| The majority of research on human brain-computer interface (BCI) has been performed using electroencephalographic (EEG) recordings. The BCI systems based on spontaneous EEG and distinguishable mental tasks would be a promising system for this kind of BCIs is no need for stimulation equipment and long-term training. The study of the mental task based BCI in this paper mainly included EEG signal preprocessing, EEG feature extraction and classification. The main work and contributions are as follows:Effective blind source separation (BSS) methods were used to remove power line noise, electrooculography (EOG) artifacts and electromyography (EMG) artifacts from EEG signals. EOG artifact removal was performed with independent component analysis (ICA) approach that is effective and widely used in removing EOG artifacts from EEG. Besides, Power line noise was also removed by ICA method. When the six-channel EEG signals were fed as the input of ICA, there were no independent components found to be only related to the power line noises. As we know the noise frequency, we created two-channel power line signals artificially and added them to the input of ICA. In this way, the power line noises were separated from the EEG signals successfully. No studies of the mental task based BCI removed EMG artifacts before. In this paper, a new method for EMG artifact removal in EEG was presented, based on canonical correlation analysis (CCA) and low-pass filtering as a BSS technique. The raw EEG can be seemed as the mixture of EEG and EMG sources that are uncorrelated, thus, CCA can be used to separate the EEG signal and EMG artifacts. EMG artifacts have a low autocorrelation, therefore they were present in the lowest autocorrelated CCA components. However, these components were also found to contain brain activity. Therefore, low-pass filters were used to remove the EMG artifacts in those corresponding CCA components. The EEG signals were reconstructed with the brain activity related CCA components along with the low-pass filter processed CCA components. This new method is able to remove the EMG artifacts as well as keeping the related EEG as intact as possible and is shown to be effective in eliminating EMG contamination.Time-frequency and time-frequency-space feature extraction methods were proposed in this paper. Rhythm and spectra features extracted with frequency domain methods are mainly used in the mental task based BCI. But these methods are based on the hypothesis of stationarity of analyzed EEG signals. For EEG are typical nonstationary, the time-frequency methods are more suitable to analyze EEG. Linear time-frequency analysis and bilinear time-frequency analysis were applied to extract features from the spontaneous EEG during different mental tasks. Short-time Fourier transform (STFT) has no cross-terms,however it's time-frequency concentration is not satisfied. In the solving procecess of STFT, auto-regressive (AR) model was used to replace FFT for spectral estimation. In this way, the time-frequency concentration became better. The STFT based on AR model was more effective for feature extraction. Wigner-Ville distribution (WVD) has good time-frequency concentration, but the cross-term is disturbed. Smoothed pseudo WVD (SPWVD), the improved WVD method, can suppress the cross-terms, thus it can extract EEG feature more accurately. Time-frequency-space method adding space domain features from the multi-channel EEG signals is a valuable idea. For the high dimension features extracted from the multivariable EEG and heavy calculation using this method, spatial decorrelation was performed to reduce the dimension of feature vector and calculation, which improved practicality. Two kinds of classifiers, namely the classifier based on Fisher discriminant analysis (FDA) and Mahalanobis distance (MD) and the classifier based on least squares support vector machine (LS_SVM), were developed in this paper. FDA+MD classifier with low computational complexity is simple and fit for on line application. SVM based on structural risk minimization is a new method for pattern recognition. This algorithm solves practical problems such as over learning, high dimension and local minimum in traditional methods and has very well generalization ability, but the calculation time is long. For this reason, we used LS_SVM for classification because it is more efficient and fit for BCI after transforming the quadratic programming problem into linear equation. Besides, multiple classifiers based on LS_SVM were also proposed.Three experiments were conducted in this study. In each experiment, two-class and multi-class classifications were performed using the EEG data from eight subjects. The effectiveness of the proposed feature extraction methods was studied in the first experiment. The results indicated that the best classification performances were achieved by STFT based on AR model and SPWVD methods. But the method of STFT based on AR model is simpler. Comparison between the two kinds of classifiers was carried out in the second experiment. It is evident that LS_SVM classifiers gave better classification accuracies in two-class classifications and FDA+MD classifiers had advantages on classification accuracy and calculating speed in three-class, four-class and five-class classifications. We proposed a new point of view that the high frequency band of scalp EEG might contain valuable information that may contribute to more accurate mental task classification. This view was verified in the third experiment. We compared the classification results obtained using the features from the high frequency band (40-100 Hz) together with those from the lower frequency bands with the classification results obtained using the features only from the lower frequency bands. Significantly higher classification accuracies were obtained by adding the high frequency band features compared to using the low frequency bands alone, which demonstrated that the information in high frequency components from scalp-recorded EEG is valuable for the mental task based BCI.
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