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研究生:林威志
研究生(外文):Wei-Chih Lin
論文名稱:音樂刺激下腦波信號分析
論文名稱(外文):Analysis of the EEG Signals in Response to Musical Signal Stimuli
指導教授:徐建業徐建業引用關係邱泓文邱泓文引用關係
指導教授(外文):Chien-Yeh HsuHung-Wen Chiu
學位類別:碩士
校院名稱:臺北醫學大學
系所名稱:醫學資訊研究所
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:55
中文關鍵詞:音樂頻譜分析獨立元件分析法腦波導程相關係數
外文關鍵詞:MusicSpectral AnalysisIndependent Component Analysis (ICA)Electroencephalographic (EEG)ChannelCorrelation
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近幾年中,越來越多研究在探討音樂在生理上之影響。腦波是過去被廣泛應用在測量腦部活動上之生理訊號。本研究中,我們嘗試運用頻譜分析和獨立元件分析法來分析受測者對不同類型音樂刺激下之腦波反應。

本研究擷取三十二位受測者於接受不同音樂訊號刺激下之腦波,音樂訊號刺激分別為重金屬樂(Metal)、鋼琴奏鳴曲(Sonata)、受測者自選音樂(Favorite)和無音樂狀態(No Music)。將腦波訊號依頻率不同濾波成Alpha、Beta、Theta與Gamma波,並計算各波中各導程之能量值,並依此值作為特徵求出各導程間不同音樂刺激與不同受測者間之相關係數。

結果顯示,在Metal狀態下,有最小的Alpha能量。而在No Music狀態下,Gamma能量呈現較小的情況。顯示聆聽音樂時會出現Gamma波,而聽Metal時會降低Alpha波。

而在個體間之差異情形探討上,發現聆聽Metal時,不同個體間腦波特徵相似度高,而聆聽Favorite時,相似度最小。顯示聆聽Metal可引起較為相似之腦波。此外本研究發現,前顱左半區域於三情境下(Metal、Sonata、Favorite)腦波相似度差異較大,代表此區對腦部音樂感知較為敏感,也就是不同音樂類型會引起相似度較小之腦波。

此外,本研究發覺個體間的腦波差異大於音樂所引起之腦波差異,故腦波研究上如何排除降低因個體不同所造成研究資料之差異,將可對研究目的降低變數達到更準確之分析。

本研究中,我們亦嘗試ICA分析,發覺腦波經ICA後所得之某些獨立元件在頻譜分析上,對於不同情境可顯示其頻譜能量差異,但並非每一個體實驗皆有此現象。
In recent years, many researches have focused on the physiological effects of music. The electroencephalographic (EEG) is often used to verify the influences of music on human brain activity. In this study, we attempted to apply the spectral analysis and the independent component analysis (ICA) to analyze and to discover the EEG responses of subjects with different musical signal stimuli. It is expected that some features on EEG can be demonstrated to reflect the different musical signal stimuli.

The EEGs of thirty-two healthy volunteers listening to different music was acquired. Musical signal stimuli are categorized into metal music, sonata music, no music and the favorite music selected by subjects. Spectral analysis wase applied to obtain the Alpha, Beta, Theta and Gamma band power of EEG signal under different music stimuli. The power at each band of each channel was used as the features of EEG. The correlation of the features between different situations and subjects was used to show which channel displays the difference of EEG signals.

The results show that minimum alpha power was recorded in listening to metal music and the power of gamma band is lower when listening to no music, which imply that gamma band appears during music listening process, and reduction of alpha band occurs when listening to metal music.

Regarding the difference between each individual, we found that the similarity between individuals is high when listening to metal music, and it is low when listening to favorite music. Besides, the similarity between each individual is high in the channel at the left of anterior cranial is highly different. When listening to metal music, sonata music and favorite music, which implies that this section may be sensitive to musical signal stimuli.

Besides, the study discovers that the difference between individual is greater than the difference between musical signal stimuli. So how to eliminate the difference of EEG data caused by the difference of individual is important to obtain the accurate analysis results.

