Wavelet transform based approach for EEG feature selection of motor imagery data for braincomputer interfaces

The Electroencephalogram (EEG) signals recorded during motor imagery (MI) tasks are often used as input in the brain-computer interface (BCI) applications. The adaptation of these EEG signals to control signals depend on the selection of an accurate feature that can provide good classification. The study compared various features of alpha frequency band adapting wavelet-based time-frequency analysis approach. The mean, variance, wavelet energy, Shannon entropy, log energy entropy, kurtosis, and skewness feature were extracted from the EEG signals recorded from symmetrical electrode locations of the motor cortex and their performance accuracies were evaluated using SVM and KNN classification algorithms. In results, the Shannon entropy showed the highest classification accuracy of 86.4 % with SVM classifier. Findings of this study establish Shannon entropy as a potential feature to use in EEG-based BCI analysis along with SVM classification algorithm.