Wavelet-based motion artifact removal for functional near-infrared spectroscopy

Abstract

Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than -16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal.

Transient Artifact Reduction Algorithm (TARA) Based on Sparse Optimization

“…It has been found that wavelet methods compare favorably to other methods for the correction of motion artifacts in single-channel NIRS time series [9], [21], [24]. Fig.…”

“…However, if artifacts differ substantially among channels or if multiple channels are unavailable, then single-channel methods are needed [9], [21], [24]. Several methods for detecting and/or correcting motion artifacts in NIRS time series have been compared [9], [21], [24], [31], [32], leading to the conclusion that wavelet-based methods are more effective than other methods, especially for single-channel processing. Wavelets have also been shown effective for reducing ocular artifacts in EEG [2], [22].…”

“…To find such that satisfies (23), we equivalently find such that (27) That is, we seek to set in (24) so that is relatively noisefree with high probability. The value for accomplishing this is derived using optimality conditions from convex analysis.…”

“…Note that TARA provides a reasonable denoising result: the total signal is relatively noise-free, preserves the discontinuities in the data, and does not exhibit ringing around the discontinuities. Since wavelet methods have been successfully used for the correction of transient artifacts [9], [21], [24], we illustrate a wavelet-based decomposition in Fig. 7 of the same example signal as was considered in Fig.…”

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

“…These methods remove MAs by examining the characteristics of the signal [26][27][28][29][30][31][32][33]. One of these methods was developed by our group, the "movement artifact reduction algorithm" (MARA) [34].…”