Hossein Bashashati, University of British Columbia (2017) "A User-Customized Self-Paced Brain Computer Interface"

Hossein Bashashati, University of British Columbia (2017) "A User-Customized Self-Paced Brain Computer Interface", advisor: Gary Birch

Much attention has been directed towards synchronous Brain Computer Interfaces (BCIs). For these BCIs, the user can only operate the system during specific system-defined periods. Self-paced BCIs, however, allow users to operate the system at any time he/she wishes. The classification of Electroencephalography (EEG) signals in self-paced BCIs is extremely challenging.

In this thesis, the authors propose a fully automatic, scalable algorithm that customizes a self-paced BCI system based on the brain characteristics of each user and at the same time captures the dynamics of the EEG signal. Their algorithm is an important step towards transitioning BCIs from research environments to real-life applications, where automatic, scalable and easy to use systems
are needed. of different classifiers in sensory motor BCIs followed by rigorous statistical tests. This study is the largest of its kind as it has been performed on 29 subjects of synchronous and self-paced BCIs. The authors then develop a Bayesian optimization-based strategy that automatically customizes a synchronous BCI based on the brain characteristics of each individual subject. Their results show that their automated algorithm (which relies on less sophisticated feature extraction and classification methods) yields similar or superior results

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compared to the best performing designs in the literature. The authors then propose an algorithm that can capture the time dynamics of the EEG signal for self-paced BCI systems. The authors show that this algorithm yields better results compared to several well-known algorithms, over 13 self-paced BCI subjects. Finally, the authors propose a fully automatic, scalable algorithm that customizes a self-paced BCI system based on the brain characteristics of each user and at the same time captures the dynamics of the EEG signal. Their final algorithm is an important step towards transitioning BCIs from research environments to real-life applications, where automatic, scalable and easy to use systems are needed.