MST-Net: A Multi-scale Swin Transformer Network for EEG-based Cognitive Load Assessment

In this study, a multi-scale Swin Transformer network (MST-Net) was proposed for cognitive load assessment, which extracts local features with different sensory fields using a multi-scale parallel convolution model and introduces the attention mechanism of the Swin Transformer to obtain the feature correlations among multi-scale local features. The performance of the proposed network was validated using the EEG signals collected during cognitive tasks and N-back tasks with three different load levels. Results show that the MST-Net network achieved the best classification accuracy on both local and public datasets, and was higher than the mainstream Swin Transformer and CNN. Furthermore, results of ablation experiments and feature visualization revealed that the proposed MST-Net could well characterize different cognitive loads, which not only provided novel and powerful tools for cognitive load assessment but also showed potential for broad application in brain-computer interface (BCI) systems.PMID:38049039 | DOI:10.1016/j.brainresbull.2023.110834

Source: Brain Research Bulletin - December 4, 2023 Category: Neurology Authors: Zhongrui Li Rongkai Zhang Ying Zeng Li Tong Runnan Lu Bin Yan Source Type: research