A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

Abstract

The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.

Keywords: AI; EEG; Schizophrenia (SCZ); classification; deep learning; machine learning.

FIGURE 1

The 10–20 system guides EEG electrode placement (Cooper et al., 2014).

FIGURE 2

Block diagram for AI-based Schizophrenia classification.

FIGURE 3

Machine Learning model block diagram representation.

FIGURE 4

A Deep Learning model consists of an input layer, hidden layers, and an output layer.

FIGURE 5

A module of LSTM network.

FIGURE 6

PRISMA flowchart of literature search procedures.

FIGURE 7

The total number of papers on schizophrenia classification.