Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912544938000384 |
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| author | Pandey, Kartik Balasubramanian, Arun Samanta, Debasis |
| author_facet | Pandey, Kartik Balasubramanian, Arun Samanta, Debasis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14060 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach Pandey, Kartik Balasubramanian, Arun Samanta, Debasis Neurons and Cognition Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning Signal Processing 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. |
| title | Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach |
| topic | Neurons and Cognition Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2508.14060 |