Decoding of Palmar Grasp and Hand Open Tasks from Low-Frequency EEG from People with Spinal Cord Injury using Machine Learning Algorithms

Spinal cord injury (SCI) is a chronic disorder that is detrimental to the spinal cord and causes the loss of neuronal function, particularly sensorimotor functions. Brain-computer interface (BCI)-controlled rehabilitation systems have been proposed as a promising treatment component for people with SCI whose treatment is based on a long and tiring rehabilitation process. With respect to this, we presented a novel approach using an electroencephalography (EEG) based BCI rehabilitation system to help SCI patients. For this purpose, low-frequency EEG signals acquired from nine people with SCI were analyzed by considering attempted arm and hand movements. We used both time-domain features based on statistical changes (e.g., mean, variance, skewness, and kurtosis, etc.) and frequency-domain features based on Fast Fourier Transform in the EEG signal to decode the two intentions: hand open and palmar grasp. For binary classification, seven machine learning models (Fine KNearest Neighbour, Ensemble, Logistic Regression Kernel, Support Vector Machines Kernel, Fine Tree, Quadratic Discriminant, and Wide Neural Network) were used to classify the features. Accuracy, Precision, Recall, and F1 score criteria were used to evaluate machine learning models. In conclusion, we achieved successful results like an Accuracy of %91.70, Precision of %93, Recall of %90, and F1 Score of %91 by using frequency domain features combined with the Fine K-Nearest Neighbour model, with a prediction speed of 8848.84 obs/sec, and a training time of only 10.59 seconds. These results indicate that our methodology can decode executed hand open and palmar grasp motions from people with SCI. For this reason, it could be a critical and crucial contribution to the literature regarding the application of BCI.