Activity Coefficient-based Channel Selection for Electroencephalogram: A Task-Independent Approach
This addresses the need for adaptable channel selection in EEG applications, reducing cross-channel interference and computational overhead without requiring task-specific re-optimization, though it is incremental as it builds on existing channel selection methods.
The paper tackles the problem of channel selection in EEG-based brain-computer interfaces by proposing a task-agnostic method called Activity Coefficient-based Channel Selection (ACCS), which achieves up to 34.97% improvement in multi-class classification accuracy by selecting the top 16 channels based on a novel metric.
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.