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dc.contributor.authorDavis, Jeffery Jonathan (joshua)en_US
dc.contributor.authorKozma, Roberten_US
dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorFreeman, Walter J.en_US
dc.date.accessioned2018-08-21T05:56:42Z-
dc.date.available2018-08-21T05:56:42Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn2161-4393en_US
dc.identifier.urihttp://hdl.handle.net/11536/146543-
dc.description.abstractPrevious experimental studies on rabbits using electrocorticograms (ECoGs) over the cortical surface indicate spatio-temporal dynamics in the form of amplitude modulation (AM) patterns, which intermittently collapse at theta rates and give rise to rapidly propagating phase modulated (PM) patterns. The observed dynamics have been shown to be of cognitive relevance carrying useful information on the meaning of sensory information perceived by the subject. We have extended these studies to human scalp EEG measurements, which show evidence that cognitively relevant AM and PM patterns are observable by non-intrusive experimental techniques as well. The present work develops experimental techniques for studying cognitively relevant spatio-temporal neural dynamics using a high-density EEG array. Theoretical considerations indicate that the required spatial resolution to detect and categorize amplitude and phase patterns should be in the range of 3-5 mm. A prototype 1-dimensional array (MINDO-48S) has been developed, which has 48 electrodes in a flexible linear array of 5 mm spacing. The present work focuses on the extraction of broadly distributed spatio-temporal patterns, which carry cognitively relevant information. Preliminary analysis of the signal-to-noise ratio indicates that the sensitivity of the experiment allows the predicted AM patterns to be measured.en_US
dc.language.isoen_USen_US
dc.subjectScalp Electroencephalogram (EEG)en_US
dc.subjectHigh-resolution arrayen_US
dc.subjectAmplitude Modulation (AM)en_US
dc.subjectPhase Modulation (PM)en_US
dc.subjectNeurodynamicsen_US
dc.titleSpatio-Temporal EEG Pattern Extraction Using High-Density Scalp Arraysen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)en_US
dc.citation.spage889en_US
dc.citation.epage896en_US
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000399925501010en_US
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