Classification of right and left hand motor imagery using deep learning in electroencephalography and near-infrared spectroscopy

Introduction: In this study, a hybrid brain-computer interface for the classification of right and left-hand motor imagery using the deep learning method is presented to increase accuracy and performance. A hybrid brain-computer interface is designed to achieve a way of communicating between the brain and an external device for patients such as amyotrophic lateral sclerosis. Therefore, the user can control the external device such as a wheelchair without using any organs of the body and only using the brain. Methods: Two electroencephalographic and near-infrared spectroscopy signals were recorded from 29 healthy men and women, and pre-processing of the signals was done to eliminate noise. The wavelet transform was used to obtain the scalogram as two-dimensional images for both of the signals, and images were inserted separately from each region of the brain and the merge region into the pre-trained convolutional neural network to extract features, classification, and prediction of left and right-hand motor imagery. Results: The results for a combination of scalogram images of Frontal-Central and Central-Parietal regions in electroencephalographic signal reached 88%, for near-infrared light spectroscopy reached 85% and for the merge of two scalogram images reached 90%. Conclusion: The combination of scalogram images and the deep learning method used in this study reached significant improvement in the prediction accuracy of right and left-hand motor imagery for wheelchair motion control. doi.org/10.30699/icss.22.3.95