Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs

TL;DR

This paper introduces MuseCogNet, a joint learning framework that combines self-supervised and supervised methods to improve cognitive workload decoding using portable EEG devices, addressing non-stationarity issues.

Contribution

It presents a novel neuro-informed joint learning approach that enhances decoding accuracy in portable BCIs by integrating neurophysiological insights with machine learning techniques.

Findings

MuseCogNet outperforms existing methods on Muse dataset

The framework captures robust neurophysiological patterns

It enables effective neurocognitive monitoring in real-world settings

Abstract

Portable and wearable consumer-grade electroencephalography (EEG) devices, like Muse headbands, offer unprecedented mobility for daily brain-computer interface (BCI) applications, including cognitive load detection. However, the exacerbated non-stationarity in portable EEG signals constrains data fidelity and decoding accuracy, creating a fundamental trade-off between portability and performance. To mitigate such limitation, we propose MuseCogNet (Muse-based Cognitive Network), a unified joint learning framework integrating self-supervised and supervised training paradigms. In particular, we introduce an EEG-grounded self-supervised reconstruction loss based on average pooling to capture robust neurophysiological patterns, while cross-entropy loss refines task-specific cognitive discriminants. This joint learning framework resembles the bottom-up and top-down attention in humans, enabling MuseCogNet to significantly outperform state-of-the-art methods on a publicly available Muse dataset and establish an implementable pathway for neurocognitive monitoring in ecological settings.