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arXiv · 2508.14060

Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach

Kartik Pandey·Arun Balasubramanian·Debasis Samanta
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Abstract

Electroencephalogram (EEG) signals have gained widespread adoption in brain-computer interface (BCI) applications due to their non-invasive, low-cost, and relatively simple acquisition process. The demand for higher spatial resolution, particularly in clinical settings, has led to the development of high-density electrode arrays. However, increasing the number of channels introduces challenges such as cross-channel interference and computational overhead. To address these issues, modern BCI systems often employ channel selection algorithms. Existing methods, however, are typically task-specific and require re-optimization for each new application. This work proposes a task-agnostic channel selection method, Activity Coefficient-based Channel Selection (ACCS), which uses a novel metric called the Channel Activity Coefficient (CAC) to quantify channel utility based on activity levels. By selecting the top 16 channels ranked by CAC, ACCS achieves up to 34.97% improvement in multi-class classification accuracy. Unlike traditional approaches, ACCS identifies a reusable set of informative channels independent of the downstream task or model, making it highly adaptable for diverse EEG-based applications.

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Kartik Pandey, Arun Balasubramanian, Debasis Samanta. 2025-08-10. Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach. https://arxiv.org/abs/2508.14060

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