Abstract
In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms
for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to
investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can
help in the ...»»»»
In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms
for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to
investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can
help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency
bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum
number of selected components is investigated, in order to help decision processes for future works.^^^^
Citation:
Solé Casals, J., Vialatte, F., Chen, Z. & Cichocki, A. 2009, "COHERENCY AND SHARPNESS MEASURES BY USING ICA ALGORITHMS An Investigation for Alzheimer's Disease Discrimination", Biosignals 2009: Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing; 2nd International Conference on Bio-Inspired Systems and Signal Processing, eds. P. Encarnacao & A. Veloso, INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION, SETUBAL; AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL, JAN 14-17, 2009, pp. 468.