6533b85ffe1ef96bd12c2680

RESEARCH PRODUCT

BENEFITS OF MULTI-DOMAIN FEATURE OF MISMATCH NEGATIVITY EXTRACTED BY NON-NEGATIVE TENSOR FACTORIZATION FROM EEG COLLECTED BY LOW-DENSITY ARRAY

Heikki LyytinenTiina Huttunen-scottQibin ZhaoJukka KaartinenTapani RistaniemiAndrzej CichockiAnh Huy PhanFengyu Cong

subject

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 processes

description

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.

yearjournalcountryeditionlanguage
2012-11-27International Journal of Neural Systems
https://doi.org/10.1142/s0129065712500256