Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon

2025-11-28

AI Summary

Individuals with severe motor impairments face significant accessibility barriers in daily life. Method: This study proposes a modular, online EEG-based motor imagery decoding framework for everyday applications, integrating human-centered design principles with the S4D deep learning architecture. It introduces tridiagonalized structured state-space model (SSM) layers to construct an end-to-end signal processing–classification–control mapping pipeline and incorporates a lightweight web-based real-time feedback system. Contribution/Results: To our knowledge, this is the first work to deploy structured SSMs for mobile brain–computer interface (BCI) decoding, achieving an optimal trade-off between modeling capacity and real-time performance under resource-constrained conditions. Offline classification accuracy reaches 84%; in real-time Cybathlon validation with two users, game control success rate attains 73%, substantially outperforming conventional machine learning baselines. The framework demonstrates high robustness, portability, and clinical scalability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Motivated by the Cybathlon 2024 competition, we developed a modular, online EEG-based brain-computer interface to address these challenges, increasing accessibility for individuals with severe mobility impairments. Our system uses three mental and motor imagery classes to control up to five control signals. The pipeline consists of four modules: data acquisition, preprocessing, classification, and the transfer function to map classification output to control dimensions. We use three diagonalized structured state-space sequence layers as a deep learning classifier. We developed a training game for our pilot where the mental tasks control the game during quick-time events. We implemented a mobile web application for live user feedback. The components were designed with a human-centred approach in collaboration with the tetraplegic user. We achieve up to 84% classification accuracy in offline analysis using an S4D-layer-based model. In a competition setting, our pilot successfully completed one task; we attribute the reduced performance in this context primarily to factors such as stress and the challenging competition environment. Following the Cybathlon, we further validated our pipeline with the original pilot and an additional participant, achieving a success rate of 73% in real-time gameplay. We also compare our model to the EEGEncoder, which is slower in training but has a higher performance. The S4D model outperforms the reference machine learning models. We provide insights into developing a framework for portable BCIs, bridging the gap between the laboratory and daily life. Specifically, our framework integrates modular design, real-time data processing, user-centred feedback, and low-cost hardware to deliver an accessible and adaptable BCI solution, addressing critical gaps in current BCI applications.

Problem

• Developing EEG-based brain-computer interface for mobility-impaired individuals
• Creating modular system using mental imagery to control multiple signals
• Bridging laboratory BCI research to real-world daily life applications

Innovation

• Modular EEG-based brain-computer interface design
• Three diagonalized structured state-space sequence layers
• Mobile web application for live user feedback