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| 論文名稱 | 利用隱馬可夫模型處理腦電波訊號 |
| 研討會開始日期 | 2017-11-17 |
| 研討會結束日期 | 2017-11-17 |
| 所有作者 | 梁丁文、楊雄斌、吳志峰、戴莉蓁 |
| 作者順序 | 第一作者 |
| 通訊作者 | 是 |
| 研討會名稱 | 2017創新發明應用研討會(AII 2017) |
| 是否具有對外公開徵稿及審稿制度 | 是 |
| 研討會舉行之國家 | NATTWN-中華民國 |
| 研討會舉行之城市 | 台中 |
| 發表年份 | 2017 |
| 所屬計劃案 | 統一美學(UMA)在不同情境下之美學探討與機器自動判讀美感之研究 |
| 可公開文檔 |
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| 可公開文檔 | |
| 可公開文檔 |
[摘要] :
隱馬可夫模式(HMM, Hidden Markov Model)是一種計算預測的機率方法，它是延伸馬可夫鏈並導入隱藏狀態的計算模式，在這種模式中，有兩種類型的狀態同時在改變中，一種是可觀察到的狀態變化，另一種是無法觀察到的狀態變化，同時兩種狀態之間也存在互相關連的機率。腦電波(Electroencephalography, EEG)資料是由