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Deep learning for EEG data analytics: A survey
Author(s) -
Li Gen,
Lee Chang Ha,
Jung Jason J.,
Youn Young Chul,
Camacho David
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5199
Subject(s) - computer science , electroencephalography , artificial intelligence , deep learning , machine learning , supervised learning , analytics , learning analytics , data analysis , data science , artificial neural network , psychology , data mining , psychiatry
Summary In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi‐supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi‐supervised learning on EEG data analytics and discussing the importance of semi‐supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.