STIT-Net- A Wavelet based Convolutional Transformer Model for Motor Imagery EEG Signal Classification in the Sensorimotor Bands.

Abstract

Motor Imagery (MI) electroencephalographic (EEG) signal classification is a pioneer research branch essential for mobility rehabilitation. This paper proposes an end-to-end hybrid deep network "Spatio Temporal Inception Transformer Network (STIT-Net)" model for MI classification. Discrete Wavelet Transform (DWT) is used to derive the alpha (8-13) Hz and beta (13-30) Hz EEG sub bands which are dominant during motor tasks to enhance the performance of the proposed work. STIT-Net employs spatial and temporal convolutions to capture spatial dependencies and temporal information and an inception block with three parallel convolutions extracts multi-level features. Then the transformer encoder with self-attention mechanism highlights the similar task. The proposed model improves the classification of the Physionet EEG motor imagery dataset with an average accuracy of 93.52% and 95.70% for binary class in the alpha and beta bands respectively, and 85.26% and 87.34% for three class, for four class 81.95% and 82.66% were obtained in the alpha and beta band respective EEG based motor signals which is better compared to the results available in the literature. The proposed methodology is further evaluated on other motor imagery datasets, both for subject-independent and cross-subject conditions, to assess the performance of the model.