NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application

Abstract

Electroencephalogram (EEG)-based applications often require numerous channels to achieve high performance, which limits their widespread use. Various channel selection methods have been proposed to identify minimum EEG channels without compromising performance. However, most methods are limited to specific data paradigms or prediction models. We propose NeuroXAI, a novel method that identifies channel importance regardless of the type of EEG application. It integrates the surrogate analysis algorithm to optimize EEG signals and the data sampling algorithm, which effectively selects from highly voluminous EEG data. The efficacy of channel selection via the proposed method was evaluated through three datasets acquired under different paradigms (motor imagery, steady-state visually evoked potentials, and event-related potentials). On datasets based on these paradigms, NeuroXAI-based channel selection reduced the number of channels while maintaining or enhancing performance. The advantages of the proposed method include enhanced performance, robustness over varying data paradigms and the type of prediction model. The XAI technique enables intuitive interpretation of the constructed model operation, making it applicable in various fields such as model debugging and model interpretation. NeuroXAI has the potential to be used as a practical tool to develop better EEG applications.