1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
10 years of news and resources for members of the IEEE Signal Processing Society
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
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.
|Call for Officer Nominations: Vice President-Membership and Vice President-Education||19 July 2019|
|Call for Nominations: Fellow Evaluation Committee - Chair and Vice Chair Positions||31 July 2019|
|Nominations Open for 2019 SPS Awards||1 September 2019|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2019|
© Copyright 2019 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.