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dc.contributor.author狄斐立en_US
dc.contributor.authorTILLET, Philippeen_US
dc.contributor.author林進燈en_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2014-12-12T02:39:21Z-
dc.date.available2014-12-12T02:39:21Z-
dc.date.issued2013en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT070160813en_US
dc.identifier.urihttp://hdl.handle.net/11536/73963-
dc.description.abstract腦機接口的研究始於1970年代作為美國國家研究項目的一部分。此後,在信號處理的進展結合現代硬體,導致了這樣的系統,其應用範圍從視頻遊戲到生物醫療的應用更為豐富有趣。 這項工作的目的,是提出技術,增強腦電波之訊號處理的狀態。具體來說,我們建議設置一個現代化的硬體和軟體技術。我們透過一個新的實驗驗證了獨立成分分析(ICA),這比參考值更快、更準確的驗證了我們的想法zh_TW
dc.description.abstractResearch on Brain Computer Interfaces (BCIs) began in the 1970s as part of an American national project. Since then, the advances in signal processing combined with the emergence of modern hardware led to a surge of interest in such systems, whose applications range from video games to biomedicals. The processing of such increasingly accurate signals has become a strenuous task, which requires to leverage the most recent algorithms on the most recent hardware. This work seeks to propose techniques to update the current Brain-Computer Interfacing framework so as to take these new problems into account. Specifically, we suggest a set modern hardware and software techniques which could be used to enhance the state of EEG signal analysis. We validate our considerations through a novel implementation of the Independent Component Analysis problem, which largely outperforms the reference in terms of speed and accuracy, when separating dense EEG sources.en_US
dc.language.isoen_USen_US
dc.subject高性能計算zh_TW
dc.subject最优化zh_TW
dc.subject獨立成分分析(ICA)zh_TW
dc.subject腦電波之訊號處理zh_TW
dc.subjectHigh-Performance Computingen_US
dc.subjectUnconstrained Optimizationen_US
dc.subjectIndependent Component Analysis (ICA)en_US
dc.subjectEEG Signal Analysisen_US
dc.title高通量腦電波之訊號處理zh_TW
dc.titleOn high-throughput EEG signal analysisen_US
dc.typeThesisen_US
dc.contributor.department電機資訊國際學程zh_TW
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