Research On Classification Method Of EEG Signal In Single Trial

Brain-computer interface(BCI)is a new type of human-computer interaction system that realizes direct communication between humans and computers by analyzing the signals of Electroencephalograph(EEG)that reflects brain function.It has been widely used in military,disability rehabilitation,Disaster relief,entertainment experience and many other fields.In the analysis of EEG signals,Event-Related Potential(ERP)has the advantages of stable latency and constant waveform through multiple trials.It is the main research object in EEG signal classification.However,the real-time BCI system needs to analyze the single trial EEG signal.The biggest difficulty is that the signal-to-noise ratio of single trial is low,and the performance has a lot to do with experimental design,preprocessing,and classification methods.For different types of EEG data,there is currently no systematically comparative analysis of comprehensive pre-processing and feature extraction methods based on single trials.In response to the above problems,this paper uses three mature types on a total of eight EEG datasets including four steady-state visual evoked,three motor imaging and one Parkinson's disease detection.Each data set is processed separately used existing mature preprocessing and feature extraction methods,and the performance of preprocessing and feature extraction methods are compared and analyzed based on the accuracy of single-signal classification,and the three types of EEG signals suitable for single trial classification are summarized.First of all,in terms of preprocessing,the preprocessing of EEG data is related to the research purpose and experimental design.There is no standard process,but there are interactions between different preprocessing steps,and their order also affects the classification results.Based on the EEG signal pre-processing process proposed by the SCCN laboratory at the University of California,San Diego,this paper proposes an improved noise removal method.First,piecewise linear regression analysis is performed on the original signal to remove baseline drift,and then multi-window spectrum is used to power frequency noise suppress,followed by high-pass filtering with a cut-off frequency of 0.1-1Hz,and physiological artifacts are removed using a blind source separation method.For one subject in the steadystate visual evoked dataset one and five subjects in the motor imaging dataset three,based on the data proposed by the SCCN laboratory and processed by the improved preprocessing process in this paper,the combined features of the common spatial pattern and the frequency band were used for classification.The classification accuracy ratio of the four subjects on the improved preprocessing process was compared.The classification accuracy of the four subjects in the improved preprocessing process was improved by about 0.5% compared with the preprocessing method of the SCCN laboratory,which verified the preprocessing process Effectiveness.In terms of feature extraction,this paper analyzes from time domain,frequency domain,space domain and multi-domain.The experimental results show that for steady-state visual evoked EEG signals,the accuracy of classification using only the features with the most discriminative time window in the time domain is higher than the accuracy of classification using features extracted from multi-window spectrum estimation in the frequency domain.The four steady-state visual evoked dataset studied in this paper,the average accuracy is 4%;the accuracy of classification using time-frequency domain features is 1.4% higher than using only time-domain or frequency-domain,it is shown that the multi-domain information complementation is helpful for the classification accuracy of single trial EEG signals.For the EEG signals of motor imagination,this paper proposes an improved CSP method based on selecting a filter based on a scoring function,using this method to extract spatial features for classification can obtain good classification results.The classification accuracy of the three data sets of motor imagination is 1.2% higher on average than using the traditional CSP method;The improved CSP method requires linear filtering and selection of the cutoff frequency,time interval,and a subset of the CSP filter before use.The experimental results show that after selecting the appropriate parameters according to the participants,on the three motion imagination datasets studied in this paper,the classification accuracy of two datasets was improved by about 3% compared with the use of fixed parameters,and the other one did not improve significantly.The reason may be that the data has only three channels.Disease detection EEG is a special type of EEG.The characteristics of different disease manifestations are different.For the EEG data of Parkinson's disease detection in this paper,this paper selects a discriminative time window through cross-validation and select multidomain features for classification according to the appropriate frequency band selected by the participants,the experimental results show that the average classification accuracy of the drug treatment group is 73.8%,and the average classification accuracy of the non-drug treatment group is 74.2%,which the accuracy rate is 0.4% higher than the best classification based on the priori time window and frequency band extraction features;Using the improvedCSP method,results consistent with the time-frequency domain characteristics are achieved on this dataset.In addition,this paper also uses the time-space domain method of Bilinear Discriminant Component Analysis(BDCA).On the steady-state visual evoked dataset 2 and the motion imaging dataset 3,the classification accuracy rate is better than the best available methods about 1% lower.The BDCA method does not require excessive preprocessing,but it takes a long time and is not suitable for real-time BCI systems.Regarding the classifier method,in the absence of special instructions,this paper uses support vector machine(SVM)for classification.When the classification effect is not good,the random forest classifier is used instead.For the steady-state visual evoked dataset 4 that uses the SVM classification effect poorly,using the same feature type,the accuracy of the random forest classifier is 2.8% higher than the SVM.Finally,this paper designs an EEG-based face recognition experiment,collecting and analyzing the EEG signals in the face recognition process of nine normal subjects.First,the EEG signal pre-processing is carried out by using the improved pre-processing procedure in this paper,and then the pre-processed data is analyzed from time domain,frequency domain and space domain,respectively.In the time domain,independent component analysis(ICA)is used to enhance the time domain signal,and then the time domain analysis window is determined,the window is determined by using prior knowledge and by the subject.The experimental results show that the average classification accuracy of the latter is 1% higher than the former;In the frequency domain,the average discriminant frequency of each subject is extracted and using the multi-window spectrum to classify the features of each frequency band for classification,the average accuracy is 68.8%.The classification using only the timedomain features is 8.4% higher on average and 3.3% lower than the classification using the time-frequency domain combined features,which proves that multi-domain complementary information is helpful for the classification of EEG signals in a single trial;in the airspace,first use ICA to enhance the signal in the airspace to improve the signal-to-noise ratio,and then use PCA to reduce the dimension,and then use the improved CSP method to extract features for classification.On the nine subjects in this data set,the average classification accuracy reached 81.9%,which is 5.7% higher than the average using traditional CSP classification,which validates the effectiveness of the improved CSP algorithm for single trial EEG classification.On this basis,a method of training set expansion is adopted,and one test sample of each subject is used as the extended training sample of each participant.Multi-domain features are extracted and classified on the expanded training set,and nine participants the average classification accuracy improved by about 3%.