用自监督学习提升便携脑机接口的认知负荷解码准确率
Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
- 融合自监督与有监督学习，模拟人类上下文注意力机制
- 在Muse公开数据集上显著优于现有方法，解码准确率提升12%
- 适合便携式脑电设备在真实场景中部署使用
便携式消费级脑电设备（如Muse头带）为日常脑机接口应用（包括认知负荷检测）提供了前所未有的移动性，但便携式脑电信号的非平稳性加剧，制约了数据保真度和解码精度，导致便携性与性能之间存在根本矛盾。为此，我们提出MuseCogNet（基于Muse的认知网络），一种整合自监督与有监督训练范式的统一联合学习框架。特别地，我们引入基于平均池化的脑电驱动自监督重构损失，以捕捉稳健的神经生理模式，同时通过交叉熵损失优化任务特定的认知判别特征。该联合学习框架模拟人类的自下而上与自上而下注意力机制，使MuseCogNet在公开的Muse数据集上显著优于现有最先进方法，并为生态场景下的神经认知监测提供了可实施路径。
原文摘要 · Abstract (English)
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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