Recognition Of Silently Spoken Word From Eeg Signals Using Dense Attention Network (DAN)

In this paper, we propose a method for recognizing silently spoken words from electroencephalogram (EEG) signals using a Dense Attention Network (DAN). The proposed network learns features from the EEG data by applying the self-attention mechanism on temporal, spectral, and spatial (electrodes) dimensions. We examined the effectiveness of the proposed network in extracting spatio-spectro-temporal in-formation from EEG signals and provide a network for recognition of silently spoken words. The DAN achieved a recognition rate of 80.7% in leave-trials-out (LTO) and 75.1% in leave-subject-out (LSO) cross validation methods. In a direct comparison with other methods, the DAN outperformed other existing techniques in recognition of silently spoken words.