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

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 results. The average accuracy of offline cognitive overload assessment using the proposed method on local and open datasets is 82.44 ± 1.59% and 78.24 ± 1.43%, respectively. The average classification accuracy of its online cognitive overload assessment is about 79.90 ± 2.53%. This indicates that the proposed method can effectively assess cognitive overload under both offline and online conditions. Furthermore, we found that higher-frequency sub-bands are more advantageous for cognitive overload assessment. Significance. EEG signals can be used for effectively cognitive overload assessment, and the integration of feature selection methods enhances the accuracy of the evaluation, providing reliable methodological support for future cognitive overload monitoring in human-computer interaction systems. .