Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53582
Full metadata record
???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
---|---|---|
dc.contributor.advisor | 張帆人(Fan-Ren Chang) | |
dc.contributor.author | Chia-Hsin Chen | en |
dc.contributor.author | 陳家興 | zh_TW |
dc.date.accessioned | 2021-06-16T02:25:59Z | - |
dc.date.available | 2017-08-16 | |
dc.date.copyright | 2015-08-16 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-08-05 | |
dc.identifier.citation | C. Guger, G. Edlinger, W. Harkam, I. Niedermayer, and G. Pfurtscheller, 'How many people are able to operate an EEG-based brain-computer interface (BCI)?,' IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, pp. 145-147, Jun 2003.
[2] A. J. Doud, J. P. Lucas, M. T. Pisansky, and B. He, 'Continuous Three-Dimensional Control of a Virtual Helicopter Using a Motor Imagery Based Brain-Computer Interface,' Plos One, vol. 6, Oct 26 2011. [3] K. Lafleur, K. Cassady, A. Doud, K. Shades, E. Rogin, and B. He, 'Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface,' J Neural Eng, vol. 10, p. 046003, Jun 4 2013. [4] Hyun Seok Kim, Min Hye Chang, Hong Ji Lee, and Kwang Suk Park, Senior Member, IEEE. “A Comparison of Classification Performance among the Various Combinations of Motor Imagery Tasks for Brain-Computer Interface”, 6th Annual International IEEE EMBS Conference on Neural Engineering San Diego, California, 6 - 8 November, 2013 [5] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., 'PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals,' Circulation, vol. 101, pp. E215-E220, Jun 13 2000. [6] Hugo Gamboa(2005, December 1). Networks [Online]. Available: http://en.wikipedia.org/wiki/Electroencephalography [7] Kirmizi-Alsan, Elif; Bayraktaroglu, Zubeyir; Gurvit, Hakan; Keskin, Yasemin H.; Emre, Murat; Demiralp, Tamer (2006). 'Comparative analysis of event-related potentials during Go/NoGo and CPT: Decomposition of electrophysiological markers of response inhibition and sustained attention'. Brain Research 1104 (1): 114–28 [8] Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R., et al. (2000). Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria. Psychophysiology, 37(2), 127-152 [9] David B. Fankhauser ,Ph.D., Professor of Biology and Chemistry University of Cincinnati Clermont College (2008, March 1). Networks [Online]. Available: http://biology.clc.uc.edu/fankhauser/Labs/Anatomy_&_Physiology/A&P202/Nervous_System_Physiology/EEG_protocol2.html [10] Mononomic (2008, December 21). Networks [Online]. Available: https://commons.wikimedia.org/wiki/File:ComponentsofERP.svg [11] A. Hiraiwa, K. Shimohara, Y. Tokunaga, “EEG topography recognition by neural network,” Eng. Med. Biol., pp. 39-42, 1990. [12] Lüder Deecke (2005, December 25). Networks [Online]. Available: http://commons.wikimedia.org/wiki/File:Bereitschaftspotenzial_fig1.jpg [13] Gert Pfurtscheller, Christa Neuper, and Niels Birbaumer , Human Brain–Computer Interface , page 8 [14] H. Ramoser, J. M. Gerking, and G. Pfurtshceller, “Optimal spatial filtering of single trial EEG during imagined hand movement, ” IEEE Trans. Rehab. Eng.,vol.8, no.4, pp.441-446,2000. [15] S. Lemm, B. Blankertz, G. Curio, and K. R. Muller, 'Spatio-spectral filters for improving the classification of single trial EEG,' Ieee Transactions on Biomedical