Format

Send to

Choose Destination
Front Hum Neurosci. 2015 Jan 28;9:3. doi: 10.3389/fnhum.2015.00003. eCollection 2015.

fNIRS-based brain-computer interfaces: a review.

Author information

1
Department of Cogno-Mechatronics Engineering, Pusan National University Busan, Republic of Korea.
2
Department of Cogno-Mechatronics Engineering, Pusan National University Busan, Republic of Korea ; School of Mechanical Engineering, Pusan National University Busan, Republic of Korea.

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.

KEYWORDS:

brain-computer interface; brain-machine interface; feature classification; feature extraction; functional near-infrared spectroscopy (fNIRS); physiological noise

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central
Loading ...
Support Center