Browsing by Author "Kucukselbes, H."

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Citation - Scopus: 7
Binary Classification of Spinal Cord Injury Patients' Eeg Data Based on the Local Linear Embedding and Spectral Embedding Methods
(Institute of Electrical and Electronics Engineers Inc., 2023) Kucukselbes, H.; Sayılgan, Ebru
Spinal cord injury (SCI) is a chronic disease that damages the spinal cord, leading to the loss of neuronal function, especially sensorimotor functions. Brain-Computer Interface (BCI) controlled rehabilitation systems offer a promising therapeutic treatment for individuals with SCI. Their treatment often involves a lengthy and demanding rehabilitation process. For this reason, we introduced an innovative approach that utilizes an electroencephalography (EEG)-based BCI rehabilitation system to assist SCI patients. Our study involved the analysis of low-frequency EEG signals from nine individuals with SCIs while attempting arm and hand movements. EEG analysis generally consists of preprocessing, feature extraction, and Machine Learning (ML) algorithms for classification. However, relying solely on traditional methods for each step may prove inadequate for real-time applications. Traditional approaches can sometimes be limited by the complexity and high dimensionality of the signals. To address these challenges, we employed Manifold Learning, which allows for a more effective representation of the temporal and spatial features of brain activity in a lower-dimensional space of EEG signals. Our study obtained certain results by trying Spectral Embedding and Local Linear Embedding methods as Manifold Learning algorithms. The classification was implemented using k-NN, Naive Bayes, and SVM methods. According to the average results, the k-NN algorithm gives the best accuracies for Local Linear Embedding methods obtained at 0.995, and the Spectral Embedding methods obtained at 0.996. While comparing the Manifold Learning methods, we achieved the highest success in Spectral Embedding. © 2023 IEEE.
Citation - Scopus: 3
Decoding of Palmar Grasp and Hand Open Tasks From Low-Frequency Eeg From People With Spinal Cord Injury Using Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2023) Avci, M.B.; Kucukselbes, H.; Sayılgan, Ebru
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. © 2023 IEEE.