Motor Activity Recognition Using Eeg Data and Ensemble of Stacked BLSTM-LSTM Network and Transformer Model

With the rapid development of brain-computer interfaces, the number of applications based on this technology is increasing rapidly. This work proposes a Stacked BLSTM-LSTM, EEG-Transformer, and their ensemble network to predict real-life motor activities of individuals using EEG (ElectroEncephalo-Gram) data. A 32 electrode gel-based EEG recording device has been used to record brain signals from 20 subjects while performing 17 commonly used day-to-day motor activities. The stacked BLSTM-LSTM and EEG Transformer networks predicted the activities with an accuracy of 97.9%, 96.7%, respectively. The ensemble improved the classification accuracy further to 98.5%, which is a considerable improvement over the existing state-of-the-art methods. This study also reveals that raw and delta band frequencies are better in predicting the activities than other frequency bands of the EEG signals. Motor activity recognition has several applications, including rehabilitation, healthcare, gaming, and preventing loss of lives during mitigation of fires, diffusion of bombs, etc., via imitation robots.