Electroencephalogram (EEG) signal classification for brain–computer interface using discrete wavelet transform (DWT)

This work proposes classification of two-class motor imagery electroencephalogram signals using different automated machine learning algorithms. Here data are decomposed into various frequency bands identified by wavelet transform and will span the range of 0–30 Hz.

Statistical measures will be applied to these frequency bands to identify features that will subsequently be used to train the classifiers. Further, the assessment parameters such as SNR, mean, SD and entropy are calculated to analyze the performance of the proposed work.

The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.

The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.