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姓名 徐則林(Tse-Lin Hsu) 查詢紙本館藏 畢業系所 電機工程學系 論文名稱 應用深度學習於運動區腦波之手部動作預測 相關論文 檔案 [Endnote RIS 格式]
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至系統瀏覽論文 ( 永不開放)
摘要(中) 心電圖、腦電波、肌電波等是臨床上重要的診斷儀器，傳統溼式 Ag/AgCl電
極雖然在訊號接收上相當穩定，但實驗中造成使用者的不適，特別是用於降低電極與
皮膚間阻抗的電解凝膠，此介質可能對使用者的皮膚造成刺激，甚至是產生過敏反應，
故我們使用乾式電極進行實驗。由於在醫學研究中指出 EEG中特定的頻寬具有大腦活
動與動作技能表現的關聯，因此本研究希望藉由整合腦波訊號處理與慣性感測器系統，
提供足夠且標記的腦波訓練資料，發展貼近日常動作的腦波分析系統。本實驗以慣性
感測器作為標記工具，透過不同動作間的角度變化作為腦波的時間標記，目的在結合
慣性感測器與腦波機來提升腦波人機介面的準確率。我們在受試者雙手各裝置感測器，
結合腦波電極設置在 10-20 EEG System之 C3、Cz、C4、F3、F4位置的腦波，每隔 8
秒做一次手臂動作，紀錄受測者的動作姿態與腦波，透過肢體間的角度變化進行腦波
的時間標記，標記方式為抓取動作瞬間作為基準點，以此基準點向前取兩秒的資料作
為分析腦波的區間，透過小波轉換 (wavelet transform) 的方式取出此腦波區間的
(Event RelatedDesynchronization/Event RelatedSynchronization, ERD/ERS)，並將五個通
道所取出的頻率與時間關係做疊加，作為疊加後的二維時頻圖像，輸入卷積神經網路
(Convolution Neural Networks, CNN)及長短期記憶神經網路(Long Short-Term Memory,
LSTM)進行分析，達到 CNN 80% 及 LSTM 89% 準確率，並以此架構找出動作與腦波相
對應的連結。摘要(英) Electrocardiogram (ECG), electroencephalogram(EEG), electromyography(EMG) are important diagnostic instruments in clinic. Although the Ag/AgCl electrode is quite stable in signal reception, it causes user discomfortable in the experiments, especially for using Electrolyte gel to reduce the impedance between the electrode and the skin. The use of wet type electrolyte may cause stimulation to the user′s skin, and even produce allergic reaction, so we use dry electrodes for experiments. Since it is pointed out in medical research that the specific bandwidth in EEG has a correlation between brain activity and motor performance, in this study we propose to develop a brain wave analysis system close to daily movements by integrating brain wave signal processing and IMU system to provide sufficient and marked brain wave training data. In this experiment, IMU is used as the marking tool, and angles change between different actions are used as the time mark of brain wave. The purpose of this experiment is to improve the accuracy of brain computer interface (BCI) by combining IMU and brain wave system. In this research, we mounted two IMU on subject’s left and right arm. The EEG electrodes were attached on C3,Cz,C4,F3, and F4 positions, according to international 10-20 EEG system. Subjects were asked to do specified motion between 8sec, and timing of subject’s posture data was wirelessly transmitted for EEG labeling. EEG data were segmented into epochs from -2sec anchored to subject’s movement onsets. Labeled EEG data were extracted Event RelatedDesynchronization/Event RelatedSynchronization (ERD/ERS) by wavelet transform, and we combine the five channels (C3,Cz,C4,F3, and F4) time-frequency relationship as two-dimension image. Using this image as Convolution Neural Networks(CNN) and Long Short-Term Memory(LSTM) input attained CNN 80% and LSTM 89%, to exploring the connections between subject’s movement and brain wave. 關鍵字(中) ★ 腦電波
★ 腦波人機介面
★ 深度學習網路關鍵字(英) ★ Electroencephalography (EEG),
★ Brain Computer Interface (BCI)
★ Deep Learning Neural Network論文目次 目錄
中文摘要 ................................................................................................................................ i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 ....................................................................................................................... 1
1-1 研究動機與目的 .................................................................................................... 1
1-2 文獻探討 ............................................................................................................... 2
1-3 論文章節架構 ........................................................................................................ 3
第二章 原理介紹 ............................................................................................................... 4
2-1 腦電訊號 ............................................................................................................... 4
2-1-1 腦電波類別 ........................................................................................................ 4
2-1-2 量測位置與方法 ................................................................................................ 6
