基於腦波分析之不同環境音下專注力識別

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：79

、訪客IP：3.17.75.227

姓名

符祐嘉(Yu-Chia Fu) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

基於腦波分析之不同環境音下專注力識別
(Concentration Recognition under Different Ambient Sounds Based on EEG Analysis)

相關論文

★ 以GATE模型及系統矩陣演算法重建SPECT螺旋影像	★ LED檯燈視覺舒適度研究
★ 表面電漿共振系統之相位擷取與分析	★ 人眼眼球模型與視覺表現之模擬分析研究
★ 白光LED之視覺生理效應評估	★ 不同色溫螢光燈用於辦公室照明之視覺效應研究
★ 表面電漿共振儀之動態相位偵測技術與微量生物分子檢測應用	★ 二次通過成像架構量測人眼的光學系統品質
★ 週期性奈米金屬結構對拉曼散射訊號增強之研究	★ 日眩光要因分析研究
★ 非球面檢測之迭代相移干涉與子孔徑相位接合演算法開發	★ 應用可容忍隨機位移之相移干涉術於相位式表面電漿共振系統之穩定度增進
★ 以偵測任務及系統效能評估找尋多針孔微單光子放射電腦斷層掃描系統之最佳化配置	★ 結合表面電漿共振及溫度控制於免疫球蛋白鍵結之檢測分析
★ 以二次通過成像量測架構及降低誤差迭代演算法重建人眼之點擴散函數	★ 多陽極光電倍增管閃爍相機之訊號讀出系統與高效最大可能性位置估算演算法開發

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2027-10-1以後開放)

摘要(中)

本研究透過人因實驗，模擬室內辦公場所，提取在不同環境音下使用者專注與放鬆狀態之腦波，經訊號處理及特徵萃取流程，將受試者不同情境的腦波數據使用多種分析工具進行腦波狀態辨識及評量，進一步探討不同聲音環境對使用者專注程度之影響，期許能幫助使用者注意自身專注力問題進而提升在工作、學習上的表現。
在環境噪音之專注度影響實驗中，受試者將會體驗兩輪的專注度遊戲測驗，每一輪對應的背景噪音與遊戲測驗內容不同，在接收實驗人員指示後，受試者應在不同聲音情境下進行專注度遊戲測驗或放鬆休息之動作，實驗過程中使用Neurosky耳機型腦波儀紀錄受試者即時的腦波訊號，作為專注度評估之數據，且受試者在專注度測驗中的得分表現、反應速度也會作為後續客觀分析的參考資料。
收集完受試者各情境間不同狀態之腦波數據，透過訊號處理的方式將雜訊干擾濾除，以減少雜訊干擾導致後續分析上的誤判，接著提取碎形維度(Fractal Dimension)、希爾伯特–黃轉換(Hilbert-Huang Transform, HHT)後得到的特定頻段之頻帶功率作為腦波分析的特徵指標，並利用接收者操作特徵曲線(Receiver operating characteristic curve, ROC Curve)、支持向量機(Support Vector Machine, SVM)及長短期記憶(Long Short-term Memory, LSTM)三種分析工具進行腦波狀態分類。
研究結果顯示去除雜訊對於使用碎形維度作為分析指標進行腦波狀態分類的效能有顯著的提升，特徵指標DSA、DSA3-100則在雜訊處理後分類效能微幅降低且大多無顯著差異。分類器的選擇上，使用ROC曲線之最佳閾值所決定的線性二元分類器需要的運算時間最短，其分類效能也為三者之中最低的；LSTM與SVM間的比較，在分類正確率上，LSTM較SVM更勝一籌，運算時間也較為省時。在專注度測驗表現的統計結果中發現，受試者不會受到聲音情境的影響，而是因練習效應而產生表現之差異。

摘要(英)

