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dc.contributor.authorVigneron, Vincenten_US
dc.contributor.authorChen, Hsinen_US
dc.contributor.authorChen, Yen-Taien_US
dc.contributor.authorLai, Hsin-Yien_US
dc.contributor.authorChen, You-Yinen_US
dc.date.accessioned2017-04-21T06:49:41Z-
dc.date.available2017-04-21T06:49:41Z-
dc.date.issued2009en_US
dc.identifier.isbn978-3-642-03968-3en_US
dc.identifier.issn1865-0929en_US
dc.identifier.urihttp://hdl.handle.net/11536/134414-
dc.description.abstractWe describe in this paper advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. The programs are capable of detecting and classifying the spikes from multiple, simultaneously active neurons, even in situations where there is a high degree of spike waveform superposition on the recording channels. Sparse Decomposition (SD) approach was used to define the linearly independent signals underlying sensory information in cortical spike tiring patterns. We have investigated motor cortex responses recorded during movement in freely moving rats to provide evidence for the relationship between these patterns and special behavioral task. Ensembles of neurons were simultaneously recorded in this during long periods of spontaneous behaviour. Waveforms provided from the neural activity were then processed and classified. Typically, most information correlated across neurons in the ensemble were concentrated in a small number of signals. This showed that these encoding vectors functioned as a feature detector capable of selectively predicting significant sensory or behavioural events. Thus it encoded global magnitude of ensemble activity, caused either by combined sensory inputs or intrinsic network activity. SD on an overcomplete dictionary has recently attracted a lot of attention in the literature. because of its potential application in many different areas including Compressive Sensing (CS). SD approach is compared to the generative approach derived from the likelihood-based framework, in which each class is modeled by a known or unknown density function. The classification of electroencephalographic (EEG) waveforms present 2 main statistical issues: high dimensional data and signal representation.en_US
dc.language.isoen_USen_US
dc.subjectClassificationen_US
dc.subjectAtomic Decompositionen_US
dc.subjectSparse Decompositionen_US
dc.subjectOvercomplete Signal Representationen_US
dc.titleDictionary-Based Classification Models. Applications for Multichannel Neural Activity Analysisen_US
dc.typeProceedings Paperen_US
dc.identifier.journalENGINEERING APPLICATIONS OF NEURAL NETWORKS, PROCEEDINGSen_US
dc.citation.volume43en_US
dc.citation.spage378en_US
dc.citation.epage+en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000274227200035en_US
dc.citation.woscount1en_US
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