Optimisation of features using evolutionary algorithm for EEG signal classification
by Mihir Narayan Mohanty, Aurobinda Routray, Prithviraj Kabisatpathy
International Journal of Computational Vision and Robotics (IJCVR), Vol. 1, No. 3, 2010

Abstract: Stochastic optimisation plays a significant role in analysis of complex problems. EEG data is very noisy and has different types of artefacts. In this paper, we have evaluated the various time-frequency analysis of different signals as the features. Since the EEG signals are non-stationary in nature, time-frequency transformations have been suggested to extract the common features for a particular mental task performed by different subjects. The major contribution of this paper is the optimisation of different time-frequency kernels belonging to Cohen's class. A comparative assessment of the classification performance with the conventional Gaussian kernels in time as well as frequency domain has been also performed. It has been found that the Wigner-Ville type time-frequency kernel exhibit the best performance with an accuracy of 94%, followed by STFT. Comparative simulation results demonstrate a significant improvement in the classification accuracy in case of these optimised kernels.

Online publication date: Sat, 15-Jan-2011

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