CN108520239A - A kind of Method of EEG signals classification and system - Google Patents
A kind of Method of EEG signals classification and system Download PDFInfo
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Abstract
The present invention discloses a kind of Method of EEG signals classification and system.The method includes:Obtain EEG signals;EEG signals are pre-processed by independent component analysis;Feature extraction is carried out to pretreated EEG signals by dual-tree complex wavelet transform and cospace pattern;Classification processing is carried out to the EEG signals after feature extraction by Multiple Kernel Learning support vector machines.Using this method and system in the present invention, can be rapidly and efficiently classification difference is carried out to EEG signals.
Description
Technical field
The present invention relates to EEG signals control fields, more particularly to a kind of Method of EEG signals classification and system.
Background technology
Brain-computer interface (brain-computer interface, BCI) is in human brain and computer or other electronic equipments
Between establish it is direct exchange and control channel, idea or manipulation directly can be expressed by brain by this channel people
Equipment, without language or action, this patient that can effectively enhance body handicap exchanges or controls with the external world external
The ability of environment, to improve the quality of life of patient.Brain-computer interface technology is that one kind being related to Neuscience, signal detection, signal
The multi-disciplinary interleaving techniques such as processing, pattern-recognition.
BCI technologies provide a kind of new rehabilitation maneuver for dyskinesia, it directly provides for brain a kind of new
Communication and controlling soil moist, and do not depend on the normal output channel of brain.But technical research is still in initial stage of development, not enough
Maturation, for example, the communication speed of BCI is not also especially desirable, and individual difference is larger.Now, it more and more scholars and grinds
Study carefully mechanism is to the interested major reason of BCI technologies, it may cannot be by speaking or limb action is thought to express for those
Method or the people for operating equipment provide a kind of new communication and control mode.Many traffic accidents are destructive, are had the honor under survival
The people come are much severe disabilities, and even hand, eye, facial muscles, nerve fiber etc. is all damaged.Further more, many diseases
Disease, such as amyotrophic lateral sclerosis, brain paralysis, brain stem apoplexy, spinal cord injury, may all cause neuron death or
Apoptosis causes the damage of nerve pathway, makes brain that can not be exchanged with external environment by normal neuromuscular.This feelings
Under condition, traditional disabled person's ancillary technique is required for some form of muscular movement to participate in due to more or less, using by
Limitation, therefore it is then one kind can that a kind of direct communication channel is established between brain and external environment using BCI technologies
The selection of energy.
The NeuroSky companies in the U.S. develop brain wave remote control mobile phone, pass through the sensor technology and software of NeuroSky
Algorithm derives the psychological condition of user.The product of the said firm's research and development before is chiefly used in medical application, but the cost of sensor
Height is not easy to popularize, and is frequently used in medical field, predominantly detects defective EEG signals.
The neural scientific & technical corporation " Emotiv Systems " in California, USA San Francisco is proposed a entitled " Emotiv
" nerve head is blue " of EPOC ", user need to only play thought aroused in interest, so that it may which to manipulate computer at the moment, but it is expensive, makes
With being limited in scope.
Tsinghua University is dedicated to one machine interface system of real-time brain based on Steady State Visual Evoked Potential, achieves field neck
The first high rate of information throughput, establishes a set of advanced nerve signal process and method for classifying modes.Tsinghua University god at present
The research of brain-computer interface is mainly promoted in terms of two through engineering experiment room:It is filled first, developing the brain-computer interface with practicability
It sets;Second is that research brain-computer interface control during neuromechanism to realize the brain-computer interface algorithm with coadaptation ability,
But due to being real-time brain machine interface system, the rate of information throughput is high so that data processing occupies more resource, and counts
Excessive according to amount, the requirement to hardware is also higher and higher, and corresponding cost can increase.
Whether Shanghai Communications University can be generated by study subject continuous or lasting can be counted as the stable state of motion
The ERD/ERS phenomenons of imagination task are similar to Steady State Visual Evoked Potential (steady-state visual evoked
Potential, SSVEP) or auditory steady state (auditory steady-state responses, ASSR), it devises
A kind of new experimental paradigm.When imagination task is continuous and repeats, phenomenon can also continue to occur.It can be on the phenomenological theory
The Mental imagery task for being construed to repeat causes cortical reaction and enters a kind of " stable state ", and is moved accordingly in cerebral cortex
Control zone generates the lasting phenomenon that desynchronizes, but the Mental imagery of single direction is more, and the time needed is long, and demand takes " steady
State " is more.
