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Application of Deep Learning Techniq...

Muglikar, Omkar Dushyant.

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Application of Deep Learning Techniques for EEG Signal Classification.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Application of Deep Learning Techniques for EEG Signal Classification./
Author:	Muglikar, Omkar Dushyant.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	63 p.
Notes:	Source: Masters Abstracts International, Volume: 83-06.
Contained By:	Masters Abstracts International83-06.
Subject:	Artificial intelligence. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28777623
ISBN:	9798759967460

Application of Deep Learning Techniques for EEG Signal Classification.
Muglikar, Omkar Dushyant.

Application of Deep Learning Techniques for EEG Signal Classification. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 63 p.

Source: Masters Abstracts International, Volume: 83-06.

Thesis (M.S.)--Arizona State University, 2021.

Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent of deep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more. Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it. Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.

ISBN: 9798759967460Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Deep learning

Application of Deep Learning Techniques for EEG Signal Classification.
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300 $a 63 p.
500 $a Source: Masters Abstracts International, Volume: 83-06.
500 $a Advisor: Wang, Yalin.
502 $a Thesis (M.S.)--Arizona State University, 2021.
520 $a Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent of deep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more. Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it. Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
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650 4 $a Medical imaging. $3 3172799
653 $a Deep learning
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