Machine Learning Assessment of EEG Data in a fatigue related n-back task

Charalampous, Ioannis; Χαραλάμπους, Ιωάννης

dc.contributor.author	Charalampous, Ioannis	en
dc.contributor.author	Χαραλάμπους, Ιωάννης	el
dc.date.accessioned	2025-12-04T07:47:40Z
dc.date.available	2025-12-04T07:47:40Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/62987
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.30683
dc.rights	Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nd/3.0/gr/	*
dc.subject	SHAP	en
dc.subject	Ψυχική κόπωση	el
dc.subject	Εντοπισμός πηγής	en
dc.subject	Εργασία n-back	el
dc.subject	CNN-BiLSTM	en
dc.subject	EEG	en
dc.subject	source localization	en
dc.subject	Mental fatigue	en
dc.title	Machine Learning Assessment of EEG Data in a fatigue related n-back task	en
heal.type	bachelorThesis
heal.classification	Machine Learning	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2025-06-24
heal.abstract	Mental fatigue significantly seriously impacts cognitive functions and decision-making, especially in safety-related environments like aviation, medicine, and transportation. This thesis presents a methodology for detecting mental fatigue from electroencephalography (EEG) signals recorded while doing an n-back working memory task. A hybrid deep learning model that combines a convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) was developed to classify EEG signals as ”fatigued” and ”rested” states. In order to enhance the spatial resolution of the EEG signals, source localization was performed using the sLORETA algorithm, which projects scalp-recorded activity onto cortical surfaces. The model was trained and tested using 10-fold cross-validation, and achieved an average accuracy of 91.55%, demonstrating that it is robust and generalized across subjects. Explainability was also ensured through SHapley Additive exPlanations (SHAP), which provided insight regarding the most salient cortical sources that are responsible for the model predictions. The analysis highlighted contributions from frontal and parietal regions, that are consistent with neuroscientific findings on fatigue-related changes in brain activity. The dataset was collected from recordings of the participants in both rested and sleep-deprived conditions, enabling the model to learn discriminative patterns associated with mental fatigue. This work not only offers a high-performing and also explainable model for EEG-based fatigue assessment but also reduces the gap between deep learning and neuroscience by connecting machine learning predictions with physiologically meaningful brain processes.	en
heal.advisorName	Matsopoulos, George
heal.committeeMemberName	Tsanakas, Panayiotis
heal.committeeMemberName	Manopoulos, Christos
heal.committeeMemberName	Matsopoulos, George
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών.	el
heal.academicPublisherID	ntua
heal.fullTextAvailability	false