Towards Optimising EEG Decoding using Post-hoc Explanations and Domain Knowledge

TL;DR

This paper explores how post-hoc explanations and domain knowledge can improve EEG decoding in BCIs, emphasizing neurophysiological validation over mere accuracy metrics for more trustworthy models.

Contribution

It introduces a framework combining GradCAM explanations with domain knowledge to validate EEG decoding models, enhancing interpretability and neurophysiological relevance.

Findings

Models trained on all channels achieve 72.60% accuracy.

Channel relevance differs neurophysiologically between models.

Neurophysiological validation is crucial for trustworthy BCI models.

Abstract

Decoding EEG during motor imagery is pivotal for the Brain-Computer Interface (BCI) system, influencing its overall performance significantly. As end-to-end data-driven learning methods advance, the challenge lies in balancing model complexity with the need for human interpretability and trust. Despite strides in EEG-based BCIs, challenges like artefacts and low signal-to-noise ratio emphasise the ongoing importance of model transparency. This work proposes using post-hoc explanations to interpret model outcomes and validate them against domain knowledge. Leveraging the GradCAM post-hoc explanation technique on the motor imagery dataset, this work demonstrates that relying solely on accuracy metrics may be inadequate to ensure BCI performance and acceptability. A model trained using all EEG channels of the dataset achieves 72.60% accuracy, while a model trained with motor-imagery/movement-relevant channel data has a statistically insignificant decrease of 1.75%. However, the relevant features for both are very different based on neurophysiological facts. This work demonstrates that integrating domain-specific knowledge with XAI techniques emerges as a promising paradigm for validating the neurophysiological basis of model outcomes in BCIs. Our results reveal the significance of neurophysiological validation in evaluating BCI performance, highlighting the potential risks of exclusively relying on performance metrics when selecting models for dependable and transparent BCIs.