使用小波法搭配分類樹及回歸樹分析腦波特徵

機構典藏 > College of Engineering > Graduate Institute & Department of Aerospace Engineering > Thesis > Item 987654321/105788

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Title:	使用小波法搭配分類樹及回歸樹分析腦波特徵
Other Titles:	Analysis of brainwave characteristics with wavelet and classification and regression trees
Authors:	林聖諺;Lin, Sheng-Yan
Contributors:	淡江大學航空太空工程學系碩士班蕭富元;Hsiao, Fu-Yuen
Keywords:	腦波;小波分析;多辨率分析;分類樹;brain;wavelet;Multiresoluion Analysis;Classification and Regression Trees
Date:	2015
Issue Date:	2016-01-22 15:05:21 (UTC+8)
Abstract:	本論文主要探討利用小波法對腦波進行濾波，並使用分類樹分類腦波特徵。近年來用腦波來控制物體移動的應用，有越來越廣泛的趨勢。可是由於腦波非常複雜，要將資訊從雜亂且微小的腦波中抽取出來，是一件非常困難的事。本研究使用市售的便宜腦波儀來搜集腦波數據，並採用小波法來進行濾波，最後使用分類樹的方法進行特徵分類。本研究成果日後可應用至使用腦波進行飛行器或地面載具的軌跡控制。 This thesis investigates the characteristics of brainwave using wavelet analysis method and classification and regression trees. Recently brainwave has been applied to wider and wider fields. However, it is very difficult to extract useful information from brainwave due to its complex nature. In this research we selected a commercial simple EEG to reduce the expense. Wavelet analysis is employed to analyze collected data, and using classification and regression trees to induction characteristics of brainwave. The result is applicable to navigation of ground or aerial vehicles in the future.
Appears in Collections:	[Graduate Institute & Department of Aerospace Engineering] Thesis

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