IVCAN: An improved visual curve attention network for fNIRS-Based motor imagery/execution classification

Abstract

As the advantages of high spatial resolution and portability, functional near-infrared spectroscopy (fNIRS)-based Motor Imagery/Execution (MI/ME) have become promising approaches and are widely used in daily rehabilitation for neural plasticity enhancement. However, in real rehabilitation training scenario, it is essential to enhance the accuracy and effectiveness of ME/MI and the visualization of brain activation. In this study, firstly, a curve attention for fNIRS-based MI/ME classification was proposed to capture spatial-feature and hemodynamic responses. Secondly, inspired by the visual attention network (VAN) used in image classification, we further designed a network combining curve attention and VAN, called IVCAN. To evaluate the performance of IVCAN, two public ME datasets (Datasets A and C) and one self-collected MI dataset (Dataset B) were applied for evaluation. The experimental results show that the average accuracies were 85.52 %, 75.78 %, and 61.73 %, respectively for these three datasets, while the cross-subject average accuracies were 84.20 %, 75.37 %, and 61.84 %, respectively. More interestingly, brain activation patterns across different tasks were analyzed and demonstrate that the MI task requires the synergistic activation of more brain regions, while the ME task necessitates intense activity in specific brain areas. Over all, on one hand, this work provides a new and unified decoding method for fNIRS-based MI/ME, on the other hand, it elucidates the differences and connections in brain processing of various tasks from a blood hemodynamic perspective. The commonalities and differences of brain activation found in this study provide guidance and solutions for addressing the universality and personalization of fNIRS-based brain-computer interfaces.