Robust EEG Signal Enhancement and Feature Extraction Utilizing Hybrid Deep Learning Architectures for Clinical Applications

Abstract

Electroencephalography (EEG) signals are complicated and noisy, but the are a useful tool for monitoring brain activity and detecting disorders. This paper examines how we can use hybrid deep learning models to boost the performance of EEG tasks by enhancing the signal and extracting informative features. We start by talking about the difficulties with raw EEG data (such as blinking eye or muscle movement artefacts) and the requirement for substantial improvement. Next, we describe how hybrid deep learning architectures, such as recurrent networks or convolutional neural networks with transformers, may better extract significant features from EEG data than conventional techniques. We highlight evidence from recent studies that applied these hybrid models to critical clinical areas: detecting epileptic seizures, identifying mental health conditions like depression, aiding motor-impaired patients via brain–computer interfaces, and monitoring emotional states. The results from prior research show significantly improved accuracy and reliability – for instance, hybrid models achieving above 95–98% accuracy in certain tasks – which marks a substantial improvement over earlier approaches.