13 found
  1.  36
    fNIRS-based brain-computer interfaces: a review.Noman Naseer & Keum-Shik Hong - 2015 - Frontiers in Human Neuroscience 9.
  2.  38
    Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface.M. Jawad Khan, Melissa Jiyoun Hong & Keum-Shik Hong - 2014 - Frontiers in Human Neuroscience 8.
  3.  17
    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.
  4.  36
    Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application.Noman Naseer, Farzan M. Noori, Nauman K. Qureshi & Keum-Shik Hong - 2016 - Frontiers in Human Neuroscience 10.
  5.  14
    Early Detection of Hemodynamic Responses Using EEG: A Hybrid EEG-fNIRS Study.M. Jawad Khan, Usman Ghafoor & Keum-Shik Hong - 2018 - Frontiers in Human Neuroscience 12.
  6.  67
    Single-trial lie detection using a combined fNIRS-polygraph system.M. Raheel Bhutta, Melissa J. Hong, Yun-Hee Kim & Keum-Shik Hong - 2015 - Frontiers in Psychology 6.
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  7.  3
    Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study.Kunqiang Qing, Ruisen Huang & Keum-Shik Hong - 2021 - Frontiers in Human Neuroscience 14.
    This study decodes consumers' preference levels using a convolutional neural network in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed (...)
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  8.  19
    Effects of HD-tDCS on Resting-State Functional Connectivity in the Prefrontal Cortex: An fNIRS Study.M. Atif Yaqub, Seong-Woo Woo & Keum-Shik Hong - 2018 - Complexity 2018:1-13.
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  9.  9
    Evaluation of Neural Degeneration Biomarkers in the Prefrontal Cortex for Early Identification of Patients With Mild Cognitive Impairment: An fNIRS Study.Dalin Yang, Keum-Shik Hong, So-Hyeon Yoo & Chang-Soek Kim - 2019 - Frontiers in Human Neuroscience 13.
  10.  12
    Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain–Computer Interface.Saad Arif, Muhammad Jawad Khan, Noman Naseer, Keum-Shik Hong, Hasan Sajid & Yasar Ayaz - 2021 - Frontiers in Human Neuroscience 15.
    A passive brain–computer interface based upon functional near-infrared spectroscopy brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation state was continuously measured. Vector phase analysis (...)
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  11.  3
    Investigate the effect of HD-tDCS on the prefrontal cortex using fNIRS for neurorehabilitation.M. Atif Yaqub, Seong-Woo Woo, Amad Zafar & Keum-Shik Hong - 2018 - Frontiers in Human Neuroscience 12.
  12.  14
    Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study.So-Hyeon Yoo, Hendrik Santosa, Chang-Seok Kim & Keum-Shik Hong - 2021 - Frontiers in Human Neuroscience 15.
    This study aims to decode the hemodynamic responses evoked by multiple sound-categories using functional near-infrared spectroscopy. The six different sounds were given as stimuli. The oxy-hemoglobin concentration changes are measured in both hemispheres of the auditory cortex while 18 healthy subjects listen to 10-s blocks of six sound-categories. Long short-term memory networks were used as a classifier. The classification accuracy was 20.38 ± 4.63% with six class classification. Though LSTM networks’ performance was a little higher than chance levels, it is (...)
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  13.  3
    Comparison of active brain area for wide and dense optode configurations using initial dip.Amad Zafar, Usman Ghafoor & Keum-Shik Hong - 2018 - Frontiers in Human Neuroscience 12.