Application of Deep Learning for EEG

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Author(s): Angana Saikia (North-Eastern Hill University, India)and Sudip Paul (North-Eastern Hill University, India)
Copyright: 2020
Pages: 18
Source title: Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
Source Author(s)/Editor(s): Dilip Singh Sisodia (National Institute of Technology, Raipur, India), Ram Bilas Pachori (Indian Institute of Technology, Indore, India)and Lalit Garg (University of Malta, Malta)
DOI: 10.4018/978-1-7998-2120-5.ch007

Keywords: Artificial Intelligence / Computer Science & IT / Medical Information Science Reference / Medical Technologies

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Abstract

Deep learning is a relatively new branch of machine learning, which has been used in a variety of biomedical applications. It has been used to analyze different physiological signals and gain better understanding of human physiology for automated diagnosis of abnormal conditions. It is used in the classification of electroencephalography signals. Most of the present research has continued to use manual feature extraction methods followed by a traditional classifier, such as support vector machine or logistic regression. This is largely due to the low number of samples per experiment, high-dimensional nature of the data, and the difficulty in finding appropriate deep learning architectures for classification of EEG signals. One of the challenges in modeling cognitive events from EEG data is finding representations that are invariant to inter- and intra-subject differences as well as the inherent noise associated with EEG data collection. Herein, the authors explore the capabilities of the recent deep learning techniques for modeling cognitive events from EEG data.

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Application of Deep Learning for EEG

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