fNIRS-based brain-computer interfaces: a review

Abstract

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 (ICA), 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 (SVM), hidden Markov model (HMM), 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.

Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface

“…Various modalities have been used to measure brain signals in BCI, including electroencephalography (EEG) [5,6], functional magnetic resonance imaging (fMRI) [7] and functional near infrared spectroscopy (fNIRS) [2,[8][9][10][11][12]. fNIRS is a novel non-invasive technique applied in BCI [13][14][15][16]. fNIRS is an optical imaging technique which uses light in the range of near infrared to assess brain activities [17].…”

A type-2 fuzzy approach towards cognitive load detection using fNIRS signals

“…These noises can be divided into two categories in broad sense -experimental noise, physiological noise [21]. Experimental noise or error is generated from the motion artifacts such as head motion which results in the dislocation of optodes.…”

“…There are several band pass and advance adaptive filtering techniques are available [21] for removal of these noises. During this work we have used Chebyshev band pass filter with cutoff frequencies 0.1 Hz and 0.6 Hz such that majority of these physiological noises such as Mayer wave, respiration noise are removed.…”

“…These can be visualized in the fNIRS data as spike artifacts due to abrupt change in light intensity as the optodes change their positions. Physiological noise includes several kinds of artifacts due to heartbeat (1-1.5 Hz), blood pressure fluctuations or Mayer wave (around 0.1 Hz), respiration (0.2 -0.5 Hz) [21] etc.…”

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

“…Though functional magnetic resonance imaging (fMRI) was widely used to study the operational organization of the human brain (with considerable clinical significance), it could imply high expense and operate inconveniently for driving fatigue in real driving conditions [1]. Recently, a relatively new classification techniques for functional near-infrared spectroscopy (fNIRS) was also widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation, it has low cost, portability, safety, low noise (compared to fMRI), and ease of use [2,3], For example, Khan used fNIRS to discriminate the alert and drowsy states for a passive brain-computer interface, obtaining average accuracies in the right dorsolateral prefrontal cortex of 83.1%, 83.4,% and 84.9% in different time windows respectively [4]. However, fNIRS is mainly at present a confirmatory study with shortcomings of poor time resolution compared with EEG/ERP (event-related potential) and signal acquisition without covering the whole brain.…”