Decoding EEG signals

Bachelor thesis (2023)

Authors

M. Dai Electrical Engineering, Mathematics and Computer Science
J.J. van de Weg Electrical Engineering, Mathematics and Computer Science

Contributors

Borbala Hunyadi Signal Processing Systems - EEMCS (supervisor 1)
K.M. Dowling Electronic Instrumentation - EEMCS (supervisor 2)
D. Cavallo Tera-Hertz Sensing - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Anthony Dai, Joris van de Weg
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Published Date

29-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In the context of designing a real-time brain-computer interface for playing a game using the OpenBCI Ultracortex "Mark IV" headset, this paper focuses on the work of the decoding subgroup. The primary responsibility is to analyse EEG data retrieved from the OpenBCI headset and classify the intention of the user. Our objective is to achieve a high-accuracy classification of the EEG signals. The paper is structured into three main sections: preprocessing, feature extraction, and classification. Multiple methods for preprocessing and classification of motor execution EEG signals will be analysed, striving to contribute to the real-time implementation of the project. The results of our work provides valuable insights for future research and development in this field.