Engineering, vol. 52, pp. 1541-1548, Sep 2005. [16] K. K. Ang, Z. Y. Chin, C. Wang, C. Guan, and H. Zhang, 'Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b,' Front Neurosci, vol. 6, p. 39, 2012 [17] C. C. Jou (2014, March 20). Networks [Online]. Available: https://ccjou.wordpress.com/2014/03/20/%E7%B7%9A%E6%80%A7%E5%88%A4%E5%88%A5%E5%88%86%E6%9E%90/ [18] C. C. Jou (2014, March 14). Networks [Online]. Available: https://ccjou.wordpress.com/2014/03/14/%E8%B2%BB%E9%9B%AA%E7%9A%84%E5%88%A4%E5%88%A5%E5%88%86%E6%9E%90%E8%88%87%E7%B7%9A%E6%80%A7%E5%88%A4%E5%88%A5%E5%88%86%E6%9E%90/ [19] Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew,”Extreme learning machine: Theory and applications”, Neurocomputing 70 (2006) 489-501 [20] G.-B. Huang, H.A. Babri, Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions, IEEE Trans. Neural Networks 9(1) (1998) 224-229 [21] G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, 'BCI2000: a general-purpose brain-computer interface (BCI) system,' IEEE Trans Biomed Eng, vol. 51, pp. 1034-43, Jun 2004 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53582 | - |
dc.description.abstract | 非侵入式動作意象人腦電腦介面系統是一種以動作意象的方式使得人腦可以與外部裝置溝通的介面系統,藉由想像不同的動作對外部裝置下指令以達到腦波控制的效果。然而在非侵入式動作意象人腦電腦介面系統中,使用腦電圖(EEG, Electroencephalography)作為系統控制源導致很容易受外界雜訊干擾。除此之外,個人的用腦習慣也會影響腦波特徵的提取。因此如何提高不同動作意象的分類正確率就成為非侵入式動作意象人腦電腦介面系統的主要問題。
本論文提出一個基於極限學習機的單隱藏層前饋類神經網路作為分類器的動作意象分類演算法,並以106位受試者所提供的不同動作意象64通道腦電圖(EEG)的資料,對六組不同的兩種動作組合作二分類,並分析106位受試者的平均分類正確率及標準差。在EEG訊號處理過程中,使用第5階巴特沃斯帶通濾波器將6 Hz -30 Hz的腦波頻段濾出來,接著以共同空間型樣法將64通道EEG資料縮減並降維成6通道資料,最後提取腦波特徵後丟給後端分類器作訓練與測試。本論文提出的方法於六個項目的二分類平均正確率皆可達到83%以上。 在本論文中亦比較傳統上以線性判別分析為分類器的分類演算法在相同資料下的分類結果,並且嘗試不同的帶通濾波器濾波段來提升傳統的分類演算法的分類正確率,最後發現本論文提出的方法在六個分類項目都是優於傳統上的使用線性判別分析的分類演算法。 | zh_TW |
dc.description.provenance | Made available in DSpace on 2021-06-16T02:25:59Z (GMT). No. of bitstreams: 1 ntu-104-R02921011-1.pdf: 5045996 bytes, checksum: e79104287f4beb4c1e41f3e2231376b9 (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 摘要 I
Abstract II 目錄 III 圖目錄 V 表目錄 VII 1. 第一章 緒論 1 1.1 研究動機 1 1.2 相關研究 2 1.3 本論文提出之方法 2 1.4 本論文組織架構 3 2. 第二章 腦波與人腦電腦介面簡介 4 2.1 腦波簡介 4 2.2 事件相關電位 6 2.3 動作意象人腦電腦介面 10 3. 第三章 演算法架構與理論 12 3.1 動作意象分類演算法架構 12 3.2 本論文提出之基於極限學習機的動作分類演算法 15 3.3 共同空間型樣法原理推導 22 3.4 線性判別分析推導 26 3.5 單隱藏層前饋類神經網路與極限學習機介紹 31 3.5.1 傳統單隱藏層前饋類神經網路 31 3.5.2 極限學習機 35 4. 第四章 實驗結果與討論 39 4.1 PHYSIONET介紹: 動作想像之腦電波線上資料 39 4.2 線性判別分析作為分類器實驗結果 42 4.3 基於極限學習機的分類演算法實驗結果 51 5. 第五章 結論與未來工作 58 5.1 結論 58 5.2 未來工作 59 參考文獻 60 | |
dc.language.iso | zh-TW | |
dc.title | 基於極限學習機之演算法應用於動作意象分類 | zh_TW |
dc.title | Extreme Learning Machine Based Algorithms for Motor Imagery Classifications | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 王立昇(Li-Sheng Wang) | |
dc.contributor.oralexamcommittee | 王伯群(Bo-Qun Wang),林君明(Jun-Ming Lin),王和盛(He-Sheng Wang) | |
dc.subject.keyword | 極限學習機,單隱藏層前饋類神經網路,共同空間型樣法,動作意象人腦電腦介面, | zh_TW |
dc.subject.keyword | extreme learning machine,common spatial pattern,SLFNs,Non-invasive motor imagery brain-computer-interface, | en |
dc.relation.page | 62 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-08-05 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
Appears in Collections: | 電機工程學系 |
Files in This Item:
File | Size | Format | |
---|---|---|---|
ntu-104-1.pdf Restricted Access | 4.93 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.