Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
The nature of statistical learning theory
The nature of statistical learning theory
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The Journal of Machine Learning Research
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IEEE Transactions on Evolutionary Computation
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SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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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.