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

• URL: http://arxiv.org/abs/2506.23458v1
• Date: Mon, 30 Jun 2025 01:42:31 GMT
• Title: Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
• Authors: Xiaoxiao Yang, Chan Feng, Jiancheng Chen,
• Abstract summary: Muse headbands offer unprecedented mobility for daily brain-computer interface applications.<n>Non-stationarity in portable EEG signals constrains data fidelity and decoding accuracy.<n>We propose MuseCogNet, a unified joint learning framework integrating self-supervised and supervised training paradigms.
• Score: 1.0104586293349587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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