AI-Driven EEG Signal Processing: Advancements in Depression Marker Identification

<p dir="ltr"><i>The integration of artificial intelligence (AI) into </i><i>electroencephalography (EEG) signal analysis represents a </i><i>significant advance in the understanding and diagnosis of major </i><i>depressive disorder (MDD). This review examines how AI </i><i>techniques can improve the identification of neurophysiological </i><i>markers associated with depression through effective </i><i>preprocessing, denoising, and feature extraction. We review </i><i>convolutional neural networks, recurrent neural networks, and </i><i>hybrid models that demonstrate a remarkable ability to analyze </i><i>complex EEG data, revealing distinct neural dynamics associated </i><i>with depressive states. Additionally, this paper addresses </i><i>evaluation methodology, current challenges, and future research </i><i>directions, highlighting the potential of AI-based approaches to </i><i>improve diagnostic capabilities and inform personalized treatment </i><i>strategies for depressive disorders.</i></p>