fNIRS-based brain-computer interfaces: a review

A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent...

Full description

Bibliographic Details
Main Authors: Noman eNaseer, Keum-Shik eHong
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-01-01
Series:Frontiers in Human Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00003/full
id doaj-045db3c0d29c498fb62be01cb10e324f
record_format Article
spelling doaj-045db3c0d29c498fb62be01cb10e324f2020-11-25T03:22:51ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612015-01-01910.3389/fnhum.2015.00003124008fNIRS-based brain-computer interfaces: a reviewNoman eNaseer0Keum-Shik eHong1Keum-Shik eHong2Pusan National UniversityPusan National UniversityPusan National UniversityA brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00003/fullBrain-computer interfacebrain-machine interfacefeature extractionfunctional near-infrared spectroscopyfeature classificationphysiological noise
collection DOAJ
language English
format Article
sources DOAJ
author Noman eNaseer
Keum-Shik eHong
Keum-Shik eHong
spellingShingle Noman eNaseer
Keum-Shik eHong
Keum-Shik eHong
fNIRS-based brain-computer interfaces: a review
Frontiers in Human Neuroscience
Brain-computer interface
brain-machine interface
feature extraction
functional near-infrared spectroscopy
feature classification
physiological noise
author_facet Noman eNaseer
Keum-Shik eHong
Keum-Shik eHong
author_sort Noman eNaseer
title fNIRS-based brain-computer interfaces: a review
title_short fNIRS-based brain-computer interfaces: a review
title_full fNIRS-based brain-computer interfaces: a review
title_fullStr fNIRS-based brain-computer interfaces: a review
title_full_unstemmed fNIRS-based brain-computer interfaces: a review
title_sort fnirs-based brain-computer interfaces: a review
publisher Frontiers Media S.A.
series Frontiers in Human Neuroscience
issn 1662-5161
publishDate 2015-01-01
description A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.
topic Brain-computer interface
brain-machine interface
feature extraction
functional near-infrared spectroscopy
feature classification
physiological noise
url http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00003/full
work_keys_str_mv AT nomanenaseer fnirsbasedbraincomputerinterfacesareview
AT keumshikehong fnirsbasedbraincomputerinterfacesareview
AT keumshikehong fnirsbasedbraincomputerinterfacesareview
_version_ 1724609203592495104