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Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces

Overview of attention for article published in Frontiers in Human Neuroscience, June 2018
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces
Published in
Frontiers in Human Neuroscience, June 2018
DOI 10.3389/fnhum.2018.00246
Pubmed ID
Authors

Keum-Shik Hong, M. Jawad Khan, Melissa J. Hong

Abstract

In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) patients is investigated. Brain tasks, channel selection methods, and feature extraction and classification algorithms available in the literature are reviewed. First, we categorize various types of patients with cognitive and motor impairments to assess the suitability of BCI for each of them. The prefrontal cortex is identified as a suitable brain region for imaging. Second, the brain activity that contributes to the generation of hemodynamic signals is reviewed. Mental arithmetic and word formation tasks are found to be suitable for use with LIS patients. Third, since a specific targeted brain region is needed for BCI, methods for determining the region of interest are reviewed. The combination of a bundled-optode configuration and threshold-integrated vector phase analysis turns out to be a promising solution. Fourth, the usable fNIRS features and EEG features are reviewed. For hybrid BCI, a combination of the signal peak and mean fNIRS signals and the highest band powers of EEG signals is promising. For classification, linear discriminant analysis has been most widely used. However, further research on vector phase analysis as a classifier for multiple commands is desirable. Overall, proper brain region identification and proper selection of features will improve classification accuracy. In conclusion, five future research issues are identified, and a new BCI scheme, including brain therapy for LIS patients and using the framework of hybrid fNIRS-EEG BCI, is provided.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 234 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 234 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 18%
Student > Master 29 12%
Researcher 24 10%
Student > Bachelor 18 8%
Student > Doctoral Student 9 4%
Other 32 14%
Unknown 79 34%
Readers by discipline Count As %
Engineering 70 30%
Neuroscience 28 12%
Computer Science 19 8%
Psychology 7 3%
Unspecified 7 3%
Other 15 6%
Unknown 88 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 March 2023.
All research outputs
#7,059,720
of 23,567,572 outputs
Outputs from Frontiers in Human Neuroscience
#2,887
of 7,319 outputs
Outputs of similar age
#118,871
of 330,188 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#64
of 127 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,319 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.5. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,188 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 127 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.