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Cited by 280EEG and Brain-Computer InterfacesOptical Imaging and Spectroscopy TechniquesGaze Tracking and Assistive Technology

The DOI matches its Crossref record; 61 of the 62 references it cites were found in a registry or an index.

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

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Khan, M. Jawad, Hong, Melissa Jiyoun, Hong, Keum-Shik·2014·Frontiers in Human Neuroscience
DOI 10.3389/fnhum.2014.00244PMC PMC4009438
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Abstract

The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, "forward," "backward," "left," and "right." The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.

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Crossref · 10.3389/fnhum.2014.00244

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Figures

Figure 1 Experimental paradigm. One complete data set over the span of a minute, consisting of four rest periods, two task periods detected by NIRS from the prefrontal brain region and two motor execution periods detected from C3 and C4 regions of brain using EEG.
Figure 1 Experimental paradigm. One complete data set over the span of a minute, consisting of four rest periods, two task periods detected by NIRS from the prefrontal brain region and two motor execution periods detected from C3 and C4 regions of brain using EEG.CC BY · PubMed Central
Figure 2 Optode location for EEG and NIRS. (A) 8 EEG electrodes placement over the Cz of the brain, (B) 12 channel locations on the prefrontal brain region using three sources and eight detectors.
Figure 2 Optode location for EEG and NIRS. (A) 8 EEG electrodes placement over the Cz of the brain, (B) 12 channel locations on the prefrontal brain region using three sources and eight detectors.CC BY · PubMed Central
Figure 3 System block diagram. The complete process from signal acquisition to control commands generation.
Figure 3 System block diagram. The complete process from signal acquisition to control commands generation.CC BY · PubMed Central
Figure 4 Brain-signal acquisition: Each spike from the left or right motor cortex represents a hand tapping with the right or left hand, respectively. The concentration change in the prefrontal brain region reflects the change from rest to mental counting (“forward”) and mental subtraction (“backward”).
Figure 4 Brain-signal acquisition: Each spike from the left or right motor cortex represents a hand tapping with the right or left hand, respectively. The concentration change in the prefrontal brain region reflects the change from rest to mental counting (“forward”) and mental subtraction (“backward”).CC BY · PubMed Central
Figure 5 Stop condition: Common rest state of the three signals is selected as stop command. The “Stop” command can only be generated if absolute rest is detected from three brain regions.
Figure 5 Stop condition: Common rest state of the three signals is selected as stop command. The “Stop” command can only be generated if absolute rest is detected from three brain regions.CC BY · PubMed Central
Figure 6 EEG data classification for left and right control commands. (A) Shows the classification of trials for “Left” and “Stop” command signals for Subject 7, (B) Shows the classification of trials for “Right” and “Stop” command signals for Subject 6.
Figure 6 EEG data classification for left and right control commands. (A) Shows the classification of trials for “Left” and “Stop” command signals for Subject 7, (B) Shows the classification of trials for “Right” and “Stop” command signals for Subject 6.CC BY · PubMed Central
Figure 7 NIRS data classification for forward and backward control commands. (A) Shows the classification of trials for “Forward” and “Stop” command signals for Subject 3, (B) Shows the classification of trials for “Backward” and “Stop” command signals for Subject 7.
Figure 7 NIRS data classification for forward and backward control commands. (A) Shows the classification of trials for “Forward” and “Stop” command signals for Subject 3, (B) Shows the classification of trials for “Backward” and “Stop” command signals for Subject 7.CC BY · PubMed Central

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Show all 62 references

Research works printed but not linked: 2

  1. Lin, 2012, Wireless brian-computer interface for electric wheelcahirs with EEG and eye-blinking signals, Int. J. Innovat. Comput. Inform. Control, 8, 6011
  2. Turnip, 2012, Classifying mental activities from EEG-P300 signals using adaptive neural network, Int. J. Innovat. Comput. Inform. Control, 8, 6429

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Metadata

Authors
Khan, M. J., Hong, M. J., & Hong, K.-S.
Affiliations
  • Pusan National University (KR)
  • Columbia University (US)
Corresponding author
Hong, Keum-Shik
ORCID
Venue
Frontiers in Human Neuroscience
Publisher
Frontiers Media SA
Published
2014-04-28
Volume
8
Pages
244-244
ISSN
1662-5161 (ISSN-L)
DOI
10.3389/fnhum.2014.00244
PMC
PMC4009438
OpenAlex
W1974893143
Cited by
280
References
62
Citations by year
2026: 4 · 2025: 3 · 2024: 6 · 2023: 7 · 2022: 14 · 2021: 31 · 2020: 30 · 2019: 32 · 2018: 42 · 2017: 40 · 2016: 32 · 2015: 31 · 2014: 8
FWCI
20.63
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Human Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Research Foundation
  • National Research Foundation of Korea
  • Ministry of Education, Science and Technology
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Keywords
hybrid brain-computer interface, near-infrared spectroscopy, EEG, prefrontal cortex, motor cortex, oxyhemoglobin, hand tapping, brain signal classification

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