In this study, we perform human factors experiments in a simulated office to extract the brainwaves of participants in their focused and relaxed states under different ambient sounds. Through signal processing and feature extraction methods, we evaluate and classify the brainwaves of participants in different situations by using a variety of analysis tools. By exploring the influence of different sound environments on users’ concentration, we hope to assist users in considering their own concentration status and improving their performance in work and study.
In the experiment of “the effect of ambient noise on concentration’’, participants will take two rounds of concentration games, and the tested sound and the content of the game corresponding to each round are different. After receiving the instructions from the experimenter, the participant will take a concentration game or relax in different sound situations. During the experiment, we use a Neurosky MindWave Mobile 2 to record the real-time brainwave data of participants for the evaluation of concentration status. The game scores and reaction time in the concentration game will also be used as reference materials for objective analysis.
The collected brainwave data are preprocessed to filter out the noise and interference, so as to reduce the misjudgment caused by the noise and interference in the succeeding analysis. Then, we extract the fractal dimension of the brainwave data and use the Hilbert-Huang transform (HHT) to extract the band powers of specific frequency bands as the features in the brainwave analysis. Furthermore, we use three analysis tools to classify the brainwaves in working and relaxing states, including receiver operating characteristic curve (ROC curve), support vector machine (SVM) and long short-term memory (LSTM).
The results show that the classification performance of brainwave states using fractal dimension is significantly improved after noise filtering. However, the alpha-band related indices “DSA” and “DSA3-100” show a slight decrease in classification accuracy after noise filtering, but no significant difference before and after the noise filtering in most cases. Among the three classifiers, the operation time for the linear binary classifier determined by the ROC curve is the shortest, but its classification accuracy is the lowest. The comparison between LSTM and SVM shows that LSTM is better than SVM in terms of classification accuracy, and the computation of LSTM is also more time-saving. From the scores and reaction time in the concentration games, the performance of the participants are not affected by the sound situations but by the practice effect.

關鍵字(中)

★ 腦電圖
★ 碎形維度
★ 希爾伯特-黃轉換
★ 接收者操作特徵曲線
★ 支持向量機
★ 長短期記憶

關鍵字(英)

★ EEG
★ Fractal Dimension
★ Hilbert-Huang Transform
★ Receiver Operating Characteristic Curve
★ Support Vector Machine
★ Long Short-term Memory

論文目次

摘要 I
Abstract III
致謝 V
目錄 VI
圖目錄 VIII
表目錄 X
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 論文架構 2
第二章文獻探討 4
2-1 環境噪音及專注度 4
2-2 生理回饋與腦波 5
2-2-1 腦電圖 7
2-2-2 腦電位量測 9
2-2-3 腦波訊號分析 13
第三章實驗架構與分析方法 14
3-1 實驗設計 14
3-2 實驗相關設備與環境配置 16
3-2-1 音響設備 16
3-2-2 腦波儀 17
3-2-3 實驗環境配置 18
3-3 實驗流程與內容 19
3-3-1 實驗流程 19
3-3-2 專注度遊戲試驗 20
3-4 實驗數據處理方法 22
3-4-1 眨眼訊號檢測方法 22
3-4-2 碎形維度(Fractal Dimension, FD) 26
3-4-3 希爾伯特–黃轉換(Hilbert-Huang Transform, HHT) 29
3-4-4 經驗模態分解法(Empirical Mode Decomposition, EMD) 29
3-4-5 希爾伯特轉換(Hilbert Transform, HT) 32
3-4-6 頻帶功率(Band Power) 35
3-5 實驗數據分析方法 36
3-5-1 接收者操作特徵曲線(Receiver operating characteristic curve) 36
3-5-2 支持向量機(Support Vector Machine, SVM) 40
3-5-3 交叉驗證(Cross Validation) 44
3-5-4 人工神經網路(Artificial Neural Network, ANN) 45
3-5-5 長短期記憶(Long Short-term Memory, LSTM) 48
第四章實驗數據分析流程與結果 51
4-1 數據分析流程 51
4-1-1 雜訊處理 51
4-1-2 特徵萃取 55
4-1-3 數據分析 57
4-2 數據分析結果 63
4-2-1 遊戲一之實驗結果 63
4-2-2 遊戲二之實驗結果 88
第五章實驗結果討論 97
5-1 去除雜訊對腦波分析之影響 97
5-2 不同特徵指標與不同分類器之比較 102
5-3 遊戲一與遊戲二之結果比較 108
5-4 專注度遊戲測驗結果 118
第六章結論與未來展望 127
參考文獻 129
附錄一臺灣大學研究倫理審查核可證明 133

