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Discrimination of real and imaginary lower body movement: a Deep Learning approach

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Discrimination of real and imaginary lower body movement: a Deep Learning approach (EN)
Μαναρά, Χριστίνα (EL)
Manara, Christina (EN)
ntua (EL)
Matsopoulos, George (EN)
Tsanakas, Panagiotis (EN)
George, Matsopoulos (EN)
Georgios, Stamou (EN)
masterThesis
2024-07-08T08:00:19Z
2024-03-01
Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) "Επιστήμη Δεδομένων και Μηχανική Μάθηση" (EL)
This study introduces a cutting-edge method for analyzing topographical maps derived from electroencephalogram (EEG) data to classify leg movements. Leveraging the spatial information encoded in EEG topographic maps, we propose a hybrid model combining Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). This ap- proach is designed to extract and integrate spatial features from the topographic maps and temporal dynamics of EEG signals, respectively. By applying preprocessig techniques (data augmentation, ensemble method for dataset imbalance etc), enhancing the model’s ability to capture the nuanced patterns associated with different leg movements. Differ- ent optimizers, such as Adam, RMSprop & SGD, with different parameters, are performed in order to detect the best model’s performance. Preliminary results show the model’s efficacy in differentiating between specific leg movement tasks, indicating its potential utility in neurorehabilitation and brain-computer interface applications. Our research highlights the significance of advanced signal processing and machine learning tech- niques in interpreting complex brain signals, suggesting avenues for further exploration in optimizing model architecture and improving real-time prediction capabilities. (EN)
∆ίκτυα Συνέλιξης (EL)
Βελτιστοποιητές (EL)
Τοπογραφικοί θερμικοί χάρτες εγκεφάλου (EL)
Νευρωνικά Δίκτυα (EL)
Επαναληπτικά Νευρωνικά ∆ίκτυα (EL)
Data augmentation (EN)
Topographical brain maps (EN)
Optimisers (EN)
R3DCNN (EN)
Deep Learning (EN)
Greek
Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Συστημάτων Μετάδοσης Πληροφορίας και Τεχνολογίας Υλικών. Εργαστήριο Βιοϊατρικής Τεχνολογίας (EL)
Default License
National Technical University of Athens
Digital Library of National Technical University of Athens | Dspace@NTUA
Κεντρική Βιβλιοθήκη Ε.Μ.Π.
Ιδρυματικό Αποθετήριο
Μεταπτυχιακές Εργασίες



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