Authors:
J. Solé-Casals
1
;
F. Vialatte
2
;
J. Pantel
3
;
D. Prvulovic
3
;
C. Haenschel
4
and
A. Cichocki
5
Affiliations:
1
1Digital Technologies Group, University of Vic, Spain
;
2
RIKEN Brain Science Institute, LABSP, Japan
;
3
Johann Wolfgang Goethe University, Germany
;
4
Bangor University, United Kingdom
;
5
2RIKEN Brain Science Institute, LABSP, Japan
Keyword(s):
EEG, Mild Cognitive Impairment, Alzheimer disease, ICA, BSS, Neural networks.
Abstract:
To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude (≥100 μV). We then evaluated the outcome of this cleaning by means of the classification of patients u
sing multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.
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