Search results for "naturalistic music"

showing 4 items of 4 documents

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

2021

AbstractPrevious researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli…

masennusElementary cognitive taskComputer scienceBiomedical EngineeringmusiikkiElectroencephalographyMusic listeningvärähtelytInternal MedicinemedicineHumansTensor decompositionEEGDepressive Disorder Majormedicine.diagnostic_testQuantitative Biology::Neurons and CognitionDepressionsignaalinkäsittelyGeneral NeuroscienceFunctional connectivityRehabilitationBrainComputer Science::Software Engineeringsignaalianalyysihermoverkot (biologia)ElectroencephalographyHyperconnectivitymajor depression disorder naturalistic music stimuli oscillatory networksMagnetic Resonance ImagingPotential biomarkersCorrelation analysiscoupled tensor decompositiondynamic functional connectivitykognitiivinen neurotiedeNeuroscienceMusicärsykkeet
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Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization

2021

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserve…

masennusmajor depressive disordersignaalinkäsittelymusiikkinaturalistic music stimulisignaalianalyysiNeurosciences. Biological psychiatry. NeuropsychiatryHuman Neuroscienceconstrained tensor factorizationbehavioral disciplines and activitiesBehavioral NeurosciencePsychiatry and Mental healthNeuropsychology and Physiological PsychologyNeurologyCANDECOMP/PARAFACaivotutkimusEEGärsykkeetBiological PsychiatryRC321-571Original ResearchFrontiers in Human Neuroscience
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Generation of stimulus features for analysis of FMRI during natural auditory experiences

2014

In contrast to block and event-related designs for fMRI experiments, it becomes much more difficult to extract events of interest in the complex continuous stimulus for finding corresponding blood-oxygen-level dependent (BOLD) responses. Recently, in a free music listening fMRI experiment, acoustic features of the naturalistic music stimulus were first extracted, and then principal component analysis (PCA) was applied to select the features of interest acting as the stimulus sequences. For feature generation, kernel PCA has shown its superiority over PCA in various applications, since it can implicitly exploit nonlinear relationship among features and such relationship seems to exist genera…

Quantitative Biology::Neurons and CognitionComputer Science::Soundsignaalinkäsittelyfeature extractionfMRIkernel PCAkokeet (tutkimustoiminta)riippumattomien komponenttien analyysiICAPolynomial kernelnaturalistic music
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Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

2021

To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscil…

masennusmedicine.medical_specialtyComputer Networks and Communicationsneural oscillationsFeature extractionmusiikkiAlpha (ethology)musiikkipsykologiaMajor depressive disordernaturalistic music listeningAudiologyElectroencephalographyDIAGNOSISbehavioral disciplines and activities050105 experimental psychology03 medical and health sciences0302 clinical medicineSIGNALSmedicine0501 psychology and cognitive sciencesEEGRESTING-STATE NETWORKSmajor depressive disorderINDEPENDENT COMPONENT ANALYSISONGOING EEGmedicine.diagnostic_testsignaalinkäsittely05 social sciences3112 Neuroscienceshermoverkot (biologia)signaalianalyysiFUNCTIONAL CONNECTIVITYADULTSGeneral MedicineMagnetoencephalographymedicine.diseasebrain networksIndependent component analysisongoing EEGhumanitiesElectrophysiologyindependent component analysisFMRI DATAFeature (computer vision)SYNCHRONIZATIONMajor depressive disorderPsychology030217 neurology & neurosurgeryRESPONSESInternational Journal of Neural Systems
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