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Feature Extraction And Recognition Of EEG Based On The AR Model

Posted on:2009-03-14Degree:MasterType:Thesis
Country:ChinaCandidate:Q ZouFull Text:PDF
GTID:2144360245982354Subject:Biomedical engineering
Abstract/Summary:PDF Full Text Request
Brain-computer interface (BCI) has wide prospects for brain science, rehabilitation engineering, automatic control, military and biomedical engineering in recent years. It becomes the hot research topic in many areas. Signal processing of EEG is important and difficult.The paper discuss the methods based on the AR model of adaptive algorithm and multi-variable AAR model algorithm to extract feature information which is related with ERD/ERS in EEG Methods to estimate coefficients of the model are introduced. The paper adopts kalman filtering and QR decomposition to estimate the coefficients of AAR and MVAAR models respectively to maximize the real information of EEG Three ways as linear analysis (LDA), classification based on the Mahalanobis distance (MDA) and the method named "leave-one-trial out" are used to classify different tasks. The concepts as mutual information, kappa coefficient, and values of area under the ROC curve (AUC) are introduced to estimate the performance of classification.From the results, we can see MVAAR algorithm made higher accuracy than AAR. MVAAR algorithm reached the higher correct rate than AAR algorithm. AAR model describe non-stationary features of EEG very well. MVAAR algorithm which orders are relatively low need less subjectivity but has high simulation. It realizes multi-channel data input and is more practical. The traditional LDA, MDA and "leave-one-trial out" also reached good results. Although LDA and MDA algorithms are depend on the mean and covariance of the data, when the covariance of two types is great, MDA will perform better than LDA. The principle of "leave-one-trial out" is simple and easy to achieve, but if the experimental data is huge, computation and calculation time will be the problem that we need to consider. For characteristic of person and the test, using one algorithm to do classification for different subject made different results.
Keywords/Search Tags:BCI, EEG, AAR, MVAAR, task classification
PDF Full Text Request
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