From Brain Waves to Diagnoses: AI's Role in Schizophrenia Detection

Brain signals can be represented as numerical vector sets using electroencephalograms (EEGs). These signals are used to estimate a wide range of brain disorders, including schizophrenia, dementia, epilepsy, and Parkinson's. Experts must invest a great deal of time and effort in manually analyzing these signals. Finding the best EEG processing models for clinical use is challenging for researchers. Numerous machine learning-based processing techniques are suggested for these signals. The task of choosing a model for clinical use is made more difficult by the fact that these models have different performance criteria. This study explores the emerging role of Artificial Intelligence (AI) in transforming the landscape of schizophrenia detection through the analysis of brain waves. Leveraging neuroimaging technologies, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), researchers have sought to unveil distinctive patterns in the brain activity of individuals with schizophrenia. The research encompasses studies from the last decade, emphasizing the diverse AI methodologies employed in interpreting brain wave data. Machine learning algorithms, particularly deep learning models, have shown excellent results in identifying subtle aberrations associated with schizophrenia. These algorithms analyze intricate patterns within EEG and fMRI data, revealing hidden insights that traditional diagnostic methods might overlook. As the field advances, collaboration between clinicians, AI researchers, and ethicists becomes imperative to ensure responsible development and deployment of these technologies in clinical settings. This study contributes to the ongoing discourse on the transformative impact of AI in mental health and sets the stage for future research directions in schizophrenia detection.