@conference{Rouxel2000,

title = {Unsupervised adaptive separation of impulse signals applied to EEG analysis},

author = {Alexandre Rouxel and Daniel Le Guennec and Odile Macchi},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=861997},

doi = {10.1109/ICASSP.2000.861997},

year  = {2000},

date = {2000-06-05},

volume = {1},

pages = {420-423},

address = {Turkey},

organization = {IEEE International Conference on Acoustics, Speech, and Signal Processing},

abstract = {In this paper the theoretical properties of a novel self adaptive

source separation algorithm are studied. It is a normalized

version of a modified relative gradient. It is shown that its

stability domain in terms of the normalized kurtosises of

sources is complementary of the unmodified gradient

algorithm. So it can separate a source with a very high kurtosis

from other sources having positive kurtosis. The algorithm is

then used to analyze EEG signals because they often have

positive kurtosises especially for patients suffering from

epilepsy. The good behavior of this novel algorithm is

illustrated via simulated data and then demonstrated with real

signals in an EEG analysis to separate an epileptic source from

other brain signals},

note = {Pre SFI},

keywords = {analysis},

pubstate = {published},

tppubtype = {conference}

}