Brain to Machine Interface Based on EEG Signals

Brain to machine interface (BMI) is a research topic aiming to develop more direct interface between a human brain and a machine. The research is primarily motivated by desire to help humans who are in need of assistance or repair of their
cognitive, sensory, or motor functions. In general, BMI is designed to be either invasive (i.e. implanted into the body) or non-invasive (i.e. without need for surgery). A BMI based on EEG signals is an example of non-invasive technique. Other applications of this technique are industrial, military and entertainment. (See for example: http://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface )

The main goal of this project is to develop a working BMI prototype capable of realtime signal processing and control. Source of the signals are EEG waves collected by non-invasive brain probes. Based on the current signal input, the BMI unit performs the signal processing and generates the decision signal to be used for controlling a machine (i.e. a computer or a robot). The student will be responsible for implementation of the BMI unit.

Faculty Supervisor:

Dr. Robert Sobot

Student:

Sandeep Samal

Partner:

Discipline:

Engineering - computer / electrical

Sector:

Life sciences

University:

Western University

Program: