A resampling approach to estimate the stability of one-dimensional or multidimensional independent components.
When applying unsupervised learning techniques in biomedical data analysis, a key question is whether the estimated parameters of the studied system are reliable. In other words, can we assess the quality of the result produced by our learning technique? We propose resampling methods to tackle this question and illustrate their usefulness for blind-source separation (BSS). We demonstrate that our proposed reliability estimation can be used to discover stable one-dimensional or multidimensional independent components, to choose the appropriate BSS-model, to enhance significantly the separation performance, and, most importantly, to flag components that carry physical meaning. Application to different biomedical testbed data sets (magnetoencephalography (MEG)/electrocardiography (ECG)-recordings) underline the usefulness of our approach.
Meinecke
Frank
F
Department of Physics, University of Potsdam, 14469 Potsdam, Germany. frank.meinecke@first.fraunhofer.de
Ziehe
Andreas
A
Kawanabe
Motoaki
M
Müller
Klaus-Robert
KR
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods; D005072:Evoked Potentials, Auditory / Q000502:physiology; D005246:Feedback; D005260:Female; D005323:Fetal Monitoring / Q000379:methods; D006340:Heart Rate, Fetal / Q000502:physiology; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D011247:Pregnancy; D025341:Principal Component Analysis; D011786:Quality Control; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity