不同背景音影響下之腦波分析

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姓名

梁昭敏(Chao-Min Liang) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

不同背景音影響下之腦波分析

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至系統瀏覽論文 ( 永不開放)

摘要(中)

本研究探討在不同聲音情境下專注與安靜放鬆之腦波狀態。實驗使用腦波儀測量人體頭部表面電訊號，分析受試者於安靜、管弦樂、噪音和混合音等聲音情境下之腦波訊號，以作為後續專注度分析數據。腦波經由訊號處理來濾除雜訊，以減少雜訊干擾，降低分析上的錯誤。接著使用碎形維度(fractal dimension)、近似熵(approximate entropy)、與經過希爾伯特-黃轉換(Hilbert-Huang transform)後所得的alpha與beta頻段功率作為分類的特徵指標，並於後續利用接收者操作特徵曲線(receiver operating characteristic curve)與支持向量機(support vector machine)兩種分析工具進行腦波情境分類，判別受試者在各種情境間的腦波差異狀況及不同情境對專注力影響。

摘要(英)

In this study, we discussed the brain wave modes of concentration and relaxation under various sound conditions. Twenty-four participants were recruited to play the attention game in the quiet, orchestra, noise and mixed sound states through human factors experiments. The brain waves were filtered out by signal processing to reduce noise interference and analytical errors. Then, we used the fractal dimension, the approximate entropy and the alpha and beta band power as features in the brainwave analysis. Furthermore, the brainwave classification was performed by using two analysis tools, including receiver operating characteristic curve (ROC curve) and support vector machine (SVM), to identify the difference in brainwave status between different contexts and the effect of different contexts on concentration.

關鍵字(中)

★ 腦電圖
★ 碎形維度
★ 希爾伯特-黃轉換
★ 支持向量機
★ 專注力

關鍵字(英)

★ EEG
★ fractal dimension
★ Hilbert-Huang transform
★ support vector machine
★ concentration

論文目次

摘要 I
ABSTRACT II
致謝 III
目錄 IV
圖目錄 VII
表目錄 IX
第一章緒論 1
1-1 研究動機 1
1-2 研究目的 1
1-3 論文架構 2
第二章文獻回顧 3
2-1 腦電波 3
2-2 腦電圖量測與分析 6
2-3腦波注意力及專注力判別 9
2-4 音樂對大腦之影響 10
第三章實驗架構與分析方法 11
3-1 實驗設計 11
3-2 實驗環境與設備 11
3-2-1 實驗環境 11
3-2-2 腦波儀及音響設備 12
3-3 實驗流程 14
3-4 專注力遊戲 16
3-5實驗數據分析方法 17
3-5-1 眨眼訊號檢測 17
3-5-2 碎形維度 (Fractal Dimension, FD) 19
3-5-3 近似熵 (Approximate Entropy, ApEn) 22
3-5-4 希爾伯特-黃轉換法 (Hilbert-Huang Transform, HHT) 23
3-5-4-1經驗模態分解法(Empirical Mode Decomposition,EMD) 23
3-5-4-2希爾伯特轉換 (Hilbert Transform, HT) 27
3-5-5接受者操作特徵曲線 (Receiver Operating Characteristic Curve, ROC curve) 29
3-5-6 支持向量機 (Support Vector Machine, SVM) 32
3-5-7 交叉驗證 (Cross Validation) 36
第四章實驗數據分析流程 37
4-1 雜訊處理 37
4-2 特徵萃取 40
4-3 數據分析 41
第五章實驗結果與分析 44
5-1 ROC曲線分析 44
5-2 單特徵SVM 55
5-3 多特徵SVM 57
5-4 不同音樂對專注之影響性 58
5-5 實驗遊戲結果及主觀問卷分析 59
第六章結論與未來展望 61
參考文獻 63

參考文獻

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指導教授

陳世叡(Shih-Jui Chen)

審核日期

2023-7-27

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