參考文獻

[1] H. J. Jerison. “Effects of noise on human performance,” Journal of Applied Psychology, 43(2), 96–101 (1959).
[2] L. Cheng, S. Wang, Q. Chen, X. Liao. “Moderate noise induced cognition impairment of mice and its underlying mechanisms,” Physiology & Behavior, 104(5), 981-988 (2011).
[3] C. Clark, J. Head, S. A. Stansfeld. “Longitudinal effects of aircraft noise exposure on children′s health and cognition: A six-year follow-up of the UK RANCH cohort,” Journal of Environmental Psychology, 35, 1-9 (2013).
[4] J. J. O′Malley, A. Poplawsky. “Noise-Induced Arousal and Breadth of Attention,” Perceptual and Motor Skills, 33(3), 887-890 (1971).
[5] G. Toplyn, W. Maguire. “The differential effect of noise on creative task performance,” Creativity Research Journal, 4(4), 337-347 (1991).
[6] S. Son, S. Kwag. “Effects of white noise in walking on walking time, state anxiety, and fear of falling among the elderly with mild dementia,” Brain Behav, 10(12) (2020).
[7] G. Söderlund, S. Sikström, A. Smart. “Listen to the noise: noise is beneficial for cognitive performance in ADHD,” J, Child Psychol Psychiatry, 48(8), 840-847 (2007).
[8] 蔡昀安，「不同環境音下之腦電波特徵分析」，國立中央大學碩士論文，民國110年。
[9] S. Cohen, G. W. Evans, D. S. Krantz, D. Stokols. “Physiological, motivational, and cognitive effects of aircraft noise on children: moving from the laboratory to the field,” The American Psychologist, 35(3), 231-243 (1980).
[10] V. H. Rausch, E. M. Bauch, N. Bunzeck. “White noise improves learning by modulating activity in dopaminergic midbrain regions and right superior temporal sulcus,” J Cogn Neurosci, 26(7), 1469-1480 (2013).
[11] E. Ratcliffe, B. Gatersleben, P. T. Sowden. “Bird sounds and their contributions to perceived attention restoration and stress recovery,” Journal of Environmental Psychology, 36, 221-228 (2013).
[12] E. R. Kandel et al. Principles of Neural Science, Fifth Edition. (McGraw-Hill Education, 2012).
[13] 網路資料，取自: https://pansci.asia/archives/346890 , accessed on July 2022.
[14] L. F. Haas. “Hans Berger (1873–1941), Richard Caton (1842–1926), and electroencephalography,” Journal of Neurology, Neurosurgery & Psychiatry, 74(1), 9 (2003).
[15] Human EEG with prominent alpha-rhythm. Source: https://commons.wikimedia.org/wiki/File:Human_EEG_with_prominent_alpha-rhythm.png , accessed on July 2022.
[16] H. Marzbani, H. R. Marateb, M. Mansourian. “Neurofeedback: A comprehensive review on system design, methodology and clinical applications,” Basic Clin Neurosci, 7(2), 143-158 (2016).
[17] 柯景瀚，「不同聲音或照明情境下室內靜態工作之專注程度指標開發」，國立中央大學碩士論文，民國111年。
[18] International 10-20 system for EEG. Source: https://reurl.cc/lVxMOA , accessed on July 2022.
[19] The International 10-20 System seen from ( A ) left and ( B ) above the head. Source: https://www.researchgate.net/figure/The-International-10-20-System-seen-from-A-left-and-B-above-the-head-A-ear_fig1_257625325 , accessed on July 2022.
[20] Neurosky MindWave Mobile. Source: https://store.neurosky.com , accessed on July 2022.
[21] M. Iwasaki et al. “Effects of eyelid closure, blinks, and eye movements on the electroencephalogram,” Clinical Neurophysiology, 116(4), 878-885 (2005).
[22] M. A. Sovierzoski, F. I. M. Argoud, F. M. de Azevedo. “Identifying eye blinks in EEG signal analysis,” 2008 IEEE International Conference on Information Technology and Applications in Biomedicine.
[23] M. Jurczak, M. Kołodziej, A. Majkowski. “Implementation of a convolutional neural network for eye blink artifacts removal from the electroencephalography signal,” Front Neurosci, 11(16), 782367 (2022).
[24] M. Agarwal, R. Sivakumar. “Blink: A fully automated unsupervised algorithm for eye-blink detection in EEG signals,” 2019 IEEE 57th Annual Allerton Conference on Communication, Control, and Computing.
