Machine learning algorithms for detecting fatigue:
An EEG data analysis

Sofras, Michail; Σοφράς Μιχαήλ

dc.contributor.author	Sofras, Michail	en
dc.contributor.author	Σοφράς Μιχαήλ	el
dc.date.accessioned	2025-07-16T07:51:59Z
dc.date.available	2025-07-16T07:51:59Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/62167
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.29863
dc.description	Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) "Μεταφραστική Βιοιατρική Μηχανικής και Επιστήμης"	el
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	EEG (Electroencephalography)	en
dc.subject	Phase Lag Index (PLI)	en
dc.subject	Brain Networks	en
dc.subject	Machine Learning	en
dc.subject	Μηχανική Μάθηση	el
dc.subject	Πνευματική Κόπωση	el
dc.subject	Ηλεκτροεγκεφαλογράφημα	el
dc.subject	Δείκτης Καθυστέρησης Φάσης	el
dc.subject	Δίκτυα Εγκεφάλου	el
dc.subject	Mental Fatigue	en
dc.title	Machine learning algorithms for detecting fatigue: An EEG data analysis	en
heal.type	masterThesis
heal.classification	Machine Learning	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2025-02-12
heal.abstract	Mental fatigue considerably affects cognitive performance, decision-making, and general productivity in diverse fields such as healthcare, transportation, and military operations. Extended cognitive strain may result in diminished vigilance and increasing error rates, presenting significant hazards to safety and productivity. Electroencephalography (EEG), a non-invasive technique for monitoring cerebral activity, offers an objective approach to identify and categorize mental fatigue. This thesis introduces a robust machine learning framework for classifying EEG data into rest and fatigue states, highlighting the application of specialized feature selection methods and machine learning classifiers. Electroencephalogram (EEG) data were obtained from 20 subjects performing 54 trials, categorized into rest and fatigue groups. The preprocessing procedures involved artifact elimination, including the removal of noise from muscular activity and eye blinks, as well as bandpass filtering into five frequency bands: delta, theta, alpha, beta, and gamma. Functional connectivity was assessed with the Phase Lag Index (PLI), a reliable metric of phase synchronization among EEG channels, producing high-dimensional datasets. To address the problem of dimensionality, eleven feature selection techniques, such as LASSO, ReliefF, Recursive Feature Elimination with Correlation Bias Reduction (RFE-CBR), and Fisher Score, were employed to discern the features that are most important while preserving interpretability. Five classifiers were trained using the chosen features: k-Nearest Neighbors (KNN), Support Vector Machine (SVM) with radial basis function (RBF) and linear kernels, Linear Discriminant Analysis (LDA), and Random Forest (RF). Cross-validation methods were employed to guarantee the generalization of the chosen features across different subjects. Performance was assessed utilizing criteria including accuracy, sensitivity, specificity, and F1-Score. The findings indicated that feature selection significantly enhanced classification performance. LASSO was identified as the most effective feature selection algorithm, achieving a combined accuracy of 97.5% with only 19 features and exhibiting excellent performance metrics (accuracy, sensitivity, specificity, and F1-Score) across all classifiers, demonstrating its efficacy in EEG-based fatigue detection. Lasso identified features that distinguish between rest and fatigue states using EEG channel connections. Connectivity was predominantly focused in the Frontal and Central lobes, indicating their functions in cognitive control and sensorimotor integration. Delta and Theta rhythms exhibited the highest differentiation, indicating their involvement in restorative processes and sustained attention under fatigue. These findings highlight LASSO's accuracy in identifying features relevant to fatigue identification. The present research illustrates that employing feature selection methods not only reduces the dimensionality of EEG data but also improves model interpretability by concentrating on the most prominent features. The suggested methodology establishes a basis for additional studies in EEG-based fatigue detection and presents possible applications in clinical and working environments, where the assessment of mental fatigue is essential for enhancing safety and performance.	en
heal.advisorName	Matsopoulos, George	en
heal.committeeMemberName	Matsopoulos, George	en
heal.committeeMemberName	Manopoulos, Christos	en
heal.committeeMemberName	Tsanakas, Panayiotis	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	96 σ.	el
heal.fullTextAvailability	false