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dc.contributor.authorHuang, Wei-Chungen_US
dc.contributor.authorHung, Shao-Hangen_US
dc.contributor.authorChung, Jen-Fengen_US
dc.contributor.authorChang, Meng-Hsiuen_US
dc.contributor.authorVan, Lan-Daen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2014-12-08T15:47:44Z-
dc.date.available2014-12-08T15:47:44Z-
dc.date.issued2008en_US
dc.identifier.isbn978-1-4244-2878-6en_US
dc.identifier.urihttp://hdl.handle.net/11536/31920-
dc.description.abstractBlind source separation of independent sources from their mixtures is a common problem for multi-sensor applications in real world, for example, speech or biomedical signal processing. This paper presents an independent component analysis (ICA) method with information maximization (Infomax) update applied into 4-channel one-line EEG signal separation. This can be implemented on FPGA with a fixed-point number representation, and then the separated signals are transmitted via Bluetooth. As experimental results, the proposed design is faster 56 times than soft performance, and the correlation coefficients at least 80% with the absolute value are compared with off-line processing results. Finally, live demonstration is shown in the DE2 FPGA board, and the design is consisted of 16,605 logic elements.en_US
dc.language.isoen_USen_US
dc.subjectBluetoothen_US
dc.subjectfixed-pointen_US
dc.subjectICAen_US
dc.subjectbiomedical signalen_US
dc.subjectblind source separationen_US
dc.subjectmulti-sensoren_US
dc.subjectinformation maximizationen_US
dc.titleFPGA Implementation of 4-Channel ICA for On-line EEG Signal Separationen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2008 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE - INTELLIGENT BIOMEDICAL SYSTEMS (BIOCAS)en_US
dc.citation.spage65en_US
dc.citation.epage68en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000264876700018-
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