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

By Behnam Molavi, Guy Albert Dumont. Physiological Measurement, 2012

365 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/772500a98144a3371017cda6699aece55cea7c9a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Robert J. Cooper
2. Louis Gagnon
3. Juliette Selb
4. David A. Boas
5. Sungho Tak
6. Jong Chul Ye
7. Maria Angela Franceschini
8. Theodore J. Huppert

In Cognitive Neuroscience

1. Helle Klingenberg Iversen
2. David A. Boas

In Radiology, Nuclear Medicine and Imaging

1. Robert J. Cooper
2. Louis Gagnon
3. Juliette Selb
4. David A. Boas
5. Sungho Tak
6. Jong Chul Ye
7. Maria Angela Franceschini
8. Theodore J. Huppert

Citations in the corpus

• A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy (2012, methodology): “Signal filtering based on a discrete wavelet transform has also been applied to the removal of motion artifacts from NIRS data (Molavi and Dumont, 2012).”
• Temporal Derivative Distribution Repair (TDDR): A motion correction method for fNIRS (2018, neutral): “wavelet-based methods (Chiarelli et al., 2015; Molavi and Dumont, 2012) deals well with motion spikes but exacerbates baseline shift artifacts.”