Graduate Students
Justin Chan
MASc Candidate
Department of Electrical and Computer Engineering and Institute of Biomaterials and Biomedical Engineering
University of Toronto
Advisor: Tom Chau
E-mail: justinchan[at]alumni[dot]utoronto[dot]ca
Education & Training
Justin completed his Bachelors of Applied Science in Computer Engineering at the University of Toronto in 2009 with a minor in Bioengineering. He has worked as a software engineering intern for Microsoft and did a full year of backend web application development in eRetail. During his undergrad he began to develop an interest in neural prostheses, signal processing, and machine learning.
Current Research
Access technologies help those with severe and multiple physical disabilities who are unable to communicate and interact with the surrounding world. While numerous access technologies have been developed in recent years, noninvasive brain-computer interface (BCI) technologies have largely been limited to EEG, which has several drawbacks such as interference from electrical signals in the environment and motion artifacts. Near-Infrared Spectroscopy (NIRS) is an emerging non-invasive BCI alternative that harnesses blood flow in neural cortical tissue. So far NIRS-BCI studies have been exclusively offline, and no feedback was available to the users to give them conscious “control” over the system. An on-line NIRS-BCI system with real-time feedback would potentially provide an intuitive solution for nonverbal users to communicate with the outside world. A preliminary study in our lab on the real-time control of a NIRS-BCI has suggested the need for more engaging visual feedback, reassessment of training methods, and improved classification algorithms.
The primary objective of my research is to discover discriminatory features from multichannel frequency domain NIRS signals for real-time decoding of the functional intent of the user. Based on the identified signal features, the secondary objective is to implement and evaluate the real-time performance of an inductively trained classifier (HMM, artificial neural network).
Other Interests
Justin spends much of his free time practicing martial arts. He studied Judo as a child and now trains in Jeet Kune Do and Wing Chun regularly. He also likes to play keyboard and guitar and produce his own music.