Invention content
The object of the present invention is to provide a kind of Method of EEG signals classification and system, come rapidly and efficiently to EEG signals into
Row difference classification.
To achieve the above object, the present invention provides following schemes:
A kind of Method of EEG signals classification, the method includes:
Obtain EEG signals;
EEG signals are pre-processed by independent component analysis;
Feature extraction is carried out to pretreated EEG signals by dual-tree complex wavelet transform and cospace pattern;
Classification processing is carried out to the EEG signals after feature extraction by Multiple Kernel Learning support vector machines.
Optionally, the acquisition EEG signals specifically include:
EEG signals are obtained by mindset.
Optionally, it is described by independent component analysis to EEG signals carry out pretreatment specifically include:
Centralization processing is carried out to the EEG signals;
Whitening processing is carried out to the EEG signals after the centralization, obtains orthogonal matrix Z;
According to the orthogonal matrix Z, an initial weight W is randomly choosed, W is enabled*=E { Zg (WTZ)}-E{g’(WTZ) } W,
Wherein W*For the adjoint matrix of W, g is nonlinear function, a1For constant, 1≤a1≤2;
Judge W=W*/||W*| | value whether be 1, obtain judging result, if judging result indicate W=W*/||W*| |
When value is 1, W is exported, if judging result indicates W=W*/||W*| | value when not being 1, then execute described according to the orthogonal moment
Battle array randomly chooses an initial weight W, enables W*=E { Zg (WTZ)}-E{g’(WTZ) } W, wherein W*For the adjoint matrix of W, g is
Nonlinear function, a1For constant, 1≤a1≤2。
Optionally, described that feature is carried out to pretreated EEG signals by dual-tree complex wavelet transform and cospace pattern
Extraction specifically includes:
The pretreated EEG signals are sampled;
Even numbers complex wavelet transform decomposition is carried out to the EEG signals after the sampling, chooses the frequency of δ waves, θ waves, α waves and β waves
The corresponding frequency range of rate range, and the δ waves, θ waves, α waves and β waves are reconstructed;
To the frequency range progress cospace Pattern Filter of δ waves, θ waves, α waves and β waves after the reconstruct, and then pre-processed
The feature vector of EEG signals afterwards.
Optionally, it is described to the pretreated EEG signals carry out sampling specifically include:
According to frequency fsPretreated EEG signals are sampled, fs=30Hz2l, it is small that wherein l indicates that even numbers is answered
The number of plies in wave conversion.
The present invention still further provides a kind of eeg signal classification system, the system comprises:
Acquisition module, for obtaining EEG signals;
Preprocessing module pre-processes EEG signals for passing through independent component analysis;
Characteristic extracting module, for by dual-tree complex wavelet transform and cospace pattern to pretreated EEG signals into
Row feature extraction;
Sort module, for being carried out at classification to the EEG signals after feature extraction by Multiple Kernel Learning support vector machines
Reason.
Optionally, which is characterized in that the preprocessing module specifically includes:
Centralization processing unit, for carrying out centralization processing to the EEG signals;
Whitening processing unit obtains orthogonal matrix Z for carrying out whitening processing to the EEG signals after the centralization;
Initial weight acquiring unit, for according to the orthogonal matrix Z, randomly choosing an initial weight W, enabling W*=E
{Zg(WTZ)}-E{g’(WTZ) } W, wherein W*For the adjoint matrix of W, g is nonlinear function, a1For constant, 1≤a1≤2;
Judging unit, for judging W=W*/||W*| | value whether be 1, obtain judging result;
Output unit, for indicating W=W when judging result*/||W*| | value be 1 when, export W;
Execution unit, for indicating W=W according to judging result*/||W*| | value when not being 1, then execute described according to institute
Orthogonal matrix is stated, an initial weight W is randomly choosed, enables W*=E { Zg (WTZ)}-E{g’(WTZ) } W, wherein W*For the adjoint of W
Matrix, g are nonlinear function, a1For constant, 1≤a1≤2。
Optionally, the characteristic extracting module specifically includes:
Sampling unit, for being sampled to the pretreated EEG signals;
Even numbers complex wavelet transform resolving cell, for carrying out even numbers complex wavelet transform decomposition to the EEG signals after sampling,
Choose the corresponding frequency range of frequency range of δ waves, θ waves, α waves and β waves;
Reconfiguration unit, for the δ waves, θ waves, α waves and β waves to be reconstructed;
Filter unit is used for the frequency range progress cospace Pattern Filter to δ waves, θ waves, α waves and β waves after the reconstruct,
And then obtain the feature vector of pretreated EEG signals.
