Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface

@article{Naseer2013OnlineBD,
  title={Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface},
  author={Noman Naseer and Melissa Jiyoun Hong and Keum Shik Hong},
  journal={Experimental Brain Research},
  year={2013},
  volume={232},
  pages={555-564}
}
In this paper, a functional near-infrared spectroscopy (fNIRS)-based online binary decision decoding framework is developed. Fourteen healthy subjects are asked to mentally make “yes” or “no” decisions in answers to the given questions. For obtaining “yes” decoding, the subjects are asked to perform a mental task that causes a cognitive load on the prefrontal cortex, while for making “no” decoding, they are asked to relax. Signals from the prefrontal cortex are collected using continuous-wave… 
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References

SHOWING 1-10 OF 58 REFERENCES
Decoding subjective preference from single-trial near-infrared spectroscopy signals.
TLDR
An NIRS-BCI paradigm based on directly decoding neural correlates of decision making, specifically subjective preference evaluation is proposed, and which drink was preferred on a single-trial basis is decoded.
Functional Near Infrared Spectroscope for Cognition Brain Tasks by Wavelets Analysis and Neural Networks
TLDR
Results of fNIRs signal analysis indicating that there exist distinct patterns of hemodynamic responses which recognize brain tasks toward developing a BCI are presented.
Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy.
TLDR
This study investigated the feasibility of an alternative two-choice NIRS-BCI paradigm based on the classification of prefrontal activity due to two cognitive tasks, specifically mental arithmetic and music imagery and suggested the potential of a two- choices based on cognitive rather than motor tasks.
fNIRS-based online deception decoding.
TLDR
A functional near-infrared spectroscopy (fNIRS)-based online brain deception decoding framework is developed and suggests that the applicability of fNIRs as a brain imaging technique for online deception detection is very promising.
Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface.
TLDR
Three different selective attention tasks were tested in conjunction with a P300-based protocol, showing encouraging results, showing that on average the imaginary movement achieved a P 300 versus No-P300 classification accuracy of 84.53%.
Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: an fNIRS study.
TLDR
This paper presents the first novel technique for fNIRS-based modelling of brain activities using the linear parameter-varying (LPV) method and adaptive signal processing and shows an improvement in the estimation of haemodynamic response.
Brain-computer interface using a simplified functional near-infrared spectroscopy system.
TLDR
This work describes the construction of the device, the principles of operation and the implementation of a fNIRS-BCI application, 'Mindswitch' that harnesses motor imagery for control, and shows that fNirS can support simple BCI functionality and shows much potential.
...
...