[25] B. B. Mandelbrot. The Fractal Geometry of Nature. Times Books; 2nd prt. Edition (1982).
[26] T. Higuchi. “Approach to an irregular time series on the basis of the fractal theory,” Physica D: Nonlinear Phenomena, 31(2), 277-283 (1988).
[27] C. Gómez, Á. Mediavilla, R. Hornero, D. Abásolo, A. Fernández. “Use of the Higuchi′s fractal dimension for the analysis of MEG recordings from Alzheimer′s disease patients,” Medical Engineering & Physics, 31(3), 306-313 (2009).
[28] C. Loo, A. Samraj, G. Lee. “Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface,” Procedia Computer Science, 3, 589-594 (2011).
[29] N. E. Huang et al. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995 (1998).
[30] Time Series Analysis, Source: https://www.analyticsvidhya.com/blog/2021/07/time-series-analysis-a-beginner-friendly-guide/ , accessed on July 2022.
[31] 陳佑榮，「應用希爾伯特黃轉換以C語言環境開發腦機介面訊號處理」，國立中央大學碩士論文，民國104年。
[32] J. A. Hanley, B. J. McNeil. “The meaning and use of the area under a receiver operating characteristic (ROC) curve,” Radiology, 143(1), 29-36 (1982).
[33] V. Jakkula. “Tutorial on Support Vector Machine (SVM),” School of EECS, Washington State University (2006).
[34] N. Cristianini, J. Shawe-Taylor. “An introduction to support vector machines and other kernel-based learning methods,” Cambridge University Press (2000).
[35] F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, B. Arnaldi. “A review of classification algorithms for EEG-based brain-computer interfaces,” Journal of Neural Engineering, 4(2), 1-13 (2007).
[36] C. Chang, C. Lin. “LIBSVM: A library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27 (2011).
[37] G. Kimeldorf, G. Wahba. “Some results on Tchebycheffian spline functions,” Journal of Mathematical Analysis and Applications, 33(1), 82-95 (1971).
[38] M. Stone. “Cross-validatory choice and assessment of statistical predictions,” Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111-133 (1974).
[39] W. S. McCulloch, W. Pitts. “A logical calculus of the ideas immanent in nervous activity,” The bulletin of mathematical biophysics, 5, 115–133 (1943).
[40] Diagram of a single-layer feedforward artificial neural network. Source: https://zh.wikipedia.org/zh-tw/%E4%BA%BA%E5%B7%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C , accessed on July 2022.
[41] 網路資料，取自：https://analyticsindiamag.com/artificial-neural-networks-101/ , accessed on July 2022.
[42] J. Hertz, A. Krogh, R. G. Palmer. Introduction to the Theory of Neural Computation. (CRC Press, 2019).
[43] J. Schmidhuber. “Deep learning in neural networks: An overview,” Neural Networks, 61, 85-117 (2015).
[44] 網路資料，取自：http://moonmoonbirdchen.blogspot.com/2017/04/deep-learning-neural-network.html , accessed on July 2022.
[45] H. T. Siegelmann. Theoretical Foundations of Recurrent Neural Networks. (Rutgers University, 1993).
[46] F. A. Gers, J. Schmidhuber, F. Cummins. “Learning to forget: continual prediction with LSTM,” 1999 IEEE Ninth International Conference on Artificial Neural Networks ICANN 99.
[47] S. Hochreiter, J. Schmidhuber. “Long short-term memory,” Neural Computation, 9(8), 1735-1780 (1997).
[48] 網路資料，取自：https://en.wikipedia.org/wiki/Long_short-term_memory , accessed on July 2022.
[49] 網路資料，取自：https://colah.github.io/posts/2015-08-Understanding-LSTMs/ , accessed on July 2022.
[50] D. P. Kingma, L. J. Ba. “Adam: A Method for Stochastic Optimization,” 2015 International Conference on Learning Representations (ICLR).
[51] M. Abo-Zahhad, S. M. Ahmed, S. N. Abbas. “A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals,” International Journal of Intelligent Systems and Applications, 7(6), 48-54 (2015).

指導教授

陳怡君(Yi-Chun Chen)

審核日期

2022-10-21

推文