In the study of independent component analysis, we discovered that some independent components of EEG can display the difference of spectral power in listening different music. But not every subject showed this phenomenon.
第一章 緒論 1
1.1. 研究動機 1
1.2. 研究目的與假設 3
第二章 文獻探討 4
2.1. 腦波 4
2.2. 腦波相關研究 5
2.2.1. 腦波與認知活動 5
2.2.2. 腦波與音樂 7
2.3. 莫札特效應 8
2.4. 腦波信號分析 10
2.4.1. 叢集分析於腦波之研究 10
2.4.2. 獨立元件分析於腦波之研究 11
2.4.3. 其他分析法於腦波之研究 11
第三章 研究材料與方法 13
3.1. 研究材料 13
3.1.1. 研究儀器 13
3.1.2. 受測者 15
3.1.3. 10-20國際標準腦波擷取法 15
3.1.4. 實驗情境音樂 17
3.1.5. ICA Tool Box for MatLab 17
3.2. 實驗流程 19
3.3. 腦波信號分析方法 21
3.3.1.頻譜分析 22
3.3.2. 獨立元件分析(Independent Component Analysis, ICA) 23
3.3.3. 相關係數 (Correlation coefficient) 24
3.3.4. 叢集分析(Cluster Analysis) 25
3.6. 資料分析與統計 25
第四章 結果 27
4.1 各種情境下之腦波與其頻譜 27
4.2 不同情境下之各導程中各頻帶能量比較 28
4.2.1 Alpha波 29
4.2.2 Beta波 30
4.2.3 Theta波 30
4.2.4 Gamma波 31
4.3 腦波相似度 31
4.3.1 不同個體在相同音樂情境下 31
4.3.2 同個體在不同音樂情境下 34
4.4 ICA分析法 36
第五章 討論與結論 42
5.1 不同情境間之腦波差異 42
5.1.1. Alpha波 42
5.1.2. Beta波 42
5.1.3. Gamma波 42
5.1.4. Theta波 43
5.2 個體差異與音樂差異 43
5.3 應用ICA於腦波訊號分析 44
參考文獻 46
附錄一、音樂與腦波研究紀錄表 51
附錄二、同情境之間各導程下各頻帶能量比較統計P-value表 52
參考文獻

中文文獻

汪彥青、林芳蘭、吳佳慧、張乃文、張初穗、蔡安悌等。《音樂治療—治療心靈的樂音》,台北:先知,民91,1月。
Bruce J, Fisch M D.《Fisch And Spehlmann’s EEG Primer》. 01 Nov, 1999 Elsevier Science.

邱安偉,《音樂對大學生腦波及心率變異性的影響》,台北醫學大學醫學研究所,碩士論文,民93,6月。

歐陽淑卿,《以音樂欣賞進行音樂治療之行動研究》,國立東華大學教育研究所,碩士論文,民92,6月。

英文文獻

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Bhattacharya J, Petsche H. Universality in the brain while listening to music. Proc R Soc Lond B Biol Sci. 2001 Dec 7;268(1484):2423-33.

Makeig S, Anllo-Vento L, Jung TP, Bell AJ, Sejnowski TJ, Hillyard SA. Independent components of the late positive event-related potential in a visual
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Ikonomidou E, Rehnstrom A, Naesh O. Effect of music on vital signs and postoperative pain. AORN J. 2004 Aug;80(2):269-74, 277-8.

Iriarte J, Urrestarazu E, Valencia M, Alegre M, Malanda A, Viteri C, Artieda J. Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study. J Clin Neurophysiol. 2003 Jul-Aug;20(4):249-57.

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Koelsch S, Mulder J Electric brain responses to inappropriate harmonies during listening to expressive music. Clin Neurophysiol, 2002 113: 862-869.

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Makeig S, Bell AJ, Jung TP, and Sejnowski TJ, Independent component analysis of
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McLachlan JC. Music and spatial task performance. Nature. 1993 Dec 9;366(6455):520.
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電子資料

The National Association for Music Therapy . Available at:
http://www.musictherapy.org/ Accessed Dec 11, 2004.

British Society for Music Therapy. Available at:
http://www.bsmt.org/ Accessed Dec 11, 2004.

The Mozart Effect. UCB Pharma. Available at:
http://www.epilepsy.org.uk/info/mozart.html. Accessed Nov 30, 2004.
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