The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Decoding subjective preference from single-trial near-infrared spectroscopy signals
Abstract
Near-infrared spectroscopy (NIRS) has recently been identified as a safe, portable and relatively low-cost signal acquisition tool for non-invasive brain-computer interface (BCI) development. The ultimate goal of BCI research is for the user to be able to communicate functional intent directly through thoughts. In this paper we propose an NIRS-BCI paradigm based on directly decoding neural correlates of decision making, specifically subjective preference evaluation. Nine subjects were asked to mentally evaluate two possible drinks and decide which they preferred. Frequency domain near-infrared spectroscopy was used to image each subject's prefrontal cortex during the task. Using mean signal amplitudes as features and linear discriminant analysis, we were able to decode which drink was preferred on a single-trial basis with an average accuracy of 80%.
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Cites
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Decoding the visual and subjective contents of the human brain
Kamitani, Y., & Tong, F. (2005) · Nature Neuroscience · doi:10.1038/nn1444 · Cited by 1,829
Neural Predictors of Purchases
Knutson, B., Rick, S., Wimmer, G. E., Prelec, D., & Loewenstein, G. (2007) · Neuron · doi:10.1016/j.neuron.2006.11.010 · Cited by 1,171
Cited by
fNIRS-based brain-computer interfaces: a review
Naseer, N., & Hong, K.-S. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00003 · Cited by 988
Enhanced performance by a hybrid NIRS–EEG brain computer interface
Fazli, S., Mehnert, J., Steinbrink, J., Curio, G., Villringer, A., Müller, K.-R., & Blankertz, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.084 · Cited by 599
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
Neuroimaging-based approaches in the brain–computer interface
Min, B.-K., Marzelli, M. J., & Yoo, S.-S. (2010) · Trends in Biotechnology · doi:10.1016/j.tibtech.2010.08.002 · Cited by 150
Dynamic difficulty using brain metrics of workload
Afergan, D., Peck, E. M., Solovey, E. T., Jenkins, A., Hincks, S. W., Brown, E. T., Chang, R., & Jacob, R. J. K. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557230 · Cited by 148
Metadata
- Authors
- Luu, S., & Chau, T.
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2008-12-22
- Volume
- 6
- Issue
- 1
- Pages
- 016003
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W1993750289
- Cited by
- 164
- References
- 3
- FWCI
- 5.84
- Type
- journal article
- Language
- en
- Open access
- closed
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