E-SAT: an extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks

Abstract Objective. Despite substantial advancements in Brain–Computer Interface (BCI), inherent limitations such as extensive training time and high sensitivity to noise largely hinder their rapid development. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances. Approach. Specifically, for E-SAT, ELM is employed both to improve self-attention module generalization ability for feature extraction and to optimize the model’s parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performance on different motor imagery (MI) EEG signals. Main results. Extensive experiments with different datasets, such as BCI Competition III Datasets IV-a, IV-b and BCI Competition IV Datasets 1, 2a, 2b, 3 are conducted to verify the effectiveness of the proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art and existing methods in subject-specific classification on all the datasets. An average classification accuracy of 99.8%, 99.1%, 98.9%, 75.8%, 90.8%, and 95.4% respectively is achieved for each datasets which demonstrate an improvement of 5%–6% compared to the existing methods. In addition, Kruskal Wallis test is performed to demonstrate the statistical significance of E-SAT and the results indicate significant difference with a 95% confidence level. Significance. The experimental results not only show outstanding performance of E-SAT in feature extraction, but also demonstrate that it helps achieve the best results among nine other robust classifiers. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datasets.