According to specific embodiment provided by the invention, the invention discloses following technique effects:
EEG signals are pre-processed by using independent component analysis in the present invention, it is more quickly steady.
Carrying out feature extraction to pretreated EEG signals by using even numbers complex wavelet transform and cospace pattern can
More effectively to reduce the redundancy that the EEG signals frequency unrelated to Mental imagery and some uncorrelated or noise pollutions provide
The influences such as information so that the characteristic value gap extracted is big as far as possible, helps to distinguish every type games imagination task.
It can be classified with the simplified mode by using the multitask classification of Multiple Kernel Learning support vector machines, can be rapidly and efficiently
Classification difference is carried out to EEG signals.
Description of the drawings
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the present invention
Example, for those of ordinary skill in the art, without having to pay creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is Method of EEG signals classification flow chart of the embodiment of the present invention;
Fig. 2 is eeg signal classification system structure diagram of the embodiment of the present invention.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The object of the present invention is to provide a kind of Method of EEG signals classification and system, come rapidly and efficiently to EEG signals into
Row difference classification.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, below in conjunction with the accompanying drawings and specific real
Applying mode, the present invention is described in further detail.
Fig. 1 is Method of EEG signals classification flow chart of the embodiment of the present invention, as shown in Figure 1, the method includes:
Step 101:Obtain EEG signals;
Step 102:EEG signals are pre-processed by independent component analysis;
Step 103:Feature is carried out by dual-tree complex wavelet transform and cospace pattern to pretreated EEG signals to carry
It takes;
Step 104:Classification processing is carried out to the EEG signals after feature extraction by Multiple Kernel Learning support vector machines.
It describes in detail below to each step:
Specifically, obtaining EEG signals in the step 101, obtained by mindset.
Mindset is derived from brain-computer interface technology years of researches achievement.People is under different psychological conditions, brain wave meeting
Different wave characters is shown, brain-computer interface technology decodes the idea of wearer with these features, to realize
Acquisition to EEG signals.
The step 102 pre-processes EEG signals by independent component analysis, specifically includes:
Step 1021:Centralization processing is carried out to the EEG signals so that the mean value of EEG signals is 0;
Step 1022:Whitening processing is carried out to the EEG signals after the centralization, obtains orthogonal matrix Z;
Step 1023:According to the orthogonal matrix Z, an initial weight W is randomly choosed, W is enabled*=E { Zg (WTZ)}-E{g’
(WTZ) } W, wherein W*For the adjoint matrix of W, g is nonlinear function, a1For constant, 1≤a1≤2;Wherein, the dimension of W matrixes with
The dimension of matrix Z is identical, and is the similar matrix of matrix Z;
Step 1024:Judge W=W*/||W*| | value whether be 1, obtain judging result, if judging result indicate W=W*/
||W*| | value be 1 when, export W, if judging result indicate W=W*/||W*| | value when not being 1, then execute described in the basis
Orthogonal matrix randomly chooses an initial weight W, enables W*=E { Zg (WTZ)}-E{g’(WTZ) } W, wherein W*For the adjoint matrix of W
Battle array, g is nonlinear function, a1For constant, 1≤a1≤2。
Wherein W=W*/||W*| | it is Rule of judgment, works as W=W*/||W*| | value be 1 when, then illustrate to restrain, output at this time
W matrixes, if W=W*/||W*| | value be 1 when, then illustrate not restrain, choose W again at this time, until convergence.
