Investigation of the Effects of Statistically Significant Features on the Classification of EEG-Based Motor Imagery Tasks

OpenAlex İngilizce

Motor imagery (MI) task classification is highly prevalent in Electroencephalography (EEG)-based Brain-Computer Interface (BCI) research area. Extremity movement task classification and finger movement classification studies are presented in this thesis. In extremity movement classification, binary-class (right hand and left hand) and multi-class (right hand, left hand, right hand, and left hand) classifications are performed using 4 different feature extraction approaches and statistically significance-based feature selection (the independent t-test, one-way ANOVA test). Firstly, time-domain, Fourier Transform (FT)-based frequency-domain, and Wavelet Transform (WT)-based time-frequency domain features are calculated from multichannel EEG signals. In addition to these features, Poincare plot measures-based nonlinear features are calculated. Two different combination sets are also created to classify MI tasks of EEG segments using the extracted features. For finger movement classification, time-domain, frequency-domain, WT-based time-frequency domain, non-linear and their two different combinations set features are investigated using ANOVA-based and Pricipal Component Analysis (PCA)-based feature selection methods. Intrincsic Time-Scale Decomposition (ITD)-based time-frequency features are also investigated using ANOVA-based feature selection. 9 different machine learning algorithms namely Decision Tree (DT), Support Vector Machine (SVM), kNearest Neighbor (k-NN), Naive Bayes (NB), Logistic Regression (LR), Discriminant Analysis (DA), Neural Networks (NN), and Kernel Approximation (KA) are used based on 5-fold cross-validation to distinguish different groups. According to experimental results, the most successful feature sets are Poincare plot measures-based non-linear feature set and the combination set of different feature sets in extremity and finger movement classification studies. The statistically significance-based feature selection method improved classification performance in most of the feature sets.