Deep Learning-Based EEG Signal Analysis for Early Diagnosis of Neurological Disorders

Neurological disorders such as epilepsy, Alzheimer's disease, and Parkinson's disease affect millions of people worldwide, and delayed diagnosis often leads to irreversible cognitive and functional decline. Electroencephalography (EEG) is a low-cost, non-invasive technique that records the electrical activity of the brain with high temporal resolution, making it a promising modality for the early detection of these conditions. However, manual interpretation of EEG recordings is time-consuming, subjective, and requires substantial clinical expertise. Recent advances in deep learning have enabled automated, data-driven analysis of EEG signals, allowing subtle and complex patterns associated with early-stage neurological disorders to be identified without extensive handcrafted feature engineering. This paper reviews deep learning architectures - including convolutional neural networks (CNNs), recurrent networks such as long short-term memory (LSTM) networks, hybrid CNN- LSTM models, and Transformer-based architectures - that have been applied to EEG-based diagnosis of epilepsy, Alzheimer's disease, and Parkinson's disease. Reported classification performances, publicly available EEG datasets, and common preprocessing pipelines are summarized and compared. The paper further discusses key challenges limiting clinical translation, including small sample sizes, inter-subject variability, limited interpretability, and the lack of standardized evaluation protocols, and outlines future directions such as explainable Al, multimodal data fusion, and federated learning for privacy-preserving diagnosis.

کلیدواژه ها:

Deep Learning ، Electroencephalography (EEG) ، Early Diagnosis ، Neurological Disorders ، Convolutional Neural Network ، Neuroscience