A Fused Power Spectrum based Feature Selection to Identify Schizophrenia from EEG Signals using Deep Learning Models - An Experimental Study

Abstract

The brain, a complex and important organ in the human body, is crucial for all our body processes. For the diagnosis and ongoing monitoring of a wide spectrum of brain disorders, accurate and early detection of the proper disorder from neurophysiological monitoring methods is essential. The importance of identification of disorders like Schizophrenia in clinical practice is examined in this research, along with the difficulties in attaining accurate results, particularly when working with small structures and precise details. A novel pre-processing methodology in this stream has been implemented for further feature and knowledge extraction and subsequent image generation. With their ability to automatically extract pertinent features from input images, CNN has made a significant advancement in the domain of image classification.This study presents and investigates in details the effect of our pre-processing on various well-known CNN based architectures. Various models like DenseNet, ResNet, MobileNet, NasNet, EfficientNet and ConvNext families along with Xception, InceptionV3 and InceptionResNetV2 models have been taken into consideration. These models have become optimal approaches to various classification tasks, each providing certain benefits and addressing particular difficulties. We have conducted this research on EEG data from a standard dataset, namely, IBIB PAN - Department of Methods of Brain Imaging and Functional Research of Nervous System dataset. This study presents a thorough review of the performance of different CNN based models and their variants on our preprocessed and generated images. On comparison with state-of-the-art results we have observed that using this approach, almost all our models have exceeded the same. Medical professionals and researchers can use the outcomes of these techniques for better diagnosis and treatment planning in the field of brain disorders. Our codes will be made available at: \href{https://github.com/SuryaMajumder/Brain-EEG-Signal-Analysis---Experimental-Study}{[Link]}