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Hasan Ayaz

Researcher at Drexel University

Publications -  203
Citations -  5698

Hasan Ayaz is an academic researcher from Drexel University. The author has contributed to research in topics: Functional near-infrared spectroscopy & Cognition. The author has an hindex of 37, co-authored 181 publications receiving 4500 citations. Previous affiliations of Hasan Ayaz include George Mason University & Boğaziçi University.

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Journal ArticleDOI

Optical brain monitoring for operator training and mental workload assessment.

TL;DR: Results indicate that fN IR measures are sensitive to mental task load and practice level, and provide evidence of the fNIR deployment in the field for its ability to monitor hemodynamic changes that are associated with relative cognitive workload changes of operators.
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Functional near-infrared neuroimaging

TL;DR: Recent findings indicate that fNIR can effectively monitor cognitive tasks such as attention, working memory, target categorization, and problem solving, and compare favorably with functional magnetic resonance imaging studies, and with the blood oxygenation level dependent signal.
Journal ArticleDOI

Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: empirical examples and a technological development

TL;DR: In this article, the authors describe neuroergonomic studies that illustrate the use of functional near infrared spectroscopy (fNIRS) in the examination of training-related brain dynamics and human performance assessment.
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Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

TL;DR: The goal here is to illustrate the experimental protocol design process and the use of MazeSuite, and to demonstrate the setup and deployment of the fNIR brain activity monitoring system using Cognitive Optical Brain Imaging (COBI) Studio software.
Proceedings ArticleDOI

Sliding-window motion artifact rejection for Functional Near-Infrared Spectroscopy

TL;DR: A simple and iterative method is developed that can be used to automate the preprocessing of data to identify segments with such noise for exclusion and this method is also suitable for real time applications.