Cognitive Load Estimation Using Brain Foundation Models and Interpretability for BCIs

TL;DR

This paper introduces a novel approach using Brain Foundation Models for real-time EEG-based cognitive load estimation in BCIs, emphasizing interpretability and improved accuracy.

Contribution

It adapts large pre-trained neural networks for EEG analysis, demonstrating enhanced accuracy and interpretability in cognitive load estimation.

Findings

BFMs improve accuracy over state-of-the-art methods.

Partition SHAP reveals prefrontal regions as key for cognitive control.

Longitudinal trends indicate learning progression.

Abstract

Accurately monitoring cognitive load in real time is critical for Brain-Computer Interfaces (BCIs) that adapt to user engagement and support personalized learning. Electroencephalography (EEG) offers a non-invasive, cost-effective modality for capturing neural activity, though traditional methods often struggle with cross-subject variability and task-specific preprocessing. We propose leveraging Brain Foundation Models (BFMs), large pre-trained neural networks, to extract generalizable EEG features for cognitive load estimation. We adapt BFMs for long-term EEG monitoring and show that fine-tuning a small subset of layers yields improved accuracy over the state-of-the-art. Despite their scale, BFMs allow for real-time inference with a longer context window. To address often-overlooked interpretability challenges, we apply Partition SHAP (SHapley Additive exPlanations) to quantify feature importance. Our findings reveal consistent emphasis on prefrontal regions linked to cognitive control, while longitudinal trends suggest learning progression. These results position BFMs as efficient and interpretable tools for continuous cognitive load monitoring in real-world BCIs.