What do we do?



Brain-Computer Interfaces

Brain-computer Interfaces (BCI) provide a non-muscular communication and control channel from the brain to the external environment, bypassing the peripheral nervous and muscle systems that we would normally use. The BCI Lab at Engineering Bionics focuses on the generation and decoding of different brain signals associated with motor/sensory intention, audio and visual attention, as well as virtual reality augmentation. We also investigate how to train BCI users to generate required brain waves so as to achieve high BCI performance. The main applications of our BCI research are in neurorehabilitation, including for stroke and Parkinson Disease.


Ning Jiang

Director

Lin Yao

Head of BCI

Fatima Karimi

PhD

Sarah Pearce

MASc

Mei Lin Chen

MASc

Jason Leung

Undergrad Research Assistant

Ben Lambert

ura

Myoelectric Control

We are currently collaborating with Dynacare on an industrial research project to develop a mobile electrocardiogram (ECG) solution. The project involves the application of signal processing techniques and development of algorithms to automatically detect and reject noise in the ECG signal such as contamination from EMG, as well as the identification of clinically parameters. This goal of the project to develop a pocket size clinical-grade ECG device that can completely replace current bulky ECG machines. This is a joint project with researchers from the faculty of Applied Health Sciences, led by Helen Chen.

We are also working with Mobio to develop a fast and robust algorithm of mobile ECG. The goal is to identify key ECG parameters that would not be able to obtain previously using mobile ECG. This is a joint-project with Dr. Alex Wong of Systems Design Engineering.


Ning Jiang

Director

Jiayuan He

Head of Myocontrol

Jan Lau

Co-op

Bahareh Tolooshams

ura

Erik Lloyd

MASc

Mobile ECG

We are currently collaborating with Dynacare on an industrial research project to develop a mobile electrocardiogram (EKG) solution. The project involves the application of signal processing techniques and development of algorithms to automatically detect and reject noise in the ECG signal such as contamination from EMG, as well as the identification of clinically significant features such as heart rate and ECG irregularities. This project is a joint project with researchers from the faculty of Applied Health Sciences, led by Helen Chen.


Ning Jiang

Director

Jiayuan He

Head of ECG

Sean Lazaro

Undergrad Research Assistant

Nancy Pham

Undergrad Research Assistant

Aleksandar Malinovic

MASc