scholarly journals Parallel Factor Analysis for multidimensional decomposition of fNIRS data - A validation study

2019 ◽  
Author(s):  
Hüsser Alejandra ◽  
Caron-Desrochers Laura ◽  
Tremblay Julie ◽  
Vannasing Phetsamone ◽  
Martínez-Montes Eduardo ◽  
...  
Keyword(s):  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Functional Near Infrared Spectroscopy ◽  
Cerebral Activation ◽  
Artifact Correction ◽  
Verbal Fluency Task ◽  
Time Space ◽  
Concentration Changes ◽  
Parallel Factor

AbstractSignificanceFunctional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared lights to estimate cerebral hemodynamic response, based on concentration changes in oxygenated and deoxygenated hemoglobin. A multi-dimensional decomposition technique, parallel factor (PARAFAC) analysis, has been validated for the identification of artifacts and cerebral activation patterns in electroencephalography and neuroimaging.AimWe aimed at introducing and validating the use of the PARAFAC model for fNIRS data analysis, which is inherently multidimensional (time, space, wavelength).ApproachEighteen healthy adults underwent fNIRS acquisition during a verbal fluency task. The signal-to-noise ratio and Pearson’s correlation were used to evaluate the efficacy of PARAFAC for motion artifact correction. Temporal, spatial and hemodynamic characteristics of the PARAFAC component allowed to identify task-related cerebral activations.ResultsMotion artifact correction with PARAFAC led to significant improvements in data quality and other advantages as compared to traditional 2D approaches (ICA, tPCA). Although PARAFAC revealed a slightly more distributed functional network, temporal and spatial characteristics of the task-related brain activation identified with PARAFAC mostly overlapped with those obtained with commonly used approaches.ConclusionThis study describes the first implementation of PARAFAC in fNIRS and supports it as a promising data-driven alternative for multi-dimensional data analyses in fNIRS.

scholarly journals A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5117
Author(s):  
David Perpetuini ◽  
Daniela Cardone ◽  
Chiara Filippini ◽  
Antonio Maria Chiarelli ◽  
Arcangelo Merla
Keyword(s):  
Wavelet Transform ◽  
Infrared Thermography ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Video Tracking ◽  
Wavelet Coherence ◽  
Functional Near Infrared Spectroscopy ◽  
Artifact Correction ◽  
Physiological Signal

Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes’ movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes’ movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes’ movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.

scholarly journals Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2957 ◽  
Author(s):  
Gihyoun Lee ◽  
Sang Jin ◽  
Jinung An
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Motion Artifact ◽  
Correction Method ◽  
Motion Artifacts ◽  
Functional Near Infrared Spectroscopy ◽  
Artifact Correction ◽  
Performance Evaluation Index

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

scholarly journals MOTION ARTIFACT REDUCTION IN FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS BY AUTOREGRESSIVE MOVING AVERAGE MODELING BASED KALMAN FILTERING

2013 ◽  
Vol 06 (04) ◽  
pp. 1350035
Author(s):  
MEHDI AMIAN ◽  
S. KAMALEDIN SETAREHDAN
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Motion Artifact ◽  
Space Representation ◽  
Autoregressive Moving Average ◽  
Functional Near Infrared Spectroscopy ◽  
State Space Representation

Functional near infrared spectroscopy (fNIRS) is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2) and deoxyhemoglobin (HHb) concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in such applications. In this paper, the autoregressive moving average (ARMA) modeling of the fNIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal. Results are compared to the autoregressive model (AR) based approach, which has been done previously, and show that the ARMA models outperform AR models. We attribute it to the richer structure, containing more terms indeed, of ARMA than AR. We show that the signal to noise ratio (SNR) is about 2 dB higher for ARMA based method.

scholarly journals Open-Access fNIRS Dataset for Classification of Unilateral Finger- and Foot-Tapping

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1486
Author(s):  
SuJin Bak ◽  
Jinwoo Park ◽  
Jaeyoung Shin ◽  
Jichai Jeong
Keyword(s):  
Open Access ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Finger Tapping ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Svm Classification ◽  
Right Hand ◽  
Foot Tapping ◽  
Concentration Changes

Numerous open-access electroencephalography (EEG) datasets have been released and widely employed by EEG researchers. However, not many functional near-infrared spectroscopy (fNIRS) datasets are publicly available. More fNIRS datasets need to be freely accessible in order to facilitate fNIRS studies. Toward this end, we introduce an open-access fNIRS dataset for three-class classification. The concentration changes of oxygenated and reduced hemoglobin were measured, while 30 volunteers repeated each of the three types of overt movements (i.e., left- and right-hand unilateral complex finger-tapping, foot-tapping) for 25 times. The ternary support vector machine (SVM) classification accuracy obtained using leave-one-out cross-validation was estimated at 70.4% ± 18.4% on average. A total of 21 out of 30 volunteers scored a superior binary SVM classification accuracy (left-hand vs. right-hand finger-tapping) of over 80.0%. We believe that the introduced fNIRS dataset can facilitate future fNIRS studies.

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