• 沒有找到結果。

第五章 結論與未來展望

5.2 未來展望

一、將神念科技開發的 MindWave 腦波耳機之開發套件與本研究的影片標 記系統做結合,發展出一套基於注意力識別之影片學習補救教學系統:

在腦波訊號處理的相關文獻中,大多是採用 10-20 非侵入式腦波電極點 量測電極帽來擷取腦波訊號,而本研究使用的是單極點單通道的非侵入式腦 波量測耳機收集腦波訊號,雖然在腦波訊號擷取上不如 10-20 非侵入式腦波 電極點量測電極帽來的精確,但是具有配戴方便的優勢,在發展成實務應用 系統上較具潛力。由於目前本研究所發展的影片標記系統,尚未取得神念科 技所開發的 MindWave 腦波意念耳機之 SDK 軟體開發套件,因此尚無法發 展出能立即依據取得之原始腦波訊號進行高低注意力識別之系統,未來希望 可以將神念科技所開發的 MindWave 腦波意念耳機之開發套件與本研究之標 記系統結合,發展出一套基於注意力識別之影片學習補救教學系統。

44

二、將場景偵測技術與本研究之影片撥放系統結合,讓系統達到自動篩選低 注意力時間範圍的功能:

本研究在判讀低注意力影片片段時,係採用人工篩選方式進行,以受試 者具有低注意力時間點之場景畫面時間作為低注意力的時間範圍,未來希望 可以將場景偵測技術與本研究之影片撥放系統結合,讓系統達到自動篩選低 注意力時間範圍的功能。

三、未來可進行大規模的教學實驗,進一步驗證系統在輔助教學應用上的價 值:

若本研究發展之整合腦波注意力辨識系統與影片撥放系統之補救教學 系統能完成上述兩項功能的發展,將具有極高的輔助教學應用價值,未來即 可進行大規模的教學實驗,進一步驗證系統在輔助教學應用上的價值。

45

參考文獻

英文部分

[1] M. Murugappan, R. Nagarajan and S. Yaacob, “Classification of Human Emotion from EEG Using Discrete Wavelet Transform,”

Biomedical Science and Engineering, pp. 390-396, 2010.

[2] Harmony, T., Feranndez, T., Antonio, F.B., Juan, S.P., Bosch, J., Lourdes, D.C., and Galan L.,“EEG changes during word and figure categorization”

Clinical Neurophysiology, vol.112, pp.1486-1498, 2001.

[3] Wolpaw J. R., McFarland D. J., Neat G. W. and Forneris C. A.,“An EEG-based brain-computer interface for cursor control, ” Electroencephalography and neurophysiology, vol.78, pp.252-259, 1991.

[4] Schalk G., McFarland D. J., Hinterberger T., Birbaumer, N. and Wolpaw J.

R.,“BCI2000:A General-Purpose Brain-Computer Interface (BCI) System,”

IEEE transactions on bio-medical engineering, vol. 51, no. 6, pp.1034-1043, 2004.

[5] Scherer R., Müller G. R., Neuper C., Graimann B., and Pfurtscheller G.,

“An asynchronously controlled EEG-based virtual keyboard : improvement of the spelling rate,” IEEE transactions on biomedical engineering, vol.51, no. 6, pp.979-984, 2004.

[6] Wolpaw J. R., Birbaumer N., McFarland D. J., Pfurtscheller G. and Vaughan T. M., “Brain-computer interface for communication and control,”

Clinical Neurophysiology, vol.113, pp.767-791, 2002.

[7] Y. Zheng, G. Zhu, S. Jiang, Q. Huang and W. Gao, “Visual-aural attention modeling for talk show video highlight detection,” IEEE Int. Conf. on

46

Acoustics, Speech and Signal Processing, pp. 2213–2216, March 2008.

[8] Treisman A., “Strategies and models of selective attention,” Psychological Review, vol.76, no.3, pp.282- 299, 1969.

[9] Parasuraman R. and David D. R., Varieties of attention, Orlando:Academic Press, 1984.

[10] Klorman R., “Cognitive event-related postentials in attention deficit disorder,”Journal of Learning Disabilities, vol.24, no.3, pp.130-141, 1991.

