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IMPLEMENTATION OF EEG SIGNAL PROCESSING AND DECODING FOR TWO-CLASS MOTOR IMAGERY DATA

BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2019年第4期31卷

作者： Al-Faiz, Mohammed Z. Al-Hamadani, Ammar A. Al Farahidi Univ Dept Univ Presidency Baghdad Iraq Al Iraqia Univ Dept Network Engn Baghdad Iraq

This work decodes two-class motor imagery (MI) based on four main processing steps: (i) Raw electroencephalographic (EEG) signal is decomposed to single trials and spatial filters are estimated for each trial by common spatial filtering (csp) method;(ii) features are extracted by taking the log transformation (normal distribution) of the spatially filtered EEG signal;(iii) optimal channel selection algorithm is proposed to reduce the number of EEG channels, such approach is regarded as key technological advantage in the implementation of brain-computer interface (BCI) to reduce the system processing time;(iv) finally, support vector machine (SVM) is employed to discriminate two classes of left and right hand MI. Two variations of SVM were proposed: polynomial function kernel and radial-based function RBF kernel. The results revealed that csp succeeded in removing the strong correlation bound between the EEG samples by maximizing the variance of class 2 samples while minimizing the variance of class 1 samples. The channel selection algorithm achieved its goal to reduce the data dimension by selecting two channels out of three having the lowest variance entropies of 0.239 and 0.261 for channel 1 and channel 2, respectively. The features vector was divided into 80% train and 20% test with five-fold cross validation. The classification performance of SVM-polynomial kernel was 87.86% while it is 95.72% for SVM-RBF kernel as average accuracy of five-folds for both. Thus SVM-RBF is superior to SVM-Poly in the proposed framework.

关键词： Brain-computer-interface Support vector machine common spatial pattern csp Optimal channel selection

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