Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
This work addresses the trade-off between portability and performance in consumer-grade BCIs for cognitive load detection, offering an incremental improvement for applications in ecological neurocognitive monitoring.
The paper tackled the problem of decoding cognitive workload from portable EEG signals, which suffer from non-stationarity, by proposing MuseCogNet, a joint learning framework that integrates self-supervised and supervised training. The result was that MuseCogNet significantly outperformed state-of-the-art methods on a publicly available Muse dataset.
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.