Temporal hemodynamic classification of two hands tapping using functional near—infrared spectroscopy

Hải, Nguyễn Thanh; Cuong, Ngo Q.; Khoa, Truong Quang Dang; Toi, Vo Van

doi:10.3389/fnhum.2013.00516

Cited by 30 publications

(23 citation statements)

References 51 publications

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“…Since motor execution involves contraction of muscles, motor execution-based BCIs are affected by proprioceptive feedback from contracting muscles and, therefore, the neuronal modulation may not be solely from the central nervous system. Several motor execution tasks including finger tapping (Cui et al, 2010a , b ; Seo et al, 2012 ), hand tapping (Hai et al, 2013 ; Khan et al, 2014 ), arm lifting (Shin and Jeong, 2014 ), knee extension (Shin and Jeong, 2014 ) and hand grasping/gripping (Nagaoka et al, 2010 ; Fazli et al, 2012 ) have been used in the previous studies.…”

Section: Brain-signal Acquisitionmentioning

confidence: 99%

“…This can cause a sudden change in the light intensity resulting in a spike-like noise. Several methods for motion-artifact correction have been proposed in the literature; the Wiener filtering-based method (Izzetoglu et al, 2005 ), eigenvector-based spatial filtering (i.e., principle component analysis (PCA)-based filtering) (Zhang et al, 2005 ), wavelet-analysis-based methods (Sato et al, 2006 ; Power et al, 2010 ), Savitzky-Golay type filters (Hai et al, 2013 ; Shin and Jeong, 2014 ), and others (Cui et al, 2010a , b ; Fekete et al, 2011 ; Cooper et al, 2012 ). Please see Cooper et al ( 2012 ) for thorough comparison of various techniques.…”

Section: Preprocessingmentioning

confidence: 99%

“…These can then be used as features for classification of different signals. The most commonly used features for discrimination of different activities for fNIRS-BCI are signal mean (Coyle et al, 2004 , 2007 ; Sitaram et al, 2007 ; Luu and Chau, 2009 ; Power et al, 2010 ; Holper and Wolf, 2011 ; Fazli et al, 2012 ; Moghimi et al, 2012 ; Faress and Chau, 2013 ; Naseer and Hong, 2013 ; Naseer et al, 2014 ; Hong et al, 2015 ), signal slope (Power et al, 2011 , 2012a , b ; Hai et al, 2013 ; Naseer and Hong, 2013 ; Power and Chau, 2013 ; Schudlo and Chau, 2014 ; Hong et al, 2015 ), signal variance (Tai and Chau, 2009 ; Holper and Wolf, 2011 ), amplitude (Naito et al, 2007 ; Cui et al, 2010b ; Bauernfeind et al, 2011 ; Stangl et al, 2013 ), skewness (Tai and Chau, 2009 ; Holper and Wolf, 2011 ), kurtosis (Tai and Chau, 2009 ; Holper and Wolf, 2011 ), and zero crossing (Tai and Chau, 2009 ).…”

Section: Feature Extraction/selectionmentioning

confidence: 99%

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fNIRS-based brain-computer interfaces: a review

Naseer

Hong

2015

Front. Hum. Neurosci.

635

595

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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.

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Section: Brain-signal Acquisitionmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Section: Feature Extraction/selectionmentioning

confidence: 99%

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fNIRS-based brain-computer interfaces: a review

Naseer

Hong

2015

Front. Hum. Neurosci.

635

595

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“…It has been previously used to denoise fNIRS signal. 25,26 Rloess combines weighted linear least squares regression with the flexibility of nonlinear regression. 27 In several fNIRS studies, Rloess has been used as a smoothing filter in data preprocessing.…”

