Avci, M.B.Kucukselbes, H.Sayılgan, Ebru2024-02-242024-02-2420239798350328967https://doi.org/10.1109/TIPTEKNO59875.2023.10359200https://hdl.handle.net/20.500.14365/51772023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703Spinal 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. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessbrain-computer interfaceelectroencephalographymachine learningspinal cord injuryBiomedical signal processingBrain computer interfaceDecodingElectrophysiologyFast Fourier transformsFrequency domain analysisHigher order statisticsLearning algorithmsNearest neighbor searchPatient rehabilitationSupport vector machinesDomain featureF1 scoresFeature-basedFrequency domainsLower frequenciesMachine learning algorithmsMachine learning modelsMachine-learningRehabilitation SystemSpinal cord injuryElectroencephalographyConference Object10.1109/TIPTEKNO59875.2023.103592002-s2.0-85182730332