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研究生:邱昱傑
研究生(外文):Chiu, Yu-Chieh
論文名稱:運用ROC與SVM分析法探討背景音樂對購物網站消費程序之腦電波變化
論文名稱(外文):Using ROC and SVM method to discussion EEG variability for background music on shopping Websites consumer process
指導教授:陶家珍陶家珍引用關係賴建榮賴建榮引用關係
指導教授(外文):Tao, Chia-JenLai, Chien-Jung
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:工業工程與管理系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:121
中文關鍵詞:背景音樂購物網站腦電波ROC分析法SVM分析法
外文關鍵詞:Background MusicShopping WebsitesElectroencephalographic (EEG)ROC analysisSVM analysis
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線上購物網站潛藏著龐大的商機與市場,未來的網路技術也會越來越開放、便利。在購物網站上,將不只帶給消費者視覺上的感受,更能有聽覺、觸覺,甚或是嗅覺上的感觸。而音樂則會帶給人類心理層面上不同的影響,誘發不同的行為反應。因此本研究探討消費者在接受背景音樂的影響下,從進入購物網站後所產生的購物網站消費程序(瀏覽商品與購買決策) 過程中,腦電波(EEG)產生的階段性變化,並透過ROC與SVM分析法,找出其瀏覽與決策階段之功率值分界點與決策階段高相關之電極位置。
研究結果發現功率值在全程無音樂水準下,Delta波、Theta波、Beta波都有許多顯著之電極。在全程有音樂水準下,電極位置則全無顯著。關聯性分析結果顯示,在全程無音樂水準下,Delta波、Theta波、Alpha波、Beta波都有大量顯著之電極通道。在全程有音樂水準下,有顯著之電極通道則大量減少,推測可能是音樂介入後,模糊了瀏覽與決策之分界,音樂介入使決策功率值上升,使瀏覽與決策兩者功率值相近,差異減少導致顯著下降。ROC與SVM分析結果顯示,全程無音樂水準下Theta波F8電極位置AUC為屬於可接受的判別力,全程有音樂下Delta波FZ電極位置在SVM分析法中有良好的表現,兩者分界點的功率值可以做為全程無音樂與有音樂下瀏覽與決策階段間功率值分界點的依據。
根據驗證結果顯示Delta波FZ電極位置在全程有音樂水準瀏覽與決策的分類有著較佳結果。Theta波F8電極位置在全程無音樂水準瀏覽與決策的分類結果在某些受試者上,有著大部分被判為決策的結果。因此本研究建議可將Delta波FZ電極位置做為全程有音樂水準瀏覽與決策分類的電極位置,而Theta波F8電極位置分類效果並不完善,可再嘗試其他電極位置作為決策分類位置。

Online shopping site, hidden huge business opportunities and market, and music will bring human different effects on the psychological level, induce different behavioral responses. The purposes of this study discussion consumers of the impact of background music into shopping websites shopping websites arising consumer process (browse products and purchase decision), the browser EEG of the step change, using ROC and SVM analysis, to find the power of cut-off point between browse and decision and high correlation of the decision state electrode sites.
Power of data analysis showed the level of none music, there are many significant of electrodes sites in delta、theta and beta band. The level of full music, all the electrode sites is no significant. Coherence of data analysis showed the level of none music, there are large number of significant electrode channel in delta、theta、alpha and beta band . The level of full music, there is large number of to reduce significant the electrode channels. Speculate music intervention will fuzzy the boundaries of browser and decision, music intervention increased decision power, similar power between browser and decision resulted in significantly decreased. ROC and SVM of data analysis showed the level of none music, theta band F8 sites belonging acceptable discrimination of Area Under Curve (AUC), the level of full music, delta band FZ sites in the SVM analysis method has good performance, delta band FZ and theta F8 sites power value can be used cut-off point basis between browser and decision state in the full and none music level.
Validation results showed delta band FZ sites in the full music level have better classification results with browser and decision, theta band F8 sites in the none music level has a majority classified as decision results, in some participants. These results demonstrate delta band FZ sites can be used as classification standard with browser and decision in the full music level, theta band F8 sites classification results are not perfect, can to try other sites as a classification position of decision state.

