IEICE TRANSACTIONS on Fundamentals
Visualization of Brain Activities of Single-Trial and Averaged Multiple-Trials MEG Data
Summary :
Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.9 pp.2294-2302
- Publication Date
- 2003/09/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
- Category
- Neuro, Fuzzy, GA
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Yoshio KONNO, Jianting CAO, Takayuki ARAI, Tsunehiro TAKEDA, "Visualization of Brain Activities of Single-Trial and Averaged Multiple-Trials MEG Data" in IEICE TRANSACTIONS on Fundamentals,
vol. E86-A, no. 9, pp. 2294-2302, September 2003, doi: .
Abstract: Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_9_2294/_p
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@ARTICLE{e86-a_9_2294,
author={Yoshio KONNO, Jianting CAO, Takayuki ARAI, Tsunehiro TAKEDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Visualization of Brain Activities of Single-Trial and Averaged Multiple-Trials MEG Data},
year={2003},
volume={E86-A},
number={9},
pages={2294-2302},
abstract={Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - Visualization of Brain Activities of Single-Trial and Averaged Multiple-Trials MEG Data
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2294
EP - 2302
AU - Yoshio KONNO
AU - Jianting CAO
AU - Takayuki ARAI
AU - Tsunehiro TAKEDA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2003
AB - Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.
ER -
English
