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dc.contributor.author黃泊硯en_US
dc.contributor.authorHuang, Po-Yenen_US
dc.contributor.author范倫達en_US
dc.contributor.authorVan, Lan-Daen_US
dc.date.accessioned2014-12-12T01:59:13Z-
dc.date.available2014-12-12T01:59:13Z-
dc.date.issued2011en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT079955540en_US
dc.identifier.urihttp://hdl.handle.net/11536/50456-
dc.description.abstract本論文提出了一個多功能和成本效益的腦電圖信號處理器,支持重新參考(rereference),同步平均(Synchronized Average),移動平均線(Moving Average),並快速獨立分量分析(FastICA)。利用Gram - Schmidt過程提出了相應的硬件,可能會在兩個主要算法FastICA預處理和循環再用。在大量的渠道的狀況下,Gram - Schmidt的預處理算法的計算複雜度小於原CORDIC為基礎的預處理算法,為了要符合成本效益,粗分段近似雙曲正切函數和合適的記憶體大小也都是重要的議題。多功能和成本效益的腦電圖信號處理器在台積電的90納米 CMOS製程技術實現,在100兆赫1.43平方毫米大小的電壓為 1.0,功耗為22.0毫瓦。從實驗結果來看,處理器每個功能達到足夠的相關係數。zh_TW
dc.description.abstractThis thesis presents a multi-function and cost-effective EEG signal processor, which supports rereference, synchronized average, moving average, and fast independent component analysis (FastICA). A Gram-Schmidt process for FastICA preprocessing is proposed such that the corresponding hardware may be reused in two main algorithms of FastICA preprocessing and iteration. From computational analysis, the Gram-Schmidt based preprocessing algorithm has less computation complexity than the original CORDIC-based preprocessing algorithm, especially in large number of channels. Coarse piecewise linear approximation of hyperbolic tangent function and choosing satisfactory memory size are presented to be cost-effective. The multi-function and cost-effective EEG signal processor implemented in the TSMC 90nm CMOS technology process consumes 22.0 mW at 100 MHz at 1.0 voltage with a size of 1.43 mm2 when moving average is operating. From the experimental results, the processor achieves satisfactory correlation coefficient for each function.en_US
dc.language.isoen_USen_US
dc.subject腦電波zh_TW
dc.subject獨立成分分析zh_TW
dc.subject處理器zh_TW
dc.subjectEEGen_US
dc.subjectICAen_US
dc.subjectprocessoren_US
dc.title多功能低成本EEG訊號處理器設計與實現zh_TW
dc.titleDesign and Implementation of a Multi-function Cost-effective EEG Signal Processoren_US
dc.typeThesisen_US
dc.contributor.department資訊科學與工程研究所zh_TW
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