Automated Machine Learning Approaches for Human Brain Disease Detection Using EEG Signal Categorization

Clinical practice could be revolutionized by the use of AI and ML approaches for disease identification and categorization, utilizing EEG signals. EEG signals are important for diagnosing and treating various neurological and psychiatric disorders, including epilepsy, sleep disorders, and cognitive impairment. However, the current methods for analyzing EEG signals have limitations in terms of accuracy and efficiency. The classification of EEG signals for disease detection has recently demonstrated encouraging results using ML-based techniques. These algorithms analyze EEG signals and find patterns that are indicative of particular disorders using cutting-edge methods such as deep learning, convolutional neural networks (CNNs), and support vector machines (SVMs). In order to increase the precision and effectiveness of disease identification in clinical practise, machine learning-based methods for classifying diseases using EEG data were proposed to show the significant potential.