Baseline Drift Detectiün Index using Wavelet Transfürm Analysis für tNIRS Signal

Gihyoun Lee, Seung Hyun Lee, Sang Hyeon Jin, Jinung An

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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.

Original languageEnglish
Title of host publication5th International Winter Conference on Brain-Computer Interface, BCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-76
Number of pages4
ISBN (Electronic)9781509050963
DOIs
StatePublished - 16 Feb 2017
Event5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of
Duration: 9 Jan 201711 Jan 2017

Publication series

Name5th International Winter Conference on Brain-Computer Interface, BCI 2017

Conference

Conference5th International Winter Conference on Brain-Computer Interface, BCI 2017
Country/TerritoryKorea, Republic of
CityGangwon Province
Period9/01/1711/01/17

Keywords

  • Baseline drift detection
  • Detrending
  • FNIRS
  • Wavelet transform

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