A novel method of cognitive overload assessment based on a fusion feature selection using EEG signals

作者：Zhongrui Li, Li Tong, Ying Zeng, Yuanlong Gao, Diankun Gong, Kai Yang, Yidong Hu, Bin Yan · 发表于：Journal of Neural Engineering · 年份：2024 · DOI：10.1088/1741-2552/ad9cc0 · 被引用次数：5 · 研究领域：EEG and Brain-Computer Interfaces、Heart Rate Variability and Autonomic Control、Sleep and Work-Related Fatigue

OBJECTIVE: Cognitive overload, as an overload state of cognitive workload, negatively impacts individuals' task performance and mental health. Cognitive overload assessment models based on Electroencephalography (EEG) can effectively prevent the occurrence of overload through early warning, thereby enhancing task execution efficiency and safeguarding individuals' mental health. Although existing EEG-based cognitive load assessment methods have achieved significant research outcomes, evaluating cognitive overload remains an ongoing challenge. Current research aims to develop an effective cognitive overload assessment model and enhance its efficacy through feature selection methods.
Approach. In the cognitive overload assessment model, we firstly employ Variational Mode Decomposition (VMD) to adaptively decompose the signal from each channel into four sub-band signals to capture valuable time-frequency information. Subsequently, frequency domain features are extracted from each sub-band, and an effective feature selection method based on Mutual Information (MI) and Neighborhood Component Analysis (NCA) was applied for feature selection, which optimizes the distribution of the feature space while considering feature correlations, making the selected features more representative. Finally, traditional machine learning methods are utilized for classification, and the effectiveness of the proposed method is tested using both offline and online classification results.
Main re...