用单块注意力结构实现低功耗脑电认知负荷实时评估
One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment
- 单块架构通过交叉注意力+轻量自注意力聚合多通道脑电信号
- 参数<0.5百万，计算量仅0.02 GFLOPs，性能仍高
- 适合嵌入式设备部署，实现实时认知负荷监控
准确连续地估计认知负荷是构建自适应人机系统的基础。然而，设计兼具表征能力与计算效率的模型在实际部署中仍具挑战。本文提出1BT（One-Block Transformer），一种用于紧凑高效脑电（EEG）认知负荷评估的单模块变换器。该模型通过最小化潜在瓶颈聚合多通道时间序列，采用单一交叉注意力模块后接轻量级自注意力。一项包含11名参与者、执行三类认知任务（抽象推理、数值解题、交互式视频游戏）的受控研究，采集了双负荷水平下的连续脑电数据。系统性架构分析确定了最紧凑配置，在显著降低计算成本的同时保持高性能。最终模型以不足0.5百万参数和0.02 GFLOPs计算量实现高精度分类，为资源受限场景下的实时认知负荷监测提供了新设计方向。
原文摘要 · Abstract (English)
Accurate and continuous estimation of cognitive workload is fundamental to creating adaptive human-machine systems. However, designing architectures that balance representational capacity with computational efficiency has been challenging for practical deployment. This paper introduces 1BT, a One-Block Transformer for compact and efficient EEG-based cognitive workload assessment. The model aggregates multi-channel temporal sequences via a minimal latent bottleneck, using a single cross-attention module followed by lightweight self-attention. A controlled study involving 11 participants performing three cognitively diverse tasks (abstract reasoning, numerical problem-solving, and an interactive video game) was conducted with continuous EEG recordings across two workload levels. Systematic architectural analysis identifies the most compact configuration that preserves high performance, while substantially lowering computational cost. The final model achieves high workload classification performance with under 0.5 million parameters and 0.02 GFLOPs, paving the way for a design direction for real-time cognitive workload monitoring in resource-constrained settings.
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