用脉冲神经网络实现低功耗实时脑力负荷检测
Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
- 融合多模态数据的脉冲神经网络模型
- 精度与传统机器学习相当，支持实时运行
- 适合嵌入式自适应设备中的脑力监控
准确评估脑力负荷在认知神经科学、人机交互和实时监测中至关重要，因为认知负荷变化会影响表现和决策。尽管基于脑电图（EEG）的机器学习模型可用于此目的，但其高计算成本阻碍了嵌入式实时应用。脉冲神经网络（SNN）的硬件实现提供了低功耗、快速、事件驱动处理的可行替代方案。本研究对比了兼容硬件的SNN模型与多种传统机器学习模型，使用开源多模态数据集。结果表明，多模态融合提升了精度，SNN性能与传统模型相当，证明其在实时认知负荷检测中的潜力。这些发现表明，基于事件的处理是实现低延迟、节能脑力负荷监测的理想方案，适用于动态调节认知负荷的自适应闭环嵌入式设备。
原文摘要 · Abstract (English)
Accurately assessing mental workload is crucial in cognitive neuroscience, human-computer interaction, and real-time monitoring, as cognitive load fluctuations affect performance and decision-making. While Electroencephalography (EEG) based machine learning (ML) models can be used to this end, their high computational cost hinders embedded real-time applications. Hardware implementations of spiking neural networks (SNNs) offer a promising alternative for low-power, fast, event-driven processing. This study compares hardware compatible SNN models with various traditional ML ones, using an open-source multimodal dataset. Our results show that multimodal integration improves accuracy, with SNN performance comparable to the ML one, demonstrating their potential for real-time implementations of cognitive load detection. These findings position event-based processing as a promising solution for low-latency, energy efficient workload monitoring in adaptive closed-loop embedded devices that dynamically regulate cognitive load.
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