Motor Imagery based Brain Computer Interface (BCI) using Artificial Neural Network Classifiers

Maitreyee Wairagkar

Abstract


Brain Computer Interface (BCI) facilitates the communication with external devices (Computer) directly by the brain without involvement of any motor pathways. These systems are useful for assisting people with the impaired motor abilities. The project envisaged the development of a reliable motor imagery BCI capable of classifying right and left hand imaginary movements for controlling a computer cursor. The electroencephalogram (EEG) signals were recorded from the sensory-motor region of the brain using wireless recording device. Using the Event Related Desynchronization (ERD), movement related features were extracted from EEG with the spatial and spectral filtering. These features were classified into two classes using the non-liner Radial Basis Function based Artificial Neural Network (ANN) classifiers. A peak accuracy of 80% was achieved for classifying imaginary hand movements of 16 different subjects. Combination of the non-linear ANN classifiers with the signal processing techniques proved to be an effective method for classifying motor imagery for BCI.

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Copyright (c) 2014 Maitreyee Wairagkar