Email this article A look into the categorization of neurological using deep learning
Abstract
Interpreting brainwave data has grown in popularity in recent years due to its efficiency in dealing with and treating a variety of illnesses related to the field of neuroscience. According to current trends, machine learning has shown numerous promising outcomes as compared to machine learning owing to its capacity to automatically extract end-to-end features from raw input data and subsequently provide superior classification results. This work analyzed research studies that used several deep learning approaches, such as CNN, R-CNN, LSTM, GAN, and others, to classify EEG signals related to epileptic, sleeping stage and cognitive stage problems.
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