Search results for "Tensor factorization"

showing 5 items of 5 documents

Classifying Healthy Children and Children with Attention Deficit through Features Derived from Sparse and Nonnegative Tensor Factorization Using Even…

2010

In this study, we use features extracted by Nonnegative Tensor Factorization (NTF) from event-related potentials (ERPs) to discriminate healthy children and children with attention deficit (AD). The peak amplitude of an ERP has been extensively used to discriminate different groups of subjects for the clinical research. However, such discriminations sometimes fail because the peak amplitude may vary severely with the increased number of subjects and wider range of ages and it can be easily affected by many factors. This study formulates a framework, using NTF to extract features of the evoked brain activities from time-frequency represented ERPs. Through using the estimated features of a ne…

Amplitudebusiness.industryEvent-related potentialAttention deficitMismatch negativityPattern recognitionNonnegative matrixArtificial intelligenceNonnegative tensor factorizationbusinessOddball paradigmNon-negative matrix factorizationMathematics
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BENEFITS OF MULTI-DOMAIN FEATURE OF MISMATCH NEGATIVITY EXTRACTED BY NON-NEGATIVE TENSOR FACTORIZATION FROM EEG COLLECTED BY LOW-DENSITY ARRAY

2012

Through exploiting temporal, spectral, time-frequency representations, and spatial properties of mismatch negativity (MMN) simultaneously, this study extracts a multi-domain feature of MMN mainly using non-negative tensor factorization. In our experiment, the peak amplitude of MMN between children with reading disability and children with attention deficit was not significantly different, whereas the new feature of MMN significantly discriminated the two groups of children. This is because the feature was derived from multi-domain information with significant reduction of the heterogeneous effect of datasets.

MaleReading disabilityAdolescentComputer Networks and CommunicationsSpeech recognitionMismatch negativityContingent Negative VariationElectroencephalographybehavioral disciplines and activitiesDyslexiaReduction (complexity)Event-related potentialmedicineHumansChildMathematicsModels StatisticalTensor factorizationmedicine.diagnostic_testbusiness.industryElectroencephalographyPattern recognitionGeneral MedicineBrain WavesAmplitudeAcoustic StimulationAttention Deficit Disorder with HyperactivityFeature (computer vision)Case-Control StudiesAuditory PerceptionEvoked Potentials AuditoryFemaleArtificial intelligencebusinesspsychological phenomena and processesInternational Journal of Neural Systems
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Multi-domain Feature of Event-Related Potential Extracted by Nonnegative Tensor Factorization: 5 vs. 14 Electrodes EEG Data

2012

As nonnegative tensor factorization (NTF) is particularly useful for the problem of underdetermined linear transform model, we performed NTF on the EEG data recorded from 14 electrodes to extract the multi-domain feature of N170 which is a visual event-related potential (ERP), as well as 5 typical electrodes in occipital-temporal sites for N170 and in frontal-central sites for vertex positive potential (VPP) which is the counterpart of N170, respectively. We found that the multi-domain feature of N170 from 5 electrodes was very similar to that from 14 electrodes and more discriminative for different groups of participants than that of VPP from 5 electrodes. Hence, we conclude that when the …

Vertex (graph theory)Underdetermined systemDiscriminative modelFeature (computer vision)business.industryEvent-related potentialElectrodeFeature extractionPattern recognitionArtificial intelligenceNonnegative tensor factorizationbusinessMathematics
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Low-rank approximation based non-negative multi-way array decomposition on event-related potentials

2014

Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD…

low-rank approximationEvent-related potentialtensor decompositionnon-negative tensor factorizationmulti-domain featurenon-negative canonical polyadic decomposition
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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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