PGMM—Pre-Trained Gaussian Mixture Model Based Convolution Neural Network for Electroencephalography Imagery Analysis

Abstract

Electroencephalography (EEG) signal processing through imagery inputs using intelligent computation methods are practiced in the recent years for improving the accuracy of detecting neural disorders. Classification and analysis of the input imagery requires prior training and assisted error detection to improve the accuracy. In this article, pre-trained Gaussian mixture model (PGMM) is introduced for improving the accuracy of EEG signal imagery analysis. The proposed model relies on deep learning classifiers for analyzing the imagery using pixel based segmentation through pre-training models. The errors in classification are identified through recurrent convolution neural network training process as aided by the extracted features. Based on the pre-trained feature assessment, the false positive errors are mitigated to achieve a better accuracy (92%) under controlled classification time and high true positives.

Keywords

• Convolutional neural network
• EEG
• feature extraction
• imagery analysis
• learning classifier