iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG.
iCanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG.
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DOI：
10.3390/s23020928
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发表时间：
2023-01-13
期刊：
影响因子：
--
通讯作者：
Downey RJ
中科院分区：
文献类型：
--
作者：
Gonsisko CB;Ferris DP;Downey RJ
Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r2 cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r2 = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.
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影响因子：
3.7
作者：
Downey, Ryan J.;Richer, Natalie;Gupta, Rohan;Liu, Chang;Pliner, Erika M.;Roy, Arkaprava;Hwang, Jungyun;Clark, David J.;Hass, Chris J.;Manini, Todd M.;Seidler, Rachael D.;Ferris, Daniel P.
通讯作者：
Ferris, Daniel P.
DOI：
10.1109/embc.2013.6609968
发表时间：
2013
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Mullen T;Kothe C;Chi YM;Ojeda A;Kerth T;Makeig S;Cauwenberghs G;Jung TP
通讯作者：
Jung TP
影响因子：
2.5
作者：
Gwin, Joseph T.;Gramann, Klaus;Ferris, Daniel P.
通讯作者：
Ferris, Daniel P.
影响因子：
7.8
作者：
Hyvärinen, A;Oja, E
通讯作者：
Oja, E
影响因子：
4.3
作者：
Gajbhiye, Pranjali;Tripathy, Rajesh Kumar;Pachori, Ram Bilas
通讯作者：
Pachori, Ram Bilas