Section: Introductionmentioning

confidence: 99%

Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky–Golay filtering

et al. 2018

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Motion artifact contamination in near-infrared spectroscopy (NIRS) data has become an important challenge in realizing the full potential of NIRS for real-life applications. Various motion correction algorithms have been used to alleviate the effect of motion artifacts on the estimation of the hemodynamic response function. While smoothing methods, such as wavelet filtering, are excellent in removing motion-induced sharp spikes, the baseline shifts in the signal remain after this type of filtering. Methods, such as spline interpolation, on the other hand, can properly correct baseline shifts; however, they leave residual high-frequency spikes. We propose a hybrid method that takes advantage of different correction algorithms. This method first identifies the baseline shifts and corrects them using a spline interpolation method or targeted principal component analysis. The remaining spikes, on the other hand, are corrected by smoothing methods: Savitzky-Golay (SG) filtering or robust locally weighted regression and smoothing. We have compared our new approach with the existing correction algorithms in terms of hemodynamic response function estimation using the following metrics: mean-squared error, peak-to-peak error ([Formula: see text]), Pearson's correlation ([Formula: see text]), and the area under the receiver operator characteristic curve. We found that spline-SG hybrid method provides reasonable improvements in all these metrics with a relatively short computational time. The dataset and the code used in this study are made available online for the use of all interested researchers.

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“…New imaging methods to fuse fNIRS measurements and fMRI data are presented to reveal the spatiotemporal dynamics of the hemodynamic responses with high spatiotemporal resolution across the brain (Yuan and Ye, 2013). Recognition algorithm is described for recognition whether one taps the left hand or the right hand (Hai et al, 2013). In the study, data with noises and artifacts collected from a multi-channel system is pre-processed using a Savitzky-Golay filter for getting more smooth data.…”

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confidence: 99%

Near-Infrared Spectroscopy (NIRS) in Functional Research of Prefrontal Cortex

2016

Frontiers Research Topics

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Functional near-infrared spectroscopy (fNIRS) uses specific wavelengths of light to provide measures of cerebral oxygenated and deoxygenated hemoglobin that are correlated with the functional magnetic functional imaging (fMRI) BOLD signal. fNIRS has emerged during the last decade as a promising non-invasive neuroimaging tool and has used to monitor various types of brain activities during motor and cognitive tasks with increasing interest from research communities. One can see classical comprehensive reviews of such investigations elsewhere (Hoshi, 2003;Koizumi et al., 2003;Obrig and Villringer, 2003). While various MRI methods documented in the literatures have provided essential information about the brain systems concerning these issues up to the present, they have two major limitations, their requirement for participant immobility and their high operational cost. Particularly, the former rules out the use of such techniques for investigating brain dynamics during every activities such as walking and running. On the other hand, fNIRS can be used under less body constraints than other imaging modalities require. Moreover, it is quiet (no operating sound). Thus, the conditions for performing fNIRS measurements are much more comfortable for human as well as non-human subjects (Wakita et al., 2010). In addition, it provides higher temporal resolution (faster sampling frequency). In this respect, fNIRS is useful for studying brain activity under more "natural" and therefore much more variable conditions though it can only monitor cortical regions with less spatial resolution (usually in the centimeter range).Since fNIRS is a non-invasive, safe and portable method, topics that NIRS researches could address are highly variable, and more diverse than those with other non-invasive imaging research techniques. That fact makes fNIRS an ideal candidate for monitoring activity of prefrontal cortex, which has been undertaken with respect to more broadly based issues across the broad expanse of research communities. In fact, many academic journals in diverse areas publish findings obtained from pioneering fNIRS measurements of prefrontal regions. We must admit that this diversity of fNIRS applications makes difficult to access accumulated experience. Protocols of fNIRS recordings from various investigations are not necessarily well-standardized and not necessarily well-grounded on available knowledge of appropriate fNIRS use. One of the purposes of the current research topic is to address this issue across both theoretical and empirical aspects.In order to realize the high potential of fNIRS, effective discrimination between physiological noise originating from forehead skin hemodynamic and cerebral signals is a pre-requisite. Main sources of physiological noise are global and local blood flow regulation processes on multiple time scales. Having attempted to identify the main physiological noise contributions in fNIRS forehead signals and to develop a method for physiological de-noising of fNIRS data, Kirilina et al. (2013)...

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Temporal hemodynamic classification of two hands tapping using functional near—infrared spectroscopy

Cited by 30 publications

References 51 publications

fNIRS-based brain-computer interfaces: a review

fNIRS-based brain-computer interfaces: a review

Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky–Golay filtering

Near-Infrared Spectroscopy (NIRS) in Functional Research of Prefrontal Cortex

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