Towards Optimal Model Order Selection for Autoregressive Spectral Analysis of Mental Tasks Using Genetic Algorithm

Summary Autoregressive (AR) models for spectral analysis of electroencephalogram (EEG) signals are advantageous over the classical Fourier transform methods due to their ability to deal with short segments of data, superior resolution and smoother spectra. But a problem or rather a parameter that needs to be optimised in this method is the model order of the AR equation. Currently, statistical methods like Akaike Information Criterion, Final Prediction Error, Residual Variance, Minimum Description Length, Criterion Autoregressive Transfer and Hannan-Quinn have been used for this purpose. These methods depend on the statistical properties of the data, which selects the lowest order that is optimal to represent the signal. In this paper, the use of genetic algorithm (GA) is proposed to select the order of the AR model during classifier training. This technique fuses GA with Fuzzy ARTMAP to select the appropriate AR model order for EEG signals during system training to optimise classification of test signals into their respective different mental tasks. The experimental results show that this method outperforms the other statistical methods and a fixed 6th order model although the simulations were carried out with a small number of genetic populations and generations to reduce the computational cost.

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