LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learning

TL;DR

This paper introduces LGL-BCI, a geometric deep learning-based brain-computer interface that achieves high accuracy and efficiency in motor-imagery EEG classification, addressing variability and complexity challenges.

Contribution

The study presents a novel lightweight geometric deep learning architecture with EEG channel selection and lossless transformation, improving speed and accuracy over existing methods.

Findings

Achieved 82.54% accuracy on real-world EEG datasets.

Reduced model parameters to 64.9K, enhancing computational efficiency.

Demonstrated robustness across multiple EEG devices and datasets.

Abstract

Brain--computer interfaces are groundbreaking technology whereby brain signals are used to control external devices. Despite some advances in recent years, electroencephalogram (EEG)-based motor-imagery tasks face challenges, such as amplitude and phase variability and complex spatial correlations, with a need for smaller models and faster inference. In this study, we develop a prototype, called the Lightweight Geometric Learning Brain--Computer Interface (LGL-BCI), which uses our customized geometric deep learning architecture for swift model inference without sacrificing accuracy. LGL-BCI contains an EEG channel selection module via a feature decomposition algorithm to reduce the dimensionality of a symmetric positive definite matrix, providing adaptiveness among the continuously changing EEG signal. Meanwhile, a built-in lossless transformation helps boost the inference speed. The performance of our solution was evaluated using two real-world EEG devices and two public EEG datasets. LGL-BCI demonstrated significant improvements, achieving an accuracy of 82.54% compared to 62.22% for the state-of-the-art approach. Furthermore, LGL-BCI uses fewer parameters (64.9K vs. 183.7K), highlighting its computational efficiency. These findings underscore both the superior accuracy and computational efficiency of LGL-BCI, demonstrating the feasibility and robustness of geometric deep learning in motor-imagery brain--computer interface applications.