S. G. Samuvel “Research and implementation of the methods to recognize and classify motor imagery potentials” doctoral dissertation defence

Thesis Defense

Author, Institution: Sam Gilvine Samuvel, Kaunas University of Technology

Science area, field of science: Natural Sciences, Informatics, N009

Scientific Supervisor: Prof. Dr. Vacius Jusas (Kaunas University of Technology, Natural Sciences, Informatics, N009)

Dissertation Defence Board of Informatics Science Field:

Prof. Dr. Habil. Rimantas Barauskas (Kaunas University of Technology, Natural Sciences, Informatics, N009)– chairman
Prof. Dr. Gintaras Palubeckis (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Prof. Dr. Chrysostomos Stylios (Ioannina University, Greece, Natural Sciences, Informatics, N009)
Prof. Dr. Habil. Antanas Verikas (Kaunas University of Technology, Natural Sciences, Informatics, N009)
Prof. Dr. Julius Žilinskas (Vilnius University, Natural Sciences, Informatics, N009)

The dissertation defence takes place online.

The doctoral dissertation is available on the internet and at the library of Kaunas University of Technology (K. Donelaičio g. 20, Kaunas).

Annotation:

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the sequence of multiple algorithms increases the performance of the feature extraction process. We have proposed a novel consolidated sequence of methods for feature extraction and feature reduction. The focus is given more on the feature extraction process and frequency bands, which are subject-specific frequency bands and time segments. A combination of subject specific frequency bands and time segments is a novel method-based technique that has not been utilized with EEG. This research proposes to use a combination of subject specific bands and time segments for feature extraction to solve a four-class motor imagery problem.

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