Specifically, g is nonlinear function, g=tanh (a can be chosen1, y) or g=y exp (- y2/ 2) or g=y3。
The step 103 carries out pretreated EEG signals by dual-tree complex wavelet transform and cospace pattern special
Sign extraction, specifically includes:
Step 1031:The pretreated EEG signals are sampled;
Step 1032:Even numbers complex wavelet transform decomposition is carried out to the EEG signals after the sampling, chooses δ waves, θ waves, α waves
Frequency range corresponding with the frequency range of β waves, and the δ waves, θ waves, α waves and β waves are reconstructed;Wherein in frequency fsUnder to pre-
Treated, and EEG signals are sampled, fs=30Hz2l, wherein l indicate even numbers complex wavelet transform in the number of plies.
Step 1033:To the frequency range progress cospace Pattern Filter of δ waves, θ waves, α waves and β waves after the reconstruct, in turn
Obtain the feature vector of pretreated EEG signals.
Fig. 2 is eeg signal classification system structure diagram of the embodiment of the present invention, as shown in Fig. 2, the system comprises:
Acquisition module 201, for obtaining EEG signals;
Preprocessing module 202 pre-processes EEG signals for passing through independent component analysis;
Characteristic extracting module 203, for passing through dual-tree complex wavelet transform and cospace pattern to pretreated brain telecommunications
Number carry out feature extraction;
Sort module 204, for being classified to the EEG signals after feature extraction by Multiple Kernel Learning support vector machines
Processing.
Specifically, the preprocessing module specifically includes:
Centralization processing unit 2021, for carrying out centralization processing to the EEG signals;
Whitening processing unit 2022 obtains orthogonal moment for carrying out whitening processing to the EEG signals after the centralization
Battle array Z;
Initial weight acquiring unit 2023, for according to the orthogonal matrix Z, randomly choosing an initial weight W, enabling W*
=E { Zg (WTZ)}-E{g’(WTZ) } W, wherein W* are the adjoint matrix of W, and g is nonlinear function, a1For constant, 1≤a1≤2;
Judging unit 2024, for judging W=W*/||W*| | value whether be 1, obtain judging result;
Output unit 2025, for indicating W=W when judging result*/||W*| | value be 1 when, export W;
Execution unit 2026, for indicating W=W according to judging result*/||W*| | value when not being 1, then execute described
According to the orthogonal matrix, an initial weight W is randomly choosed, W is enabled*=E { Zg (WTZ)}-E{g’(WTZ) } W, wherein W*For W's
Adjoint matrix, g are nonlinear function, a1For constant, 1≤a1≤2。
Specifically, the characteristic extracting module specifically includes:
Sampling unit 2031, for being sampled to the pretreated EEG signals;
Even numbers complex wavelet transform resolving cell 2032, for carrying out even numbers complex wavelet transform point to the EEG signals after sampling
Solution chooses the corresponding frequency range of frequency range of δ waves, θ waves, α waves and β waves;
Reconfiguration unit 2033, for the δ waves, θ waves, α waves and β waves to be reconstructed;
Filter unit 2034, for the frequency range progress cospace pattern filter to δ waves, θ waves, α waves and β waves after the reconstruct
Wave, and then obtain the feature vector of pretreated EEG signals.
Each embodiment is described by the way of progressive in this specification, the highlights of each of the examples are with other
The difference of embodiment, just to refer each other for identical similar portion between each embodiment.
Principle and implementation of the present invention are described for specific case used herein, and above example is said
The bright method and its core concept for being merely used to help understand the present invention;Meanwhile for those of ordinary skill in the art, foundation
The thought of the present invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (8)
1. a kind of Method of EEG signals classification, which is characterized in that the method includes:
Obtain EEG signals;
EEG signals are pre-processed by independent component analysis;
Feature extraction is carried out to pretreated EEG signals by dual-tree complex wavelet transform and cospace pattern;
Classification processing is carried out to the EEG signals after feature extraction by Multiple Kernel Learning support vector machines.
2. a kind of Method of EEG signals classification according to claim 1, which is characterized in that the acquisition EEG signals are specific
Including:
EEG signals are obtained by mindset.
