Brain Computer Interface

The research focus is on studying, analyzing and developing new algorithms to classify EEG brain signals.

Team Members

  • Vinay Chamola (Senior Member, IEEE)
  • Utkarsh Tripathi
  • Varun Kohli


  • Salil S Kanhere, UNSW, Australia
  • Eklas Hossain, Oregon Institute of Technology, USA

Our publications in Brain Computer Interface

A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.