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Title:
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Improving the Quality of EEG Data in Patients With Alzheimers Disease Using ICA
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Author:
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Vialatte, François B.; Solé-Casals, Jordi; Maurice, Monique; Latchoumane, Charles-François V.; Hudson, Niegel; Wimalaratna, Sunil R.; Jaeseung, Jeonga; Cichocki, Andrej
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Other authors:
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Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Grup de Recerca en Tecnologies Digitals; International Conference on Neural Informations Processing (15è: 2008: Auckland); ICONIP 2008 |
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Notes:
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Does Independent Component Analysis (ICA) denature EEG
signals? We applied ICA to two groups of subjects (mild Alzheimer
patients and control subjects). The aim of this study was to examine
whether or not the ICA method can reduce both group di®erences and
within-subject variability. We found that ICA diminished Leave-One-
Out root mean square error (RMSE) of validation (from 0.32 to 0.28),
indicative of the reduction of group di®erence. More interestingly, ICA
reduced the inter-subject variability within each group (¾ = 2:54 in the
± range before ICA, ¾ = 1:56 after, Bartlett p = 0.046 after Bonfer-
roni correction). Additionally, we present a method to limit the impact
of human error (' 13:8%, with 75.6% inter-cleaner agreement) during
ICA cleaning, and reduce human bias. These ¯ndings suggests the novel
usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of
subject variability. |
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Subject(s):
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-Alzheimer, Malaltia d' |
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Rights:
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(c) Springer (The original publication is available at www.springerlink.com)
Tots els drets reservats
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Document type:
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Conference Object |
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Published by:
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Springer
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