Dr. Hadis Dashtestani Profile

Dr. Hadis Dashtestani

at National Institutes of Health

SPIE Involvement:

Author

Area of Expertise:

Machine Learning , Computational Modeling , Pattern Recognition , Data Analysis

Profile Summary

Hadis is PhD fellow at Section on Analytical and Functional Biophotonics (SAFB)/ National Institute of Health. Her area of expertise is Machine Learning and Pattern Recognition. She is working on brain modeling through non-invasive imaging techniques such as functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG).

Publications (3)

Proceedings Article | 17 March 2023 Presentation

Simultaneous multimodal fNIRS-EEG recordings reveal new insights in neural activity during motor execution, observation, and imagery

Wan-Chun Su, Hadis Dashtestani, Amir Gandjbakhche

Proceedings Volume 12364, 1236409 (2023) https://doi.org/10.1117/12.2663180

KEYWORDS: Electroencephalography, Signal detection, Molybdenum, Simulation of CCA and DLA aggregates, Silver, Near infrared spectroscopy, Brain

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Proceedings Article | 9 March 2020 Presentation

fNIRS examination of mental workload changes during N-back tasks (Conference Presentation)

Kosar Khaksari, Emma Condy, John Millerhagen, Afrouz Anderson, Viswanath Gorti, Hadis Dashtestani, Amir Gandjbakhche

Proceedings Volume 11226, 112260D (2020) https://doi.org/10.1117/12.2543404

KEYWORDS: Hemodynamics, Brain, Prefrontal cortex, Data acquisition, Near infrared spectroscopy, Optical imaging, Neuroimaging

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Proceedings Article | 1 March 2019 Presentation + Paper

Application of machine learning techniques in investigating the relationship between neuroimaging dataset measured by functional near infra-red spectroscopy and behavioral dataset in a moral judgment task

Hadis Dashtestani, Joy Cui, J. Douglas Harrison, Amir Gandjbakhche

Proceedings Volume 10864, 108640S (2019) https://doi.org/10.1117/12.2520453

KEYWORDS: Brain, Neuroimaging, Simulation of CCA and DLA aggregates, Prefrontal cortex, Hemodynamics, Spectroscopy, Functional magnetic resonance imaging, Machine learning, Canonical correlation analysis, Sensors

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