein,
Each row of expression correlation matrix,
The computing of expression natural logarithm,
The computing of expression variance.
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| CN105205317A (en) * | 2015-09-10 | 2015-12-30 | 清华大学 | Method and device for reflecting collaboration degree of at least two participants |
| CN105595962A (en) * | 2015-12-22 | 2016-05-25 | 苏州大学 | Simulator circuit |
| CN109171713A (en) * | 2018-06-08 | 2019-01-11 | 杭州电子科技大学 | Upper extremity exercise based on multi-modal signal imagines mode identification method |
| CN109497997A (en) * | 2018-12-10 | 2019-03-22 | 杭州妞诺科技有限公司 | Based on majority according to the seizure detection and early warning system of acquisition |
| CN109846477A (en) * | 2019-01-29 | 2019-06-07 | 北京工业大学 | A kind of brain electricity classification method based on frequency band attention residual error network |
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| CN105205317A (en) * | 2015-09-10 | 2015-12-30 | 清华大学 | Method and device for reflecting collaboration degree of at least two participants |
| CN105205317B (en) * | 2015-09-10 | 2017-12-15 | 清华大学 | A kind of method and equipment for being used to reflect the cooperation degree of at least two participants |
| CN105595962A (en) * | 2015-12-22 | 2016-05-25 | 苏州大学 | Simulator circuit |
| CN105595962B (en) * | 2015-12-22 | 2018-08-07 | 苏州大学 | A kind of similar device circuit |
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| CN109497997A (en) * | 2018-12-10 | 2019-03-22 | 杭州妞诺科技有限公司 | Based on majority according to the seizure detection and early warning system of acquisition |
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