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Measuring Mental Workload with EEG+fNIRS
Frontiers in Human Neuroscience 11 (2017)
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Citations of this work
Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces.Keum-Shik Hong, M. Jawad Khan & Melissa J. Hong - 2018 - Frontiers in Human Neuroscience 12.
A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy.Anh Son Le, Hirofumi Aoki, Fumihiko Murase & Kenji Ishida - 2018 - Frontiers in Human Neuroscience 12.
Neurophysiological Measures of the Perception of Antismoking Public Service Announcements Among Young Population.Giulia Cartocci, Enrica Modica, Dario Rossi, Patrizia Cherubino, Anton Giulio Maglione, Alfredo Colosimo, Arianna Trettel, Marco Mancini & Fabio Babiloni - 2018 - Frontiers in Human Neuroscience 12.
Comparing the Relative Strengths of EEG and Low-Cost Physiological Devices in Modeling Attention Allocation in Semiautonomous Vehicles.Dean Cisler, Pamela M. Greenwood, Daniel M. Roberts, Ryan McKendrick & Carryl L. Baldwin - 2019 - Frontiers in Human Neuroscience 13.
Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review.Haroon Khan, Noman Naseer, Anis Yazidi, Per Kristian Eide, Hafiz Wajahat Hassan & Peyman Mirtaheri - 2021 - Frontiers in Human Neuroscience 14.
References found in this work
On the economy of the human-processing system.David Navon & Daniel Gopher - 1979 - Psychological Review 86 (3):214-255.
fNIRS-based brain-computer interfaces: a review.Noman Naseer & Keum-Shik Hong - 2015 - Frontiers in Human Neuroscience 9.
Age differences in short-term retention of rapidly changing information.Wayne K. Kirchner - 1958 - Journal of Experimental Psychology 55 (4):352.
Sensitivity of fNIRS to cognitive state and load.Frank A. Fishburn, Megan E. Norr, Andrei V. Medvedev & Chandan J. Vaidya - 2014 - Frontiers in Human Neuroscience 8.
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