研究生: |
沈世評 Shi-Ping Shen |
---|---|
論文名稱: |
用線性鑑別分析法做冥想四個方向的分類 Distinguishing the four Directions in Meditation through Linear Disciminant Analysis |
指導教授: |
葉榮木
Yeh, Zong-Mu |
學位類別: |
碩士 Master |
系所名稱: |
機電工程學系 Department of Mechatronic Engineering |
論文出版年: | 2005 |
畢業學年度: | 93 |
語文別: | 中文 |
論文頁數: | 60 |
中文關鍵詞: | 大腦人機介面 、腦電波 、線性鑑別分析法 |
英文關鍵詞: | BCI, EEG, LDA |
論文種類: | 學術論文 |
相關次數: | 點閱:226 下載:34 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
大腦人機介面(Brain-computer interface)是一種利用腦部訊號跟外界溝通的新技術,其目的是能夠幫助因神經肌肉損傷而行動受阻礙的人。對於內部刺激—想像左右手和腳動—已經有研究過了,但冥想四個方向是值得去研究。有鑑於此,本研究建立一套系統,它以冥想四個方向實驗的腦波訊號作為輸入訊號並利用快速傅立葉分析法找出腦波的特徵,然後利用線性鑑別分析法來分辨這些特徵。最後找出一組可以分辨冥想四個方向的腦波。
經由實驗結果得知,此系統可以利用實驗中分辨率最高的資料做為參考腦波,最佳的分辨率可達80%以上。未來可預計將此運用於人機介面上以造福神經疾病患者或行動不便人士。
The brain-computer interface (brain-computer interface) is a kind of new technology of utilizing brain signals to communicate with the external world. Its purpose is to help the person who is hindered take action because his neural muscle is damaged. Internal stimuli, such as imaging right-hand, left-hand, and foot moving, have been studied, but imaging four directions in meditation is worth being studied. For this reason, a system is developed, and the EEG of the four directions in meditation is taken as input signals and a fast Fourier analysis is used to find the features of the EEG. Then a linear discriminant analysis is adopted to classify the features. Finally, a set of brain waves, which can distinguish four directions in meditation, is obtained.
From the experimental results, it is better to use the data which the accuracy of the classification is the highest in the experiment to be the reference material. The best classification rate in the experiment data is more than 80%. In the future study, the system can be applied to brain-computer interface to benefit neural disease persons or disabled persons.
[1] 吳岳昌(民92)。探討ERD方法在腦機介面系統設計之效能。國立交通大學電機與控制工程學系碩士論文。
[2] 李國生(民93)。應用資料挖掘技術於長程聽覺誘發波P300與智商關係之研究。國立台南師範師學院資訊教育研究所碩士論文。
[3] 林景福(民77)。圖解腦波入門。台北市:九州。
[4] 林錦俊(民89)。雙記憶體之快速傅立葉轉換處理器的設計與製作。中華大學電機工程研究所碩士論文。
[5] 洪至懿(民91)。特徵擷取與分類應用與想像左右手手指運動之腦波辨識。國立陽明大學放射醫學科學研究所碩士論文。
[6]謝維廷(民91),禪定腦電波頻帶判讀系統設計,國立交通大學電機與控制工程學系。
[7] Despain, A. M. ,“Very fast transform algorithms for hardware implementation”, IEEE Trans. Comput., vol C-28, pp. 333-341,May 1979.
[8] Haykin S. .Neural Network: A Comprehensive Foundation.–2nd ed, Prentice Hall.1999.
[9] Pfurtscheller G. “ERD as an index of anticipatory behavior”, Handbook of electroencephalography and clinical neurophysiology revised series, vol. 6, pp. 203-217., June 1999.
[10] Allison Brendan Z. and Pineda Jaime A., “ERPs Evoked by Different Matrix Sizes: Implications for a Brain Computer Interface (BCI) System”, IEEE Trans on Neural sys. and Rehabil Eng., vol. 11, no.2, June 2003.
[11] Gibson Oliver J., and James Christopher J. ,”Temporally Constrained ICA: An Application to Artifact Rejection in Electromagnetic Brain Signal Analysis”, IEEE Trans. on Biomedical Eng. , vol. 50, no. 9, Sep. 2003.
