Improved dilation CapsuleNet for motor imagery and mental arithmetic classification based on fNIRS

AbstractPurpose This study aimed to improve the accuracy of brain-computer interface (BCI) systems based on motor imagery (MI) and mental arithmetic (MA) by utilizing functional near-infrared spectroscopy (fNIRS) and an improved dilation CapsuleNet (ID-CapsuleNet) model.Methods The study focused on the characteristics of fNIRS and employed large-kernel dilation convolution to extract hemodynamic features from fNIRS data. Inspired by CapsuleNet’s success in image classification, an ID-CapsuleNet model was designed, combining large-kernel dilation convolution and CapsuleNet. Four publicly available datasets (A, B, C, and D) were utilized for evaluating the proposed model. Datasets A and B were MA type, while datasets C and D were MI type. Ablation experiments were conducted to assess the usefulness of large-kernel convolution, dynamic routing, and dilation convolution.Results The average accuracies for each dataset were 95.01%, 76.88%, 74.03%, and 80.29% respectively. Cross-subject average accuracies were 88.72%, 75.80%, 75.78%, and 80.34%. Ablation experiments confirmed the importance of large-kernel convolution, dynamic routing, and dilation convolution in the ID-CapsuleNet model.Conclusion The developed ID-CapsuleNet model demonstrated promising potential for enhancing the performance of BCI systems based on MI and MA. The findings contribute to the advancement of BCI technology, offering improved assistive tools for disabled individuals.