An End-to-End Deep Learning Model for Mental Arithmetic Task Classification from Multi-Channel EEG

Abstract

The development of brain-computer interface (BCI) applications has recently piqued the interest of researchers because it can help physically handicapped people to communicate with their brain electroencephalogram (EEG) signal. The automatic classification of mental workload tasks from multi-channel EEG signal analysis is critical for BCI applications. In this paper, we propose an end-to-end deep learning (DL) model for the classification of the mental arithmetic task (MAT) from multi-channel EEG signals. As an end-to-end DL model, a residual-based temporal attention network (RTA-Net) is developed to achieve optimal performance for MAT classification. We have mainly considered two MAT: before mental arithmetic calculation and during mental arithmetic calculation. The RTA-Net model is validated on a freely available MAT-based EEG dataset. The results show that our proposed model yield the best performance with classification accuracy: 99.32%, F1-score: 99.20%, and Cohen’s Kappa: 98.15%, which defeat the performance of all existing methods for MAT classification. For real-world applications, our automated MAT system is ready to be tested with additional datasets.