[12] James C.J. and Lowe D. ,” Single channel analysis of electromagnetic brain signals through ICA in a dynamical systems framework”, Neural computing research group, Aston university, Birmingham, United Kingdom, 2001.
[13] McFarland D.J. and Wolpaw J.R., “ Multichannel EEG-based brain-computer communication”, Electroencephalography and Clinical Neurophysiology,vol.90, pp.444-9,1994.
[14] Aleksandar Kostov, Julio Carballido, and Jorge Martinez,” Enhancement of EEG control signals in the development of a brain-computer interface”, Faculty of rehabilitation medicine, university of Alberta, 13-16, Oct.1999.
[15] Akiyama T., Gotman J., James C.J., Kobayashi K., and Nakahori T.,” Isolation of epileptiform discharges from unaveraged EEG by independent component analysis”, Clinical Neuro. vol. 110, 1999.
[16] Cheng Ming, Gao Shangkai ,and Gao Xiaorong,” Design and implementation of a brain-computer interface with high transfer rates” IEEE Trans on Bio. Eng., vol. 49, no. 10, Oct. 2002.
[17] Donchin E., Spencer K. M., and Wijesinghe R. ,”The mental prosthesis: assessing the speed of a P300-based brain-computer interface”, IEEE Trans Rehabil Eng, vol.8, no.2, June 2000.
[18] Duda R.O., Hart P. E., and Stork D.G. (2001). Pattern Classification.–2nd ed. (pp30-76). Canada, John Wiley & Sons , Inc.2001.
[19] Troster G., Thaler M., and Wosnitza M.,” A high precision 1024-point FFT processor for 2D convolution”, IEEEE Int. Solid-State Circuit Conf., vol. 41, pp. 118-119, 424, May 1998.
[20] Calhoun G., Jones K. S., McMillan G., and Middendorf M.,” Brain-computer interfaces based on the steady-state visual-evoked response”, IEEE Trans Rehabil Eng., vol.8, pp.211-4, June 2000.
[21] Graimann Bernhard, Mller G., Neuper Christa, and Pfurtscheller G.,” An Asynchronously Controlled EEG-Based Virtual Keyboard: Improvement of the Spelling Rate”, IEEE Trans. Rehabil Eng., vol. 51, no. 6, pp. 979-984, June 2004.
[22] Birbaumer N., Kbler A., Mller G., Neuper C., and Pfurtscheller G., “Clinical application of an EEG-based brain-computer interface: A case study in a patient with severe motor impairment”, Clin. Neurophysiol., vol. 114, no. 3, pp. 399–409, March 2003.
[23] Birbaumer N., Donchin E., Heetderks W.J., McFarland D.J., Peckham P.H., Robinson C.J., Quatrano L.A., Schalk G., Vaughan T.M., and Wolpaw J.R. ,” Brain-computer interface technology:a review of the first international meeting”, IEEE Trans. Rehabilitation Eng., vol.8, pp.164-73, May 2000.
[24] Jaime A. Pineda, David S. Silverman, Andrey Vankov, and John Hestenes, “Learning to Control Brain Rhythms: Making a Brain-Computer Interface Possible”, IEEE Trans. Neural. Syst. vol. 11, no. 2, June 2003.
[25] B. Obermaier, G. R. Mller, and G. Pfurtscheller, “Virtual Keyboard”Controlled by Spontaneous EEG Activity”, IEEE Trans. Neural. Syst. vol.11 no. 4, Dec. 2003.
[26] J. R. Wolpaw, D. J. McFarland, and T. M. Vaughan, “Brain–Computer Interface Research at the Wadsworth Center”, IEEE Trans. Rehabilitation Eng., vol.8 no.2, June 2000.
[27] Reinhold Scherer*, Gernot R. Mller, Christa Neuper, Bernhard Graimann, Gert Pfurtscheller, “An Asynchronously Controlled EEG-Based Virtual Keyboard: Improvement of the Spelling Rate”, IEEE Trans. Biomedical Eng., vol. 51, no. 6, June 2004.