結合慣性感測器姿態偵測與運動區腦波之腦波人機介面

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姓名

王昱凱(Yu-Kai Wang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

結合慣性感測器姿態偵測與運動區腦波之腦波人機介面

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摘要(中)

自從大腦腦波訊號擷取的技術進步，科學家透過腦波訊號的擷取分析，協助神經或肌肉損傷的病患，運用腦波與環境溝通，讓腦機介面成為十分受到矚目的研究。而如何使系統能夠正確地判斷受測者所面臨或是正在執行的狀態，進而解讀腦波所隱藏的訊息。然而，腦波研究最關鍵的議題是如何獲得足夠的資料來對腦波訊號進行分類訓練，而正確的標記則是非常重要的關鍵議題。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，來提供足夠的腦波訓練資料，發展更貼近日常動作的腦波分析系統。本研究以慣性感測器做為工具，用於提供不同運動時腦波辨別的時間標記，目的在於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2個慣性感測，結合黏貼於C1、C2、C3、C4及前額位置的腦波，每隔5秒做一次手臂伸展動作，記錄受測者的動作姿態與腦波，透過肢體間的角度變化，來對腦波進行標記，標記方式為抓取準備要運動時的那一瞬間作為時間基準點，抓取的腦波資訊以此基準點向前取兩秒到向後四秒間的資料作為分析腦波的區間，我們只擷取動作時的腦波訊號做分析。透過與慣性感測器的結合測量腦波資訊，經過卷積神經網路(CNN)來取代以往傳統量測運動時腦波的方式。藉由慣性感測器的使用及CNN網路架構，找出動作與腦波相對應的連結。

摘要(英)

Owing to the rapid developments in brain wave acquisition technologies, brain computer interface (BCI) has drawn great attention in recent years. Scientists are trying to develop BCI technologies for neuromuscular paralyzed patients to communicate with external environments through their intentions. Nevertheless, the key issue of achieving correct intention detection is the requisite of abundant brain wave data for classifier training. Especially, precise labeling of brain wave data in different conditions is important. Therefore, this study intended to combine EEG data with subject’s limb postures, in order to obtain training data for BCI from subject’s daily life movement recordings. In this research, we mounted two inertial movement units (IMU) on subject’s left and right wrists. The EEG electrodes were attached on C1, C2, C3, and C4 positions, according to international 10-20 EEG system. Subjects were asked to extend and flex their left and right elbows individually, and timing of subject’s posture data was wirelessly transmitted for EEG labeling. EEG data were segmented into epochs from -4s to 2s anchored to subject’s movement onsets. Labeled EEG data were used to train convolutional neural network for BCI detections. Our system has achieved acceptable detection rates by exploring the connections between subject’s movements and EEG changes.

關鍵字(中)

★ 慣性感測器
★ 運動時腦波律動
★ 腦波人機介面

關鍵字(英)

★ Inertial movement unit
★ sensorimotor Mu rhythm
★ brain computer interface

論文目次

中文摘要 ii
Abstract iii
致謝 iv
目錄 v
圖目錄 vii
表目錄 ix
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 慣性感測元件 4
2-1-1 加速度計 4
2-1-2 磁力計 5
2-1-3 陀螺儀 6
2-2 姿態表示法 7
2-2-1 方向餘旋矩陣 7
2-2-2 歐拉角 9
2-2-3 四元數 12
2-3 大腦結構與功能 14
2-3-1 大腦與運動系統 15
2-3-1-1 高級階層 17
2-3-1-2 中級階層 17
2-3-1-3 局部階層 18
2-3-2 腦電波 19
2-3-3 事件相關非同步與同步腦波律動 20
2-4 神經網路 21
2-4-1 類神經網路 21
2-4-2 卷積神經網路 22
第三章系統架構建立 25
3-1 系統架構 25
3-2 慣性感測器硬體架構 26
3-3 感測器姿態融合演算法 30
3-4 CNN網路架構 34
第四章實驗建構與結果 35
4-1 實驗設計 35
4-2 慣性感測器捕捉人體姿態 36
4-3 慣性感測器與腦波機結合 39
4-4 腦波小波轉換後時頻圖 40
4-5 CNN網路分類 41
4-6 結果與討論 42
第五章結論與未來展望 44
第六章參考文獻 45

參考文獻

〔1〕 J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson, and T. M. Vaughan, "Brain-computer interface technology: a review of the first international meeting," IEEE Trans Rehabil Eng, vol. 8, pp. 164-73., 2000.
〔2〕 G. Uswatte, W. H. R. Miltner, B. Foo, M. Varma, S. Moran, and E. Taub, “Objective Measurement of Functional Upper-Extremit y Movement Using Accelerometer Recordings Transformed With a Threshold Filter,” Stroke, vol. 31, no. 3, pp. 662–667, 2000.
〔3〕 H. Zheng, N.D. Black, and N.D. Harris, “Position-Sensing Technologies for Movement Analysis in Stroke Rehabilitation,” Med. Biol. Eng. Comput, vol. 43, pp. 413-420, 2005.
〔4〕 Ming-Chun Huang, Si-Huei Lee and Ching Yeh, “Intelligent Frozen Shoulder Rehabilitation,”, IEEE INTELLIGENT SYSTEMS, 2014 IEEE Computer Society. 1541-1672, 2014.
〔5〕 J. Kim, S. Yang, and M. Gerla, “StrokeTrack: Wireless Inertial Motion Tracking of Human Arms for Stroke Telerehabilitation ,” mHealthSys ′11, no. 4, 2011.
〔6〕 Futoshi Kobayashi, Keiichi Kitabayashi, Hiroyuki Nakamoto, and Fumio Kojima, "Hand/Arm Robot Teleoperation by Inertial Motion Capture," in Robot, Vision and Signal Processing, 2013
〔7〕 Pfurtscheller, G., Lopes da Silva, F.H. “Event-related EEG/MEG synchronization and desynchronization: basic principles.”, Clin Neurophysiol. 1999;110:1842–1857.
〔8〕 Nick S. Ward, Jennifer M. Newton, Orlando B. C. Swayne, Lucy Lee, Richard S. J.Frackowiak, Alan J.Thompson, Richard J. Greenwood and John C. Rothwell, “The relationship between brain activity and peak grip force is modulated by corticospinal system integrity after subcortical stroke”, European Journal of Neuroscience, Vol. 25, pp. 1865–1873, 2007 .
〔9〕 J. R. Wolpaw and D. J. McFarland, "Multichannel EEG-based brain-computer communication," Electroencephalogr Clin Neurophysiol, vol. 90, pp. 444-9., 1994.
〔10〕 http://blog.js00000.me/archives/tag/ 淺談IMU
〔11〕 http://hyperphysics.phy-astr.gsu.edu/hbase/magnetic/Hall.html Hall effect
〔12〕 G. Ensell , M Kraft and N.White , Mems Mechanical Sensors.Artech House,2004
〔13〕 https://en.wikipedia.org/wiki/Aircraft_principal_axes Aircraft principal axes
〔14〕 Starlino, "DCM TUTORIAL – AN INTRODUCTION TO ORIENTATION KINEMATICS (REV 0.1)," 2011.
〔15〕 https://en.wikipedia.org/wiki/Euler_angles#Tait%E2%80%93Bryan_angles
〔16〕 http://www.wikiwand.com/en/Davenport_chained_rotations
〔17〕 http://library.taiwanschoolnet.org/cyberfair2006/penguin/4_1.htm 小腦功能
〔18〕 Robert Carola, John P. Harley, Charles R. Noback, Human Anatomy, McGraw-Hill Education, 1991.
〔19〕 R.R.Seeley, P.Tate, and T.D.Stephens, Essentials of Anatomy and Physiology, 6/e, Boston: McGraw-Hill, 2007.
〔20〕 http://blog.xuite.net/kc6191/study/34894869-%E4%BA%BA%E8%85%A6%E6%A7%8B%E9%80%A0%E5%92%8C%E5%8A%9F%E8%83%BD%E6%A6%82%E8%BF%B0%EF%BC%8C%E4%BB%A5%E5%8F%8A%E9%96%B1%E8%AE%80. 人腦構造和功能概述
〔21〕 http://slidesplayer.com/slide/11674956/ The Nervous System
〔22〕 https://zh.wikipedia.org/wiki/%E5%B8%83%E7%BD%97%E5%BE%B7%E6%9B%BC%E5%88%86%E5%8C%BA%E7%B3%BB%E7%BB%9F. 皮質小人
〔23〕 Penfield W. and Rasmussen T., The cerebral cortex of man: A clinical study of localization of function, Macmillan, New York, 1990.
〔24〕 Russell A. Dewey: The Homunculus, http://www.intropsych.com.
〔25〕 Heinrich Reichert, Neurobiologie, 2/e, Thieme, 2004
〔26〕 Eric Widmaier , Hershel Raff , and Kevin Strang, Human Physiology: The Mechanisms of Body Function, 11e,McGraw-Hill,Inc.,2008
〔27〕 Bear M F,Connors B W,Paradiso M A,Neuroscience : Exploring the Brain .,3rd Ed.,Lippincott Williams&Wilkins,Baltimore,USA,2007
〔28〕 https://zh.wikipedia.org/wiki/%E4%BA%BA%E5%B7%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C 人工神經網路
〔29〕 https://www.zixundingzhi.com/all/b9da37133af610e4.html. 深度學習與多層感知器
〔30〕 https://dotblogs.com.tw/greengem/2017/12/17/094150. 卷積神經網路
〔31〕 https://goo.gl/MDJbKt 資料分析&機器學習
〔32〕 https://chtseng.wordpress.com/2017/09/12/%E5%88%9D%E6%8E%A2%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF/ 初探卷積神經網路
〔33〕 MPU-9250 Product Specification
〔34〕 Madgwick, S.O.; Harrison, A.J.; Vaidyanathan, R. Estimation of IMU and MARG orientation using a gradient descent algorithm. Proceedings of 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), Zurich, Switzerland, 29 June–1 July 2011.
〔35〕 D. R. P. R. B. M. Joseph M. Cooke, Michael J. Zyda, “Npsnet: Flight simulation dynamic modelling using quaternions,” Presence, vol. 1, pp. 404–420, 1994.
〔36〕 Malmivuo, J., Plonsey, R., 1995. Bioelectromagnetism-Principles and Applications of Bioelectric and Biomagnetic Fields, New York OXFORD UNIVERSITY PRESS..

指導教授

李柏磊

審核日期

2018-8-21

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