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From Brain Science to Intelligent Machines

Enhancing the performance of a practical BCI system

Date: Wednesday 14/3/2012
Venue: MS020
Time: 1.00 pm
Speaker: Mr. Vaibhav Gandhi
Affiliation: Brain-Computer Interfacing and Assistive Technology team
Intelligent Systems Engineering Centre (ISRC), University Of Ulster

Enhancing the performance of a practical BCI system


By Mr. Vaibhav Gandhi
Brain-Computer Interfacing and Assistive Technology team
Intelligent Systems Engineering Centre (ISRC), University Of Ulster


Brain-computer interface (BCI) is a direct communication between the brain and an external device, bypassing the traditional pathway of peripheral nerves and muscles. The aim of BCI is to supplement human capabilities by enabling people (particularly disabled) to communicate/control devices by simply “imagining”. There are still many hurdles before this technology can reach its full potential, beginning with the issues in acquiring the electroencephalogram (EEG) signals, unknown noise characteristics embedded within the EEG, lower accuracy and information transfer rate, wide variability amongst subjects as well as over time etc.
This presentation focuses on the introductory concepts, challenges and advancements in the field of the BCI system as undertaken within the Ph.D. framework. The work includes two major aspects; first an intelligent graphical user interface (GUI) design to enhance the overall communication bandwidth of the BCI and secondly, a quantum filtering based pre-processing of the EEG signals to improve the overall system accuracy.
An adaptive GUI has been designed that utilizes the information from the sonar sensors attached to the controlled end device (robot). This enables the BCI user to have a quicker selection of choice thereby improving the time to complete a specific mission/goal. The nature of the human mind is non-classical and prompts the question whether the human mind and the mental processes can be considered as inherently quantum in nature. This led to the designing of a novel de-noising approach using the Schrodinger wave equation (SWE) for enhancement of the raw EEG signal. The raw EEG signal obtained during the motor imagery (i.e., mental imagination) performed by a BCI user is intrinsically embedded with noise while the actual noiseless signal is still unattainable. The raw EEG at every computational sampling instant is encoded in terms of a wave packet using the Recurrent Quantum Neural Network framework. The wave packet can be interpreted as the probability density function of the signal at that instant and evolves as per the SWE to de-noise the raw EEG signal thereby enhancing the information and ultimately the BCI accuracy.

Short biography:
Mr. Vaibhav Gandhi is a Ph.D. student in the Brain-Computer Interfacing and Assistive Technology team of the Intelligent Systems Research Center, University of Ulster, Londonderry. He obtained his B.Eng. in Instrumentation & Control engineering and M.Eng. in Automatic Control & Robotics. After completion of his postgraduate degree, he worked in India first as a Lecturer and later as an Asst. Professor in Instrumentation & Control engineering. After six years of service, he joined the University of Ulster as a Ph.D. student. Vaibhav is a recipient of the UKIERI scholarship for his Ph.D. research and has worked as a Sr. Research Asst. at IIT Kanpur under the UKIERI project. His research interests are in electrophysiological signal processing, neural networks, image processing and robotics.