Exploration of the mental attention mechanisms in motor imagery-based EEG decoding

Brain-Computer Interface (BCI) systems enable direct communication between the brain and external devices, with motor imagery (MI)-based BCIs as a key paradigm. Although decoding neural signals has advanced via machine learning and deep learning, the influence of human factors, especially mental attention on performance remains underexplored.

This study quantitatively investigates how mental attention modulates MI decoding. Specifically, it examines the enhancement of Common Spatial Pattern (CSP) features under high attention and evaluates attention-based data selection as a decoding criterion.

Experimental results demonstrate that applying mental attention as a trial selection strategy (Strategy 2) markedly improves MI decoding performance, yielding an 11.6% increase relative to the baseline accuracy of 61.3% observed without attention. These findings highlight that integrating real-time mental attention monitoring into BCI systems can enhance decoding robustness and stability, paving the way for personalized and context-aware brain-computer interactions in neurorehabilitation, cognitive training, and intelligent assistive technologies.

Prior studies focused largely on algorithmic innovations. In contrast, this work adopts a user-centric perspective, showing that attention-informed trial selection significantly improves performance even within standard CSP-based pipelines.

Incorporating mental attention into decoding frameworks enhances MI-BCI performance. This approach may improve the robustness and user-adaptability of online BCI systems, contributing to more effective and user-friendly neurotechnology.