  Common spatial pattern (CSP) is a popular feature extraction method for
electroencephalogram (EEG) motor imagery (MI). This study modifies the
conventional CSP algorithm to improve the multi-class MI classification
accuracy and ensure the computation process is efficient. The EEG MI data is
gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a
bandpass filter and a time-frequency analysis are performed for each experiment
trial. Then, the optimal EEG signals for every experiment trials are selected
based on the signal energy for CSP feature extraction. In the end, the
extracted features are classified by three classifiers, linear discriminant
analysis (LDA), na\"ive Bayes (NVB), and support vector machine (SVM), in
parallel for classification accuracy comparison. The experiment results show
the proposed algorithm average computation time is 37.22% less than the FBCSP
(1st winner in the BCI Competition IV) and 4.98% longer than the conventional
CSP method. For the classification rate, the proposed algorithm kappa value
achieved 2nd highest compared with the top 3 winners in BCI Competition IV.
