Motor-imagery EEG signal decoding using multichannel-empirical wavelet transform for brain computer interfaces

Motor-imagery (MI) electroencephalography (EEG) signal decomposition is an emerging technique for improving the performance of brain computer interfaces (BCIs). We proposed a multichannel-empirical wavelet transform (EWT) representation combined with a scattering convolution network (SCN) to efficiently decode the brain activity and extract relevant wave patterns for MI-based BCI. Two different preprocessing steps were tested: the first (PM1) included a bandpass Butterworth filter (1-40 Hz) and the independent component analysis (ICA), the second one (PM2) consisted only of a bandpass Butterworth filter (8-30 Hz). A binary support vector machine (SVM) classifier was used and the performance was evaluated in terms of classification accuracy. The proposed framework was assessed using the BCI competition IV dataset Ita, which contains EEG from 9 healthy subjects. PM1 presented a maximum mean accuracy over all subjects of 82.05% in the classification of the tongue and the left-hand MI tasks. PM2 achieved an average accuracy over all subjects of 88.40% and a standard deviation of 3.01 outperforming other state of the art methods in classifying right-hand and left-hand MI tasks. Finally, we observed that the best channels, intended as the channels holding the highest discrimination power between two MI tasks, were highly subject-specific and thus enabling task-based channel selection is crucial.