3. a kind of Method of EEG signals classification according to claim 1, which is characterized in that described to pass through independent component analysis
Pretreatment is carried out to EEG signals to specifically include:
Centralization processing is carried out to the EEG signals;
Whitening processing is carried out to the EEG signals after the centralization, obtains orthogonal matrix Z;
According to the orthogonal matrix Z, an initial weight W is randomly choosed, W is enabled*=E { Zg (WTZ)}-E{g’(WTZ) } W, wherein W*
For the adjoint matrix of W, g is nonlinear function, a1For constant, 1≤a1≤2;
Judge W=W*/||W*| | value whether be 1, obtain judging result, if judging result indicate W=W*/||W*| | value be 1
When, W is exported, if judging result indicates W=W*/||W*| | value when not being 1, then execute it is described according to the orthogonal matrix, at random
An initial weight W is selected, W is enabled*=E { Zg (WTZ)}-E{g,(WTZ) } W, wherein W*For the adjoint matrix of W, g is non-linear letter
Number, a1For constant, 1≤a1≤2。
4. a kind of Method of EEG signals classification according to claim 1, which is characterized in that described to be become by dual-tree complex wavelet
It changes and the progress feature extraction of pretreated EEG signals is specifically included with cospace pattern:
The pretreated EEG signals are sampled;
Even numbers complex wavelet transform decomposition is carried out to the EEG signals after the sampling, chooses the frequency model of δ waves, θ waves, α waves and β waves
Corresponding frequency range is enclosed, and the δ waves, θ waves, α waves and β waves are reconstructed;
To the frequency range progress cospace Pattern Filter of δ waves, θ waves, α waves and β waves after the reconstruct, and then obtain pretreated
The feature vector of EEG signals.
5. a kind of Method of EEG signals classification according to claim 4, which is characterized in that described to described pretreated
EEG signals carry out sampling and specifically include:
According to frequency fsPretreated EEG signals are sampled, fs=30Hz2l, wherein l expression even numbers Phase information changes
The number of plies in changing.
6. a kind of eeg signal classification system, which is characterized in that the system comprises:
Acquisition module, for obtaining EEG signals;
Preprocessing module pre-processes EEG signals for passing through independent component analysis;
Characteristic extracting module, it is special for being carried out to pretreated EEG signals by dual-tree complex wavelet transform and cospace pattern
Sign extraction;
Sort module, for carrying out classification processing to the EEG signals after feature extraction by Multiple Kernel Learning support vector machines.
7. a kind of eeg signal classification system according to claim 6, which is characterized in that the preprocessing module is specifically wrapped
It includes:
Centralization processing unit, for carrying out centralization processing to the EEG signals;
Whitening processing unit obtains orthogonal matrix Z for carrying out whitening processing to the EEG signals after the centralization;
Initial weight acquiring unit, for according to the orthogonal matrix Z, randomly choosing an initial weight W, enabling W*=E { Zg
(WTZ)}-E{g’(WTZ) } W, wherein W*For the adjoint matrix of W, g is nonlinear function, a1For constant, 1≤a1≤2;
Judging unit, for judging W=W*/||W*| | value whether be 1, obtain judging result;
Output unit, for indicating W=W when judging result*/||W*| | value be 1 when, export W;
Execution unit, for indicating W=W according to judging result*/||W*| | value when not being 1, then execute it is described according to it is described just
Matrix is handed over, an initial weight W is randomly choosed, enables W*=E { Zg (WTZ)}-E{g,(WTZ) } W, wherein W*For the adjoint matrix of W,
G is nonlinear function, a1For constant, 1≤a1≤2。
8. a kind of eeg signal classification system according to claim 6, which is characterized in that the characteristic extracting module is specific
Including:
Sampling unit, for being sampled to the pretreated EEG signals;
Even numbers complex wavelet transform resolving cell chooses δ for carrying out even numbers complex wavelet transform decomposition to the EEG signals after sampling
The corresponding frequency range of frequency range of wave, θ waves, α waves and β waves;
Reconfiguration unit, for the δ waves, θ waves, α waves and β waves to be reconstructed;
Filter unit, for the frequency range progress cospace Pattern Filter to δ waves, θ waves, α waves and β waves after the reconstruct, in turn
Obtain the feature vector of pretreated EEG signals.
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