Motion Artifact Removal for Functional Near Infrared Spectroscopy: A Comparison of Methods

2010 ◽  
Vol 57 (6) ◽  
pp. 1377-1387 ◽  
Author(s):  
F C Robertson ◽  
T S Douglas ◽  
E M Meintjes
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Motion Artifact ◽  
Comparison Of Methods ◽  
Artifact Removal ◽  
Functional Near Infrared Spectroscopy

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.

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