Cited 0 time in
Cited 2 time in
Baseline Drift Detection Index using Wavelet Transform Analysis for tNIRS Signal
- Baseline Drift Detection Index using Wavelet Transform Analysis for tNIRS Signal
- Lee, Gihyoun; Lee, Seung Hyun; Jin, Sang Hyeon; An, Jinung
- DGIST Authors
- Lee, Gihyoun; Lee, Seung Hyun; An, Jinung
- Issue Date
- 5th International Winter Conference on Brain-Computer Interface, BCI 2017, 73-76
- 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.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
Brain Robot Interaction Lab
There are no files associated with this item.
- Convergence Research Center for Wellness2. Conference Papers
Division of IoT∙Robotics Convergence Research2. Conference Papers
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.