Key result
Deep learning methods applied to EEG signals facilitate the detection of brain disorders and recognition of human emotions, though each method presents specific challenges in classification.
Why the study?
Deep learning methods are increasingly used in EEG signal applications, creating a need for a detailed survey on deep learning architectures, their usage, and their challenges and limitations.
This review summarizes the current applications, challenges, and limitations of deep learning methods in analyzing EEG signals for brain disorder detection and emotion recognition.
Supports deep learning for EEG-based brain disorder detection; leaves open prospective validation before clinical adoption.
Electroencephalogram (EEG) can track the brain waves which contain the neural activity of the brain. EEG signals help to understand the physiological and functional details and activities of the brain. In the era of Artificial Intelligence (AI), machine learning algorithms were useful in brain disorder detection and classification. Recently, a rapid increase in using Deep Learning (DL) methods in various applications in EEG signals not only helps in the detection of brain disorders but also facilitates the recognition of human emotions and various psycho-neuro disorders. In order to offer a beneficial and broad perspective, a detailed survey on the application of deep learning architecture in EEG signals has been carried out in this paper. Different deep learning methods, using varied architecture in EEG signal analysis, offer an understanding to develop the next level of AI-based systems. This review will provide information about how deep learning methods are used in EEG signals and the challenges and limitations of each method in classification; moreover making it helpful for those who are exploring EEG signals using DL algorithms.
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Praveena et al. (2020) conducted a review in Brain disorders and psycho-neuro disorders. Deep Learning methods was evaluated. Deep learning methods applied to EEG signals facilitate the detection of brain disorders and recognition of human emotions, though each method presents specific challenges in classification.
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