Scattering convolutional network based predictive model for cognitive activity of brain using empirical wavelet decomposition

Abstract

Understanding the cognitive activity of brain is a challenging task in brain computer interface (BCI) applications. This work aims at exploring the capability of empirical wavelet transform in decoding the brain wave pattern acquired in response to a thought process and visual stimuli. Empirical wavelet transform (EWT), when combined with the wavelet scattering coefficients is found to efficiently decode the brain wave using recurrent neural network (RNN) based classifier. Electroencephalogram (EEG) and magnetoencephalogram (MEG) are the two modalities considered in this work. The proposed framework is assessed using three different RNN architectures namely long short term memory (LSTM), bi-directional long short term memory (Bi-LSTM), gated recurrent units (GRU). The experimental results show that wavelet scattering coefficients extracted from the dominant mode of EWT decomposition record better performance of 90.23 % and 84.25 % for EEG and MEG signals using GRU as classifier. Furthermore, the wavelet scattering network which involves no learning process achieves better classification at reduced time and computational complexities.