Improving the quality of EEG data in patients with alzheimer's disease using ICA

François Benoit Vialatte, Jordi Solé-Casals, Monique Maurice, Charles Latchoumane, Nigel Hudson, Sunil Wimalaratna, Jaeseung Jeong, Andrzej Cichocki

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

18 Citations (Scopus)


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 differences 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 difference. More interestingly, ICA reduced the inter-subject variability within each group (σ=∈2.54 in the δ range before ICA, σ=∈1.56 after, Bartlett p = 0.046 after Bonferroni 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 findings suggests the novel usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of subject variability.

Original languageEnglish
Title of host publicationAdvances in Neuro-Information Processing - 15th International Conference, ICONIP 2008, Revised Selected Papers
Number of pages8
EditionPART 2
Publication statusPublished - 2009
Externally publishedYes
Event15th International Conference on Neuro-Information Processing, ICONIP 2008 - Auckland, New Zealand
Duration: 25 Nov 200828 Nov 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5507 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Neuro-Information Processing, ICONIP 2008
Country/TerritoryNew Zealand


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