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

arXiv: https://arxiv.org/abs/2508.14060 · HTML: https://mlsanity.com/paper/2508.14060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Deep Learning the EEG Manifold for Phonological Categorization from Active Thoughts](https://mlsanity.com/paper/1904.04358/) (2019)
- [Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems](https://mlsanity.com/paper/2012.01749/) (2020)

## Abstract (arXiv)

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.