[11] Guest Editors, “Guest editorial the third international meeting on brain-computer interface technology: making a difference,” IEEE Transactions on Rehabilitation Engineering, vol. 14, no. 2, pp.126-127, 2006.

[12] R. Cooper, J. W. Osselton and J. Crosley Shaw, EEG Technology, Butterworth, 3rd Edition, pp. 1-2, 1980.

[13] Nahm W., Stockmanns G., Petersen J., Gehring H., Konecny E., Kochs H.

D. and Kochs E., “Concept for an intelligent anaesthesia EEG monitor,”

Medical Informatics and the Internet in Medicine, vol.24, March 1999.

[14] Jeong J.,“EEG dynamics in patients with Alzheimer’s disease,”Clin.

Neurophysiol, vol.115, pp.1490–1505, 2004.

[15] Weng W. and Khorasani K., “An adaptive structure neural network with application to EEG automatic seizure detection,” Neural Network, vol. 9, pp.

1223–1240, August 1996.

[16] Pfurtscheller G., Müller G. R., Pfurtscheller J., Gerner H. J. and Rupp R.,

“Thought-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia,” Neuroscience letters, vol.351, pp.33-36, 2003.

[17] Webster J. G., “Electroencephalography: Brain electrical activity,”

47

Encyclopedia of medical devices and instrumentation, vol.2, pp. 1084-1107, 1988.

[18] Sanei S. and J. A. Chambers, EEG Signal Processing, John Wiley & Sons Ltd, 2007.

[19] T. K. Gregory and D. C. Pettus, “An Electroencephalographic Processing Algorithm Specifically Intended for Analysis of Cerebral Electrical Activity,” Journal of Clinical Monitoring and Computing, pp. 190-197, 2005.

[20] S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, Jul. 1989, pp. 674-693.

[21] A. Grossman and J. Morlet, “Decompositions of Hardy Functions into Square Integrable Wavelets of Constant Shape,” SIAM Journal of Mathematical Analysis, Vol. 15, No.4, Jul. 1984, pp. 723-736.

[22] Meyer. Y., “Real analysis and operator theory,” Pseudo-differential operators and applications, Proc. Svmp. Pure Math, 4S, pp.219-235, 1985.

[23] S. G. Mallat, “A theory for multiresolution signal decomposition:

thewavelet representation,” Pattern Analysis and Machine Intelligence, vol.

11, no.7, pp. 674-693, July 1989.

[24] I. Daubechies, “Orthonormal bases of compactly supported wavelets,”

Communications on Pure and Applied Mathematics, vol. 41, no. 7, pp.

909-996, 1988.

[25] Newland D. E., An Introduction to Random Vibrations, Spectral and Wavelet Analysis Longman Scientific & Technical, England, 1993.

[26] C. P. Shen, C. C. Chen, S. L. Hsieh, W. H. Chen, J. M. Chan, C. M. Chen, F.

48

Lai and M. J. Chiu, “High-Performance Seizure Detection System Using a Wavelet-Approximate Entropy-fSVM Cascade With Clinical Validation,”

EEG and Clinical Neuroscience Society (ECNS), doi:10.1177/1550059413483451., April 2013.

[27] Steven. M. Pincus, “Approximate entropy as a measure of system complexity,” Proc. Natl. Acad. Sci. USA, vo1.88 , pp2297-2301. , March 1991.

[28] L. Chen, W. Luo, Y. D. Zhen and S. Zeng, “Characterizing the complexity of spontaneous electrical signals in cultures neuronal networks using approximate entropy,” IEEE Trans. Information Technology in Biomedicine, vol. 13, no. 3, pp. 405-410, May 2009.

[29] L. I. Rudin, S. Osher and E. Fatemi. “Nonlinear total variation based noise removal algorithms,” Physical D, vol. 60, pp.259-268,1992.

[30] Feng Zhao Mingzhu Shi and Tingfa Xu, “A New Image Restoration Model Based on the Adaptive Total Variation,” Digital Manufacturing and Automation (ICDMA),vol. 1, pp. 63-66, December 2010.

[31] C. J. Lin, “A formal analysis of stopping criteria of decomposition methods for support vector machines,” IEEE Trans. Neural Network, vol.13, no.5, pp.1045-1052, Sep. 2002.

