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Machine Learning for Neurophysiologi...

Nayak, Tapsya.

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Machine Learning for Neurophysiological and Medical Imaging.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning for Neurophysiological and Medical Imaging./
Author:	Nayak, Tapsya.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	105 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:	Dissertations Abstracts International82-12B.
Subject:	Electrical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28494477
ISBN:	9798516055232

Machine Learning for Neurophysiological and Medical Imaging.
Nayak, Tapsya.

Machine Learning for Neurophysiological and Medical Imaging. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 105 p.

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Thesis (Ph.D.)--The University of Texas at San Antonio, 2021.

This item must not be sold to any third party vendors.

In this thesis, I present investigations in two domains, (1) machine learning models to provide insights and aides research in Brain Computer Interface (BCI) systems and (2) I will present my investigations regarding vulnerabilities of deep learning models i.e., adversarial attacks and adversarial training as a defense technique in medical computer vision. The goal of human machine interface systems is to provide a novel communication channel with external devices to either help disabled individuals or improve human experiences. In the first study, BCI is designed to accurately decode mental states to control external devices. Specifically, I provide an inference-based solution by proposing likelihood-ratio test to detect 'idle' versus 'control' brain states asynchronously. The second study investigates predictors from electroencephalography (EEG) signals to improved human cognitive performance under varying indoor room temperatures. The prediction model used in this study provides additional insights into the brain activation patterns that contribute towards predicting low- and high- cognitive performance.At last, I investigate adversarial attacks on medical deep learning models. Recent studies have exposed vulnerability of deep learning models to adversarial attacks that cause trained models to misclassify. In this study, I will present impact of first order adversarial attacks on deep learning models of varying capacities, especially because larger deep learning models are over-parametrizing medical classification problem. To alleviate the adverse effects of adversarial attacks we train adversarial robust models and address how these models learn to correctly classify against these attacks.

ISBN: 9798516055232Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Adversarial training

Machine Learning for Neurophysiological and Medical Imaging.
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300 $a 105 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
500 $a Advisor: Huang, Yufei.
502 $a Thesis (Ph.D.)--The University of Texas at San Antonio, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a In this thesis, I present investigations in two domains, (1) machine learning models to provide insights and aides research in Brain Computer Interface (BCI) systems and (2) I will present my investigations regarding vulnerabilities of deep learning models i.e., adversarial attacks and adversarial training as a defense technique in medical computer vision. The goal of human machine interface systems is to provide a novel communication channel with external devices to either help disabled individuals or improve human experiences. In the first study, BCI is designed to accurately decode mental states to control external devices. Specifically, I provide an inference-based solution by proposing likelihood-ratio test to detect 'idle' versus 'control' brain states asynchronously. The second study investigates predictors from electroencephalography (EEG) signals to improved human cognitive performance under varying indoor room temperatures. The prediction model used in this study provides additional insights into the brain activation patterns that contribute towards predicting low- and high- cognitive performance.At last, I investigate adversarial attacks on medical deep learning models. Recent studies have exposed vulnerability of deep learning models to adversarial attacks that cause trained models to misclassify. In this study, I will present impact of first order adversarial attacks on deep learning models of varying capacities, especially because larger deep learning models are over-parametrizing medical classification problem. To alleviate the adverse effects of adversarial attacks we train adversarial robust models and address how these models learn to correctly classify against these attacks.
590 $a School code: 1283.
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650 4 $a Physiology. $3 518431
650 4 $a Neurosciences. $3 588700
650 4 $a Medical imaging. $3 3172799
650 4 $a Artificial intelligence. $3 516317
653 $a Adversarial training
653 $a Brain computer interface
653 $a Deep learning
653 $a Machine learning
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710 2 $a The University of Texas at San Antonio. $b Electrical & Computer Engineering. $3 1018585
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