摘要…………………………………………………………………………I
英文摘要……………………………………………………………………II
誌謝…………………………………………………………………………III
目錄…………………………………………………………………………IV
表目錄………………………………………………………………………VI
圖目錄………………………………………………………………………VII
第一章 緒論…………………………………………………………………1
1.1 研究背景…………………………………………………………… 1
1.2 研究動機…………………………………………………………… 3
1.3 研究目的…………………………………………………………… 6
1.4 研究流程…………………………………………………………… 6
第二章 文獻探討………………………………………………………… 8
2.1 賣場環境與消費者行為…………………………………………… 8
2.1.1 實體賣場………………………………………………………… 8
2.1.2 網路商店………………………………………………………… 10
2.2 賣場背景音樂之影響…………………………………………… 14
2.2.1 背景音樂於實體商店之應用………………………………… 14
2.2.2 網路商店背景音樂與消費者行為…………………………… 15
2.2.3 網路商店消費者決策程序……………………………………… 16
2.3 音樂對腦波與反應行為之影響…………………………………… 17
2.3.1 音樂特性………………………………………………………… 17
2.3.2 腦波之概念與性質……………………………………………… 18
2.3.3 音樂對腦波之影響與研究……………………………………… 22
2.3.4 音樂對行為反應之影響………………………………………… 24
2.4 ROC之發展與應用………………………………………………… 25
2.4.1 ROC分析法之起源……………………………………………… 25
2.4.2 ROC分析法之類別……………………………………………… 26
2.4.3 ROC曲線做法與空間生成……………………………………… 29
2.5 SVM之發展與應用………………………………………………… 30
2.5.1 SVM之概念……………………………………………………… 30
2.5.2最大邊際分類器………………………………………………… 32
2.5.3線性不可分割問題……………………………………………… 35
2.5.4核心函數………………………………………………………… 36
2.5.5支援向量機之應用……………………………………………… 38
第三章 研究方法………………………………………………………… 41
3.1研究架構……………………………………………………………… 41
3.1.1網站背景音樂…………………………………………………… 42
3.1.2網路商店消費程序……………………………………………… 43
3.1.3尋找功率值分界點……………………………………………… 43
3.1.4資料結果驗證與討論…………………………………………… 43
3.2 研究流程…………………………………………………………… 43
3.3 從原始腦波數據進行資料擷取…………………………………… 46
3.4 統計分析…………………………………………………………… 48
3.4.1敘述性統計分析…………………………………………………… 48
3.4.2成對樣本T檢定…………………………………………………… 49
3.5 ROC分析法………………………………………………………… 50
3.6 SVM分析法………………………………………………………… 53
第四章 資料分析結果與討論…………………………………………… 57
4.1 腦電波反應………………………………………………………… 57
4.1.1瀏覽與決策階段之腦電波變化…………………………………… 57
4.1.2瀏覽與決策階段之關聯性(Coherence)分析…………………… 70
4.2 ROC分析法之結果………………………………………………… 82
4.2.1 ROC分析法之腦電波功率值結果………………………………… 82
4.2.2 ROC分析法之腦電波關聯性(Coherence)結果………………… 88
4.3 SVM分析法之結果…………………………………………………… 89
4.3.1 SVM分析法之腦電波功率值結果………………………………… 89
4.3.2 SVM分析法之關聯度(Coherence)分析結果…………………… 91
4.3.3 SVM分析法一維度之關聯度(Coherence)分析結果…………… 95
4.4 ROC與SVM分析法結果驗證………………………………………… 96
4.4.1腦電波功率值與關聯性之時間變化圖…………………………… 96
4.4.2全程無音樂功率值之時間變化圖………………………………… 97
4.4.3全程有音樂功率值之時間變化圖..……………………………… 100
4.4.4全程無音樂關聯性之時間變化圖..……………………………… 103
4.4.5全程有音樂關聯性之時間變化圖..……………………………… 106
第五章 結論與建議……………………………………………………… 110
5.1 結論………………………………………………………………… 110
5.2 建議………………………………………………………………… 113
5.3 研究限制…………………………………………………………… 113
參考文獻…………………………………………………………………… 115

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[1] 2012年台灣寬頻網路使用調查報告,2012,線上檢索日期:2012年5月27日,TWNIC-財團法人台灣網路資訊中心,網址:http://www.twnic.net.tw/
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[3] IBM,IBM預測:電腦將涵蓋人類的五感,PCMAG,線上檢索日期:2012年12月18日,網址:http://www.pcmag.com/article2/0,2817,2413300,00.asp
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