Research

My primary research interest is in applying pattern recognition and machine learning algorithms in order to extract knowledge from data.
 
During my Ph.D. studies, I focused on knowledge extraction from brain signals in a self-paced brain computer interface (BCI) system. An SBCI system provides individuals with motor disabilities the ability to control objects with their brain signals only, whenever they want. Motivated by helping people with severe motor disabilities, BCI has already gained a lot of attention and several research groups have dedicated their efforts to develop efficient BCI systems. Although the preliminary results are promising but many questions are to yet be answered and many complex problems are yet to be solved. During my Ph.D., I focused my research efforts on using the information from three specific neurological phenomena in order to improve the performance of current SBCI systems. My proposed SBCI system resulted in error rates that are significantly lower than current state-of-the-art SBCI systems (see this article about my research).
 
I am currently pursuing another fascinating application of pattern recognition in a totally different industry: Mining! Separating ore from waste rock is an important procedure in order to increase the performance of pre-concentration systems in the mining industry. Currently, I am designing a pattern recognition system that automatically separates ores from waste rocks in an underground mining system. Upon successful implementation, this system can lead to a significant boost in the revenues as well as helping the environment.

For more information about my research, please refer to my CV.

 





mehrdadf -at -ece.ubc.ca