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.
Eser Adı (dc.title) | Investigation of the Effects of Statistically Significant Features on the Classification of EEG-Based Motor Imagery Tasks |
Eser Sahibi (dc.contributor.author) | Murside Degirmenci |
Tez Danışmanı (dc.contributor.advisor) | Yalçın İşler |
Yayıncı (dc.publisher) | İzmir Katip Çelebi Üniversitesi Fen Bilimleri Enstitüsü |
Tür (dc.type) | Doktora Tezi |
Özet (dc.description.abstract) | 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. |
Kayıt Giriş Tarihi (dc.date.accessioned) | 2024-01-19 |
Açık Erişim Tarihi (dc.date.available) | 2024-07-19 |
Yayın Tarihi (dc.date.issued) | 2024 |
Yayın Dili (dc.language.iso) | eng |
Konu Başlıkları (dc.subject) | Extremity movement task classification |
Konu Başlıkları (dc.subject) | finger movement |
Konu Başlıkları (dc.subject) | EEG |
Tek Biçim Adres (dc.identifier.uri) | https://hdl.handle.net/11469/3870 |