Classification of EEG signals using Transformer based deep learning and ensemble models

Abstract

A Brain-Computer Interface (BCI) is a communication and control system designed to provide interaction between a user and a computer device. This interaction is based on the brain's electrical signals that are generated when users do specific tasks. Different categories of visual stimuli evoke distinct activation patterns in the human brain. The generated patterns can be recorded with EEG signals for use in BCI applications. Recently, deep learning-based Transformer models have demonstrated significant potential for analyzing diverse data. In this paper, a new Transformer-based model has been presented that extracts temporal and spectral features from EEG signals for classification purposes. The proposed Spectral Transformer model converts the EEG signal to the frequency domain using PSD before applying Transformer models to extract frequency features. Deep ensemble learning models are used to enhance the generalization performance of the final model by combining the benefits of both deep learning models and ensemble learning. The proposed ensemble model combines Temporal and Spectral Transformers to simultaneously utilize the time and frequency features of the signal. The accuracy of 96.1 %, 94.20 %, and 93.60 % are achieved using an ensemble model, Temporal Transformer, and Spectral Transformer, respectively. These results demonstrate the effectiveness of the proposed model for accurately classifying EEG signals in BCI applications.