提出一种不依赖任务的脑电通道选择方法，提升分类准确率。
Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
- 用活动系数衡量通道重要性，自动筛选最优通道
- 选前16个通道使多分类准确率最高提升34.97%
- 结果与任务无关，可复用于多种脑电应用
脑电图（EEG）因非侵入性、低成本和易获取，在脑机接口（BCI）中广泛应用。高密度电极阵列虽提升空间分辨率，却带来通道间干扰和计算负担。现有通道选择方法多依赖具体任务，需为每项应用重新优化。本文提出一种任务无关的通道选择方法——基于活动系数的通道选择（ACCS），引入通道活动系数（CAC）量化通道信息量。通过选取CAC值最高的16个通道，ACCS在多分类任务中实现最高34.97%的准确率提升。该方法识别出与下游任务无关的通用信息通道，适用于多种脑电应用，具有高度可迁移性。
原文摘要 · Abstract (English)
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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