[32] C. J. Lin, “On the convergence of the decomposition method for support vector machines,” IEEE Trans. Neural Network, vol.12, no.6, pp.1288-1298, Nov. 2001.

[33] Hsu C. W., Lin C. J., “A comparison of methods for multi-class support vector machines,” IEEE Trans. on Neural Networks, vol.13, no.2, pp.415-42, Mar 2002.

49

[34] Clark L., Kempton M.J., and Scarnà A., “Sustained Attention-Deficit Confirmed in Euthymic Bipolar Disorder but Not in First-Degree Relatives of Bipolar Patients or Euthymic Unipolar Depression,” Biol Psychiatry, vol.57, pp.183–187, 2005.

[35] L. Wang, G. Xu, J. Wang, S. Yang, L. Guo, and W. Yan, “GA-SVM based feature selection and parameters optimization for BCI research,” Natural Computation (ICNC), Vol. 1, pp.580-583, July 2011.

50

中文部分

[36] 吳清山、林天祐,“教育小辭書”,五南圖書出版有限公司,2005。

[37] 宋淑慧,“多向度注意力測驗編製之研究”,國立彰化師範大學特殊教 育研究所碩士論文,1992。

[38] 鄭昭明,“認知心理學”,桂冠圖書公司,2006。

[39] 王緒溢, 梁仁楷, 劉子鍵, 柯華葳, 陳德懷,黃智偉,“應用於教室內之高 互動教學環境設計-無線測驗系統與網路教學資訊管理系統之整合應 用 ” , Glboal Chinese Conference on Computers in Education / International Conference on Computer-Assisted instruction, Vol.2, pp.1016-1023. , 2001.

[40] 鄭昭明,“認知心理學理論與實踐”,桂冠圖書股份有限公司,1993。

[41] 楊坤堂,“注意力不足過動異常:診斷與處遇”,五南圖書出版有限公 司,2000。

[42] 黃勝輝,“基於學習專注力發展自律學習機制提升網路學習成效”,國立 臺灣師範大學應用電子科技學系碩士論文,2012。

[43] 郭建成,“學習專注力監測提醒系統對於提升課堂教學成效之影響研究”,

國立臺灣師範大學工業教育學系碩士論文,2012。

[44] 廖允在,“腦波即時監控系統開發-音樂對腦波影響之案例研究”,國立 雲林科技大學電子工程所碩士論文,2007。

[45] 龔充文,“注意力:認知神經科學的取向,載於陳烜之主編:認知心理 學”,131-169 頁,五南圖書出版有限公司,2007。

[46] 鄭麗玉,“認知心理學:理論與應用”,五南圖書出版有限公司,2006。

[47] 鍾聖校,“認知心理學”,台北:心理出版社,1990。

[48] 廖新春,“注意力訓練電腦輔助方案對中重度智能不足兒童注意力行為

51

訓練效果之研究”,特殊教育研究學刊,2,177-206,1986。

[49] 郭旭鍾,“實施兒童讀經教學方案對國小一年級學童注意力影響之研究”,

台北市立教育大學課程與教學研究所碩士論文,2007。

[50] 黃秀瑄(譯),認知心理學(原作者:Best, John B.),臺北市:心理,

2009。

[51] 林崇德,“小學生心理學”,台北市:五南,1995。

[52] 多湖輝,“如何集中注意力”,台北:九大,1991。

[53] 洪偉哲,“以小波轉換鑑別人類情緒腦電波”,國立臺灣師範大學機電科 技學系碩士論文,2011。

[54] 劉時廷,“多通道腦波特徵抽取及分析之癲癇預測系統”,國立台灣大 學生醫電子與資訊學研究所碩士論文,2012。

[55] 蕭善勻,“應用近似熵及自我迴歸實現比流器飽和偵測與修正”,國立 台北科技大學自動化科技研究所碩士論文,2008。

[56] 林宏欣,“植基於遺傳演算法之模糊灰色預測控制器設計及應用”,國立 臺灣師範大學工業教育學系碩士論文,1996。

[57] 林豐澤,“演化式計算下篇:基因演算法以及三種應用實例”,智慧科 技與應用統計學報,第三卷,第一期,29-56,2005。

[58] 沈家平,“心電圖訊號分析演算法與硬體架構設計”,國立臺灣師範大學 工業教育學系碩士論文,2007。

相關文件