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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.23458 |
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| _version_ | 1866913919801491456 |
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| author | Yang, Xiaoxiao Feng, Chao Chen, Jiancheng |
| author_facet | Yang, Xiaoxiao Feng, Chao Chen, Jiancheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs Yang, Xiaoxiao Feng, Chao Chen, Jiancheng Human-Computer Interaction Machine Learning 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. |
| title | Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2506.23458 |