What we can and cannot (yet) do with functional near infrared spectroscopy

Strait, Megan; Scheutz, Matthias

doi:10.3389/fnins.2014.00117

Cited by 87 publications

(66 citation statements)

References 81 publications

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“…Indeed, pre-processing of fNIRS recordings is essential to remove artefacts arising from motion and physiological noise, while remaining mindful that such methods may introduce systematic errors into the data, or conversely discard useful signals unnecessarily. Other measures to improve fNIRS recordings by increasing optode contact with the scalp, such as applying bandages around the optodes and limiting participant motion using a chin rest have been described (Strait and Scheutz, 2014).…”

Section: Discussionmentioning

confidence: 99%

Cortical cross-modal plasticity following deafness measured using functional near-infrared spectroscopy

Dewey

Hartley

2015

Hearing Research

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Evidence from functional neuroimaging studies suggests that the auditory cortex can become more responsive to visual and somatosensory stimulation following deafness, and that this occurs predominately in the right hemisphere. Extensive cross-modal plasticity in prospective cochlear implant recipients is correlated with poor speech outcomes following implantation, highlighting the potential impact of central auditory plasticity on subsequent aural rehabilitation. Conversely, the effects of hearing restoration with a cochlear implant on cortical plasticity are less well understood, since the use of most neuroimaging techniques in CI recipients is either unsafe or problematic due to the electromagnetic artefacts generated by CI stimulation. Additionally, techniques such as functional magnetic resonance imaging (fMRI) are confounded by acoustic noise produced by the scanner that will be perceived more by hearing than by deaf individuals. Subsequently it is conceivable that auditory responses to acoustic noise produced by the MR scanner may mask auditory cortical responses to non-auditory stimulation, and render inter-group comparisons less significant. Uniquely, functional near-infrared spectroscopy (fNIRS) is a silent neuroimaging technique that is non-invasive and completely unaffected by the presence of a CI. Here, we used fNIRS to study temporal-lobe responses to auditory, visual and somatosensory stimuli in thirty profoundly-deaf participants and thirty normally-hearing controls. Compared with silence, acoustic noise stimuli elicited a significant group fNIRS response in the temporal region of normally-hearing individuals, which was not seen in profoundly-deaf participants. Visual motion elicited a larger group response within the right temporal lobe of profoundly-deaf participants, compared with normally-hearing controls. However, bilateral temporal lobe fNIRS activation to somatosensory stimulation was comparable in both groups. Using fNIRS these results confirm that auditory deprivation is associated with cross-modal plasticity of visual inputs to auditory cortex. Although we found no evidence for plasticity of somatosensory inputs, it is possible that our recordings may have included activation of somatosensory cortex that masked any group differences in auditory cortical responses due to the limited spatial resolution associated with fNIRS.

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Section: Discussionmentioning

confidence: 99%

Cortical cross-modal plasticity following deafness measured using functional near-infrared spectroscopy

Dewey

Hartley

2015

Hearing Research

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“…Non-invasive brain-computer interface (BCI) methods measure brain activities either by detecting the electrophysiological signals [1][2][3] or by determining the hemodynamic responses [4][5][6][7][8][9][10]. The electrophysiological phenomena are generated due to neuronal firing as a result of brain tasks [1].…”

Section: Introductionmentioning

confidence: 99%

Passive BCI based on drowsiness detection: an fNIRS study

Khan

Hong

2015

Biomed. Opt. Express

184

136

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Abstract:We use functional near-infrared spectroscopy (fNIRS) to discriminate the alert and drowsy states for a passive brain-computer interface (BCI). The passive brain signals for the drowsy state are acquired from the prefrontal and dorsolateral prefrontal cortex. The experiment is performed on 13 healthy subjects using a driving simulator, and their brain activity is recorded using a continuous-wave fNIRS system. Linear discriminant analysis (LDA) is employed for training and testing, using the data from the prefrontal, left-and right-dorsolateral prefrontal regions. For classification, eight features are tested: mean oxyhemoglobin, mean deoxyhemoglobin, skewness, kurtosis, signal slope, number of peaks, sum of peaks, and signal peak, in 0~5, 0~10, and 0~15 second time windows, respectively. The results show that the best performance for classification is achieved using mean oxyhemoglobin, the signal peak, and the sum of peaks as features. The average accuracies in the right dorsolateral prefrontal cortex (83.1, 83.4 and 84.9% in the 0~5, 0~10 and 0~15 second time windows, respectively) show that the proposed method has an effective utility for detection of drowsiness for a passive BCI.

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“…As such, an asymmetry index has been developed which measures the difference in EEG activity in the alpha band (8)(9)(10)(11)(12) from the left to the right hemisphere; the index has been shown to be correlated with the valence emotion primitive [47,48]. Moreover, the beta frequency band (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) power at the medial prefrontal cortex (MPC) has been associated with arousal [49].…”

Section: Abci Featuresmentioning

confidence: 99%

“…Functional NIRS, on the other hand, tracks cerebral hemodynamics with better spatial resolution than EEG, but with relatively poor temporal precision (order of seconds) [24]. Overall, EEG-based aBCIs have been more widely used and reported in the literature, but fNIRS is quickly gaining grounds [25].…”

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confidence: 99%

Using affective brain-computer interfaces to characterize human influential factors for speech quality-of-experience perception modelling

Gupta

Laghari

Banville

et al. 2016

Hum. Cent. Comput. Inf. Sci.

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As new speech technologies emerge, telecommunication service providers have to provide superior user experience in order to remain competitive. To this end, quality-of-experience (QoE) perception modelling and measurement has become a key priority. QoE models rely on three influence factors: technological, contextual and human. Existing solutions have typically relied on the former two and human influence factors (HIFs) have been mostly neglected due to difficulty in measuring them. In this paper, we show that measuring human affective states is important for QoE measurement and propose the use of affective brain-computer interfaces (aBCIs) for objective measurement of perceived QoE for two emerging speech technologies, namely far-field hands-free communications and text-to-speech systems. When incorporating subjectively-derived HIFs into the QoE model, gains of up to 26.3 % could be found relative to utilizing only technological factors. When utilizing HIFs derived from an electroencephalography (EEG) based aBCI, in turn, gains of up to 14.5 % were observed. These findings show the importance of using aBCIs in QoE measurement and also highlight that further improvement may be warranted once improved affective state correlates are found from EEGs and/or other neurophysiological modalities.

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What we can and cannot (yet) do with functional near infrared spectroscopy

Cited by 87 publications

References 81 publications

Cortical cross-modal plasticity following deafness measured using functional near-infrared spectroscopy

Cortical cross-modal plasticity following deafness measured using functional near-infrared spectroscopy

Passive BCI based on drowsiness detection: an fNIRS study

Using affective brain-computer interfaces to characterize human influential factors for speech quality-of-experience perception modelling

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