Μονάδα:
Κατεύθυνση Smart Telecom and Sensing Networks
(SMARTNET)Πληροφορική
Ημερομηνία κατάθεσης:
2022-09-28
Συγγραφέας:
Pham-Trong Thuy
Στοιχεία επιβλεπόντων καθηγητών:
Van-Tam Nguyen
Head of the COMELEC department - Faculty Co-Founder of Paris AIoT
Télécom Paris - Institut Polytechnique de Paris
Πρωτότυπος Τίτλος:
Tiny ML in Microcontroller to Classify EEG Signal into Three States
Γλώσσες εργασίας:
Αγγλικά
Μεταφρασμένος τίτλος:
Tiny ML in Microcontroller to Classify EEG Signal into Three States
Περίληψη:
This thesis investigates how to implement an own-built neural network for electroencephalography signals classification on an STM32L475VG microcontroller unit. The original dataset is analyzed and processed to better understand the brain signals. There is a comparison between three machine learning algorithms (linear support vector machine, extreme gradient boosting, and deep neural network) in three testing paradigms: specific-subject, all-subject, and adaptable to select the most appropriate approach for deploying on the microcontroller. The implementation procedure with detailed notation is presented, and the inference is also performed to feasible observation. Finally, possible improvement solutions are proposed within a clear demonstration.
Κύρια θεματική κατηγορία:
Τεχνολογία – Πληροφορική
Λέξεις-κλειδιά:
electroencephalography, artificial neural network, STM32 microcontroller, SVM, XGBoost
Αρ. σελίδων ευρετηρίου:
3
Αρ. βιβλιογραφικών αναφορών:
44
