Informing EEG-Based Error Decoding With Explainable AI

Abstract

Human cognition involves intricate neural processes for error perception and correction. These processes are crucial in error-monitoring processes such as feedback, learning, control, and decision-making. We present a complete workflow using explainable artificial intelligence (XAI) to guide the feature extraction of electroencephalographic (EEG) signals in a classification task with error-related brain responses. The identification of relevant channels for classification problems has practical relevance, as a dense electrode setup reduces the usability of brain-computer interfaces (BCIs). Specialists can inspect and select seemingly important sensors based on knowledge of brain regions and neural patterns. However, reduced configurations often harm accurate model performance. The contribution of this work lies in demonstrating that XAI can be used to inform the extraction of relevant temporal and spatial information with a strong connection to machine learning model sensitivity. We employed a local and a global XAI method to i) evaluate consistency with expert knowledge, ii) identify relevant time points for asynchronous error decoding, and iii) systematically reduce the error decoding EEG setup. This advances the integration of XAI in neuroscience, thus contributing to the design of practical BCIs.