TY - GEN
T1 - Decomposition algorithms for analysing brain signals
AU - Muller, K. R.
AU - Kohlmorgen, J.
AU - Ziehe, A.
AU - Blankertz, B.
N1 - Funding Information:
A.Z. was partly funded by DFG under contracts JA 379/52 and JA 379/71. G.N. and G.C. were supported by DFG grant MA 1782/3-1. We thank J?rn Rittweger, Gabriel Curio and Gunnar R?tsch for valuable discussions.
Publisher Copyright:
© 2000 IEEE.
PY - 2000
Y1 - 2000
N2 - Analyzing biomedical data-e.g. from the brain-we encounter fundamental problems that lie largely in the fields of signal processing and machine learning. The current paper presents at first a method to deal with non-stationary signals, subsequently the signal processing technique of independent component analysis (ICA) is reviewed. We use EEG recordings of continuous auditory perception as illustration for the discussed algorithms.
AB - Analyzing biomedical data-e.g. from the brain-we encounter fundamental problems that lie largely in the fields of signal processing and machine learning. The current paper presents at first a method to deal with non-stationary signals, subsequently the signal processing technique of independent component analysis (ICA) is reviewed. We use EEG recordings of continuous auditory perception as illustration for the discussed algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84962385710&partnerID=8YFLogxK
U2 - 10.1109/ASSPCC.2000.882455
DO - 10.1109/ASSPCC.2000.882455
M3 - Conference contribution
AN - SCOPUS:84962385710
T3 - IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, AS-SPCC 2000
SP - 105
EP - 110
BT - IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, AS-SPCC 2000
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium, AS-SPCC 2000
Y2 - 1 October 2000 through 4 October 2000
ER -