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NIRS-EEG joint imaging during transcranial direct current stimulation: Online parameter estimation with an autoregressive model.

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Title: NIRS-EEG joint imaging during transcranial direct current stimulation: Online parameter estimation with an autoregressive model.
Authors: Sood, Mehak1, Besson, Pierre2, Muthalib, Makii2, Jindal, Utkarsh1, Perrey, Stephane2, Dutta, Anirban3 adutta@ieee.org, Hayashibe, Mitsuhiro3
Source: Journal of Neuroscience Methods. Dec2016, Vol. 274, p71-80. 10p.
Subjects: Electroencephalography, Transcranial direct current stimulation, Autoregressive models, Electrophysiology, Near infrared spectroscopy
Abstract: Background Transcranial direct current stimulation (tDCS) has been shown to perturb both cortical neural activity and hemodynamics during (online) and after the stimulation, however mechanisms of these tDCS-induced online and after-effects are not known. Here, online resting-state spontaneous brain activation may be relevant to monitor tDCS neuromodulatory effects that can be measured using electroencephalography (EEG) in conjunction with near-infrared spectroscopy (NIRS). Method We present a Kalman Filter based online parameter estimation of an autoregressive (ARX) model to track the transient coupling relation between the changes in EEG power spectrum and NIRS signals during anodal tDCS (2 mA, 10 min) using a 4 × 1 ring high-definition montage. Results Our online ARX parameter estimation technique using the cross-correlation between log (base-10) transformed EEG band-power (0.5–11.25 Hz) and NIRS oxy-hemoglobin signal in the low frequency (≤0.1 Hz) range was shown in 5 healthy subjects to be sensitive to detect transient EEG-NIRS coupling changes in resting-state spontaneous brain activation during anodal tDCS. Conventional sliding window cross-correlation calculations suffer a fundamental problem in computing the phase relationship as the signal in the window is considered time-invariant and the choice of the window length and step size are subjective. Here, Kalman Filter based method allowed online ARX parameter estimation using time-varying signals that could capture transients in the coupling relationship between EEG and NIRS signals. Conclusion Our new online ARX model based tracking method allows continuous assessment of the transient coupling between the electrophysiological (EEG) and the hemodynamic (NIRS) signals representing resting-state spontaneous brain activation during anodal tDCS. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Background Transcranial direct current stimulation (tDCS) has been shown to perturb both cortical neural activity and hemodynamics during (online) and after the stimulation, however mechanisms of these tDCS-induced online and after-effects are not known. Here, online resting-state spontaneous brain activation may be relevant to monitor tDCS neuromodulatory effects that can be measured using electroencephalography (EEG) in conjunction with near-infrared spectroscopy (NIRS). Method We present a Kalman Filter based online parameter estimation of an autoregressive (ARX) model to track the transient coupling relation between the changes in EEG power spectrum and NIRS signals during anodal tDCS (2 mA, 10 min) using a 4 × 1 ring high-definition montage. Results Our online ARX parameter estimation technique using the cross-correlation between log (base-10) transformed EEG band-power (0.5–11.25 Hz) and NIRS oxy-hemoglobin signal in the low frequency (≤0.1 Hz) range was shown in 5 healthy subjects to be sensitive to detect transient EEG-NIRS coupling changes in resting-state spontaneous brain activation during anodal tDCS. Conventional sliding window cross-correlation calculations suffer a fundamental problem in computing the phase relationship as the signal in the window is considered time-invariant and the choice of the window length and step size are subjective. Here, Kalman Filter based method allowed online ARX parameter estimation using time-varying signals that could capture transients in the coupling relationship between EEG and NIRS signals. Conclusion Our new online ARX model based tracking method allows continuous assessment of the transient coupling between the electrophysiological (EEG) and the hemodynamic (NIRS) signals representing resting-state spontaneous brain activation during anodal tDCS. [ABSTRACT FROM AUTHOR]
ISSN:01650270
DOI:10.1016/j.jneumeth.2016.09.008