Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Yang, Xiaoxiao, Feng, Chao, Chen, Jiancheng
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4416261760
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