Search results for " independent component analysis"

showing 4 items of 4 documents

Semi-blind Source Extraction Methods: Application to the measurement of non-contact physiological signs

2018

Non-contact physiological measurements are highlydesirable in many biomedical fields such asdiagnosis of infants, geriartic patients, patients withextreme physical trauma, and fitness and well-being.Remote photoplethysmography is increasingly beingused for non-contact measurement of heart rate fromvideos which is one of the most common biomedicalproperty required for most medical diagnosis. Oneof the common techniques for performing remotephotoplethysmography involves using Blind SourceSeparation (BSS) methods to extract the cardiacsignal from video data.In this context, the objective of this thesis is todevelop different methods in the field of extractionand separation of sources by improv…

integration of biophysical constraints[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]L’analyse de composantes indépendantes contraint[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingRemote photoplethysmographyL’analyse de composantes indépendantes[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Méthodes d’extraction semi-aveugleSemi-blind source extraction methodsIntègration des contraintes biophysiquesConstrained Independent Component AnalysisPhotopléthysmographie à distance
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Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data

2015

An extension of group independent component analysis (GICA) is introduced, where multi-set canonical correlation analysis (MCCA) is combined with principal component analysis (PCA) for three-stage dimension reduction. The method is applied on naturalistic functional MRI (fMRI) images acquired during task-free continuous music listening experiment, and the results are compared with the outcome of the conventional GICA. The extended GICA resulted slightly faster ICA convergence and, more interestingly, extracted more stimulus-related components than its conventional counterpart. Therefore, we think the extension is beneficial enhancement for GICA, especially when applied to challenging fMRI d…

ta113MultisetPCAGroup (mathematics)business.industrydimension reductionSpeech recognitionDimensionality reductionPattern recognitionMusic listeningta3112naturalistic fMRIGroup independent component analysisPrincipal component analysistemporal cocatenationArtificial intelligenceCanonical correlationbusinessmultiset CCAMathematics
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Abnormal synchrony and effective connectivity in patients with schizophrenia and auditory hallucinations

2014

Auditory hallucinations (AH) are the most frequent positive symptoms in patients with schizophrenia. Hallucinations have been related to emotional processing disturbances, altered functional connectivity and effective connectivity deficits. Previously, we observed that, compared to healthy controls, the limbic network responses of patients with auditory hallucinations differed when the subjects were listening to emotionally charged words. We aimed to compare the synchrony patterns and effective connectivity of task-related networks between schizophrenia patients with and without AH and healthy controls. Schizophrenia patients with AH (n = 27) and without AH (n = 14) were compared with healt…

MaleCerebellumMVAR multivariate autoregressionHallucinationsAH auditory hallucinationsAuditory hallucinationsBPRS Brief Psychiatric Rating ScaleAudiologylcsh:RC346-429BOLD blood oxygenation level dependentDevelopmental psychologyFunctional connectivityCerebellumNeural PathwaysEffective connectivityICA-TC ICA-time courseFunctional connectivityEmotional stimuliMiddle AgedTemporal LobeICA independent component analysisSynchronymedicine.anatomical_structureNeurologySchizophreniaMRI functional magnetic resonance imaginglcsh:R858-859.7PsychologyAdultmedicine.medical_specialtyCognitive NeuroscienceEmotional processinglcsh:Computer applications to medicine. Medical informaticsArticleYoung AdultmedicineHumansRadiology Nuclear Medicine and imagingIn patientPANSS Positive and Negative Syndrome ScaleCoI component of interestCCTC cortico-cerebellar–thalamic–corticallcsh:Neurology. Diseases of the nervous systemAuditory CortexSPM statistical parametric mapsmedicine.diseaseGCCA Granger causal connectivity analysisAcoustic StimulationFISICA APLICADASchizophreniaAuditory stimuliPSYRATS Psychotic Symptom Rating ScaleNeurology (clinical)NeuroImage: Clinical
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Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

2023

Funding Information: We wish to thank the reviewers and editors for the useful comments to improve the paper a lot. We thank Dr. Hiroshi Morioka for the useful discussion at the beginning of the project. L.P. was funded in part by the European Research Council (No. 678578 ). A.H. was supported by a Fellowship from CIFAR, and the Academy of Finland. The authors acknowledge the computational resources provided by the Aalto Science-IT project, and also wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources. | openaire: EC/H2020/678578/EU//HRMEG Resting-state magnetoencephalography (MEG) data show complex but stru…

neuropalautenon-stationarityMEGsignaalinkäsittelyCognitive Neurosciencesyväoppiminensignaalianalyysineurofeedbackunsupervised learningdeep generative modelkoneoppiminenNeurologyresting-state networkmagnetoencephalography (MEG)nonlinear independent component analysis (ICA)NeuroImage
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