Examining psychiatric diseases with deep learning and machine learning methods using brain signals

Psychiatric disorders are a major public health problem that can have a significant impact on individuals, families, and society. Early diagnosis and treatment of these disorders can improve patient outcomes and reduce the burden on society. Electroencephalography (EEG) is a non-invasive medical imaging technique that can be used to record electrical activity in the brain. EEG signals can be used to diagnose a variety of neurological disorders, including psychiatric disorders. Deep learning and machine learning are two types of artificial intelligence (AI) that can be used to analyze EEG signals. These methods can be used to identify patterns in EEG signals that are associated with psychiatric disorders. This study investigated the use of deep learning and machine learning methods to study the effect of psychiatric disorders on EEG signals. The study used a dataset of EEG signals from patients with a variety of psychiatric disorders, including schizophrenia, depression, and anxiety. The study found that deep learning and machine learning methods were able to identify patterns in EEG signals that were associated with psychiatric disorders. These patterns could be used to improve the diagnosis and treatment of psychiatric disorders. Generally, this study suggests that deep learning and machine learning methods can be used to study the effect of psychiatric disorders on EEG signals. These methods have the potential to improve the diagnosis and treatment of psychiatric disorders. Keywords: Psychiatric disorders, EEG signals, Deep learning, Machine learning, Diagnosis & Treatment