The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Hybrid EEG–fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control
Open accessAbstract
In this paper, a hybrid electroencephalography-functional near infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ∆HbO values within a 2-second moving window. In the case of EEG, two eye-blinks, three eye-blinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during one second window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG-fNIRS interface.
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Cites
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., et al. (2012) · Nature · doi:10.1038/nature11076 · Cited by 2,719
A review of classification algorithms for EEG-based brain–computer interfaces
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665
Ferrari, M., & Quaresima, V. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.049 · Cited by 2,274
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143
LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., & He, B. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/046003 · Cited by 583
Towards a cure for BCI illiteracy
Vidaurre, C., & Blankertz, B. (2010) · Brain Topography · doi:10.1007/s10548-009-0121-6 · Cited by 501
A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control
Li, Y., Pan, J., Wang, F., & Yu, Z. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2270283 · Cited by 421
Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface
Khan, M. J., Hong, M. J., & Hong, K.-S. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00244 · Cited by 280
Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI
Hong, K.-S., Naseer, N., & Kim, Y.-H. (2015) · Neuroscience Letters · doi:10.1016/j.neulet.2014.12.029 · Cited by 276
Cited by
Chen, W.-L., Wagner, J., Heugel, N., Sugar, J., Lee, Y.-W., Conant, L., Malloy, M., Heffernan, J., Quirk, B., Zinos, A., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00724 · Cited by 382
Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces
Hong, K.-S., Khan, M. J., & Hong, M. J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00246 · Cited by 283
Self-citationHong, K.-S., & Khan, M. J. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00035 · Cited by 267
Self-citationDecoding Covert Speech From EEG-A Comprehensive Review
Panachakel, J. T., & Ramakrishnan, A. G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.642251 · Cited by 156
Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review.
Ghafoor, U., Kim, S., & Hong, K.-S. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00059 · Cited by 88
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Metadata
- Authors
- Khan, M. J., & Hong, K.-S.
- Venue
- Frontiers in Neurorobotics
- Publisher
- Frontiers Media SA
- Published
- 2017-02-17
- Volume
- 11
- Pages
- 6-6
- ISSN
- 1662-5218 (ISSN-L)
- OpenAlex
- W2588765333
- Cited by
- 227
- References
- 14
- FWCI
- 16.03
- Type
- journal article
- Language
- en
- Open access
- gold
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