用滑动窗口提取时间特征，提升认知负荷分类效果
Window-Based Feature Engineering for Cognitive Workload Detection
- 采用滑动窗口对脑电数据分段提取时序特征
- 深度学习模型在准确率和F1值上优于传统方法
- 适合实时认知负荷监测场景，尤其动态任务
认知负荷是健康、心理学及国防等领域日益关注的问题。本研究基于COLET数据集，采用基于窗口的时间分段方法生成特征，并结合机器学习与深度学习模型进行认知负荷分类。通过滑动窗口对信号进行分段处理，增强原有特征表示能力，再使用多种分类模型进行分析。结果表明，深度学习模型（尤其是表格型架构）在精度、F1分数、准确率和分类性能上均优于传统机器学习方法。该研究验证了基于窗口的时序特征提取的有效性，展示了深度学习技术在复杂动态任务中实现实时认知负荷评估的潜力。
原文摘要 · Abstract (English)
Cognitive workload is a topic of increasing interest across various fields such as health, psychology, and defense applications. In this research, we focus on classifying cognitive workload using the COLET dataset, employing a window-based approach for feature generation and machine/deep learning techniques for classification. We apply window-based temporal partitioning to enhance features used in existing research, followed by machine learning and deep learning models to classify different levels of cognitive workload. The results demonstrate that deep learning models, particularly tabular architectures, outperformed traditional machine learning methods in precision, F1-score, accuracy, and classification precision. This study highlights the effectiveness of window-based temporal feature extraction and the potential of deep learning techniques for real-time cognitive workload assessment in complex and dynamic tasks.
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