A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder

TL;DR

This paper introduces a supervised autoencoder-based framework for subject-independent brain-computer interfaces that reduces calibration time and improves generalization across users, validated on BCI competition data.

Contribution

It presents a novel supervised autoencoder approach for subject-independent MI-BCI, addressing calibration challenges and outperforming traditional methods.

Findings

Outperforms conventional BCI algorithms in 8 of 9 subjects.

Achieves higher mean Kappa values, indicating better classification accuracy.

Validates effectiveness on BCI competition dataset.

Abstract

A calibration procedure is required in motor imagery-based brain-computer interface (MI-BCI) to tune the system for new users. This procedure is time-consuming and prevents na\"ive users from using the system immediately. Developing a subject-independent MI-BCI system to reduce the calibration phase is still challenging due to the subject-dependent characteristics of the MI signals. Many algorithms based on machine learning and deep learning have been developed to extract high-level features from the MI signals to improve the subject-to-subject generalization of a BCI system. However, these methods are based on supervised learning and extract features useful for discriminating various MI signals. Hence, these approaches cannot find the common underlying patterns in the MI signals and their generalization level is limited. This paper proposes a subject-independent MI-BCI based on a supervised autoencoder (SAE) to circumvent the calibration phase. The suggested framework is validated on dataset 2a from BCI competition IV. The simulation results show that our SISAE model outperforms the conventional and widely used BCI algorithms, common spatial and filter bank common spatial patterns, in terms of the mean Kappa value, in eight out of nine subjects.