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Baseline Drift Detection Index using Wavelet Transform Analysis for tNIRS Signal

Title
Baseline Drift Detection Index using Wavelet Transform Analysis for tNIRS Signal
Authors
Lee, GihyounLee, Seung HyunJin, Sang HyeonAn, Jinung
DGIST Authors
Lee, Gihyoun; Lee, Seung Hyun; An, Jinung
Issue Date
2017-01-09
Citation
5th International Winter Conference on Brain-Computer Interface, BCI 2017, 73-76
Type
Conference
ISBN
9781510000000
Abstract
The general linear model (GLM) as a standard model for fMRI analysis has been applied to functional near-infrared spectroscopic (tNIRS) imaging analysis as weil. The GLM has drawback of failure in tNIRS signals, when they have drift globally. Wavelet based de-Trending technique is very popular to correct the baseline drift (BD) in tNIRS. However, this method globally distorted the total multichannel signals even if just one channel's signal was locally drifted. This paper suggests BD detection index to indicate BD as an objective index. The experiments show the performance of the proposed detection index as graphie results with current de-Trending algorithm.
URI
http://hdl.handle.net/20.500.11750/5439
DOI
10.1109/IWW-BCI.2017.7858163
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
Files:
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Collection:
Convergence Research Center for Wellness2. Conference Papers
Division of IoT∙Robotics Convergence Research2. Conference Papers


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