On Channel Selection for EEG-Based Mental Workload Classification

Electroencephalogram (EEG) is a non-invasive technology with high temporal resolution, widely used in Brain-Computer Interfaces (BCIs) for mental workload (MWL) classification. However, numerous EEG channels in current devices can make them bulky, uncomfortable, and time-consuming to operate in real-life scenarios. A Riemannian geometry approach has gained attention for channel selection to address this issue. In particular, Riemannian geometry employs covariance matrices of EEG signals to identify the optimal set of EEG channels, given a specific covariance estimator and desired channel number. However, previous studies have not thoroughly assessed the limitations of various covariance estimators, which may influence the analysis results. In this study, we aim to investigate the impact of different covariance estimators, namely Empirical Covariance (EC), Shrunk Covariance (SC), Ledoit-Wolf (LW), and Oracle Approximating Shrinkage (OAS), along with the influence of channel numbers on the process of EEG channel selection. We also examine the performance of selected channels using diverse deep learning models, namely Stacked Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (BGRU), and BGRU-GRU models, using a publicly available MWL EEG dataset. Our findings show that although no universally optimal channel number exists, employing as few as four channels can achieve an accuracy of 0.940 (±0.036), enhancing practicality for real-world applications. In addition, we discover that the BGRU model, when combined with OAS covariance estimators and a 32-channel configuration, demonstrates superior performance in MWL classification tasks compared to other estimator combinations. Indeed, this study provides insights into the effectiveness of various covariance estimators and the optimal channel subsets for highly accurate MWL classification. These findings can potentially advance the development of EEG-based BCI applications.