Document Type: Original Research


1 Department of Computer Sciences and Engineering, School of Engineering, Shiraz University, Shiraz, Iran

2 Department of Computer Engineering, College of Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran


Objective: In this research, a new approach termed as “evolutionary-based brain map” is presented as a diagnostic tool to classify schizophrenic and control subjects by distinguishing their electroencephalogram (EEG) features.Methods: Particle swarm optimization (PSO) is employed to find discriminative frequency bands from different EEG channels. By deploying the energy of those selected frequency bands from different channels within each time frame (window) on the scalp geometry, a sort of two dimensional points along with their values are created; by applying Lagrange interpolation, an image can be constructed. Finally, by averaging the images belonging to successive time frames, an evolutionary-based brain map is created.Results: In this study, twenty subjects from each group voluntarily participated and their EEG signals were caught from 20 channels. The energy of selected bands for different channels are arranged in a feature vector for each time frame and applied to Fisher linear discriminant analysis (FLDA) resulting in 83.74% diagnostic accuracy between the two groups. The achieved result by the proposed method was much higher than applying the energy of standard EEG bands (delta, theta, alpha, beta and gamma) to the same classifier which just provided 77.04% accuracy. Applying T-test to the achieved results supports the supremacy of the proposed method as an automatic powerful diagnostic tool.Conclusion: The proposed brain map is capable of highlighting the same physiological and anatomical changes which are observed in fMRI, PET and CT as differentiable indicators between controls and schizophrenic patients


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