2-1-3 大腦與運動系統 ................................................................................................ 8
2-1-3-1 高級階層 .............................................................................................................................. 10 2-1-3-2 中級階層 .............................................................................................................................. 10 2-1-3-3 局部階層 .............................................................................................................................. 10 2-2 事件相關非同步與同步腦波律動 ....................................................................... 11
2-3 腦波誘發電位 ...................................................................................................... 12
2-3-1 視覺誘發 .......................................................................................................... 12
2-4 腦機介面 ............................................................................................................. 14
2-5 小波轉換 ............................................................................................................. 15
2-6 機器學習 ............................................................................................................. 17
v
2-6-1 類神經網路 ...................................................................................................... 17
2-6-2 激勵函數 .......................................................................................................... 18
2-6-3 梯度下降法 ...................................................................................................... 20
2-6-4 卷積神經網路 .................................................................................................. 21
第三章 研究設計與方法 .................................................................................................. 24
3-1 系統架構 ............................................................................................................. 24
3-1-1 慣性感測器硬體架構 ...................................................................................... 25
3-1-2 神經網路架構 .................................................................................................. 28
3-1-3 支援向量機 ...................................................................................................... 30
3-2 實驗設計 ............................................................................................................. 31
3-2-1 實驗對象 .......................................................................................................... 31
3-2-2 實驗設計流程 .................................................................................................. 31
3-3 慣性感測器與腦波機的結合 ............................................................................... 37
3-4 小波轉換後時頻圖 .............................................................................................. 39
第四章 結果與討論 .......................................................................................................... 42
第五章 結論與未來展望 .................................................................................................. 53
第六章 參考文獻 ............................................................................................................. 54參考文獻 [1] Graimann B, Allison B and Pfurtscheller G, “Brain-Computer Interfaces: A Gentle Introduction”, Berlin Springer, 2010 [2] Nicolas-Alonso L F and Gomez-Gil J , “Brain computerinterfaces”, a review Sensors, 2012 [3] Pfurtscheller G and Da Silva F L, Event-related EEG/MEG synchronization and desynchronization: basic principles Clin., Neurophysiol., 1999. [4] Müller-Gerking J, Pfurtscheller G and Flyvbjerg H, Designing optimal spatial filters for single-trial EEG classification in a movement task Clin. Neurophysiol., 1999 [5] Grosse-Wentrup M and Buss M, Multiclass common spatial patterns and information theoretic feature extraction IEEE Trans. Biomed. Eng., 2008 [6] Jolliffe I ,Principal Component Analysis (New York: Wiley) (doi: 10.1002/9781118445112.stat06472), 2002 [7] Ang K K, Chin Z Y, Wang C, Guan C and Zhang H, Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b Front. Neurosci., 2012 [8] Kübler A, Furdea A, Halder S, Hammer E M, Nijboer F and Kotchoubey B, A brain-computer interface controlled auditory event-related potential (P300) spelling system for locked-in patients Ann. N. Y. Acad. Sci.,2009 [9] Fukunaga K, Introduction to Statistical Pattern Recognition (New York: Academic), 2013 [10] Jensen F V, Bayesian Networks and Decision Graphs (New York: Springer) p 34, 2001 [11] Schlögl A, Lee F, Bischof H and Pfurtscheller G, Characterization of four-class motor imagery EEG data for the BCI-competition J. Neural Eng., 2005 [12] An X, Kuang D, Guo X, et al. A deep learning method for classification of EEG data based on motor imagery. In: IntelligentComputing in Bioinformatics. Springer, Heidelberg, 2014,203–10. [13] R.K. Maddula, J. Stivers, M. Mousavi, S. Ravindran, & V.R. de Sa,“Deep recurrent convolutional neural networks for classifying P300 BCI signals.” In Proceedings of the Graz BCI Conference 2017. [14] P. Wang, et al., “LSTM-based EEG classification in motor imagerytasks,” IEEE Transactions on Neural Systems and RehabilitationEngineering, vol. 26, no. 11, pp. 2086–2095, 2018. [15] Y. R. Tabar and U. Halici, “A novel deep learning approach forclassification of EEG motor imagery signals,” Journal ofNeural Engineering, vol. 14, no. 1, article 016003, 2017. [16] Sauseng P., Hoppe J., Klimesch W., Gerloff C. and Hummel F. , “Dissociation ofsustained attention from central executive functions: Local activity and interregionalconnectivity in the theta range”, European Journal of Neuroscience (25), 2007, pp.587-593. [17] Malmivuo,J., Plonsey, R., “Bioelectromagnetism “, Oxford University Press, New York, January 1995. [18] Herbert H. Jasper, “The ten-twenty electrode system of the International Federation”, Electroencephalography and clinical neurophysiology,1958
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[19] Valer Jurcak, et al., “10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems”, Neuro Image, 2007 [20] Penfield W. and Rasmussen T., The cerebral cortex of man: A clinical study of localization of function, Macmillan, New York, 1990. [21] https://www.ebmconsult.com/articles/homunculus-sensory-motor-cortex [22] https://en.wikipedia.org/wiki/Cortical_homunculus [23] Heinrich Reichert, Neurobiologie, 2/e, Thieme, 2004. [24] Pfurtscheller G and Da Silva F L, Event-related EEG/MEG synchronization and desynchronization: basic principles Clin., Neurophysiol., 1999. [25] Karl E. Misuls, Toufic Fakhoury, Spehlmann’s Evoked Potential Primer, 3Ed, Butterworth-Heinemann, May 2001. [26] Keith H. Chiappa, Evoked potentials in Clinical Medicine, 3Ed, LippincottRaven, Philadelphia, USA, 1997. [27] Korbinian Brodmann, Vergleichende Lokalisationslehre der Grosshirnrinde, Leipzig Berlin, Germany, August 1909. [28] Erich E. Sutter, “The brain response interface: communication through visuallyinduced electrical brain responses”, Journal of Microcomputer Applications, Vol. 15, pp.31-45,1992. [29] S. Rinalduzzi, et al.,“Variation of visual evoked potential delay to stimulation of central, nasal, and temporal regions of the macula in optic neuritis”, Journal Neurol Neurosurgery & Psychiatry., January 2001. [30] Russell A. Dewey: The Homunculus, http://www.intropsych.com. [31] Robi Polikar : Wavlet Tutorial, http://users.rowan.edu/~polikar/WTtutorial.html [32] Understanding Activation Functions in Neural Networks, https://medium.com/the-theory-of-everything/understanding-activationfunctions-in-neural-networks-9491262884e. [33] Yann Lecun , Léon Bottou , Yoshua Bengio , Patrick Haffner“ Gradient-Based Learning Applied to Document Recognition”, Proc. IEEE, 1998. [34] https://goo.gl/zgr9fj, 資料分析-機器學習 [35] https://www.quora.com/What-is-max-pooling-in-convolutional-neural-networks, Max Pooling [36] MPU9250 Product Specification [37] https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8%E5% AD%B8%E7%BF%92%E6%94%AF%E6%92%90%E5%90%91%E9%87%8F%E6%A9%9F-supportvector-machine-svm%E8%A9%B3%E7%B4%B0%E6%8E%A8%E5%B0%8E-c320098a3d2eㄝ, 機器學習-支撐向量機 [38] https://en.wikipedia.org/wiki/10-20_system_(EEG) [39] Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., &Salakhutdinov, R. R. , ” Improving neural networks bypreventing co-adaptation of feature detectors.” arXiv preprintarXiv:1207.0580., 2012.指導教授 李柏磊 審核日期 2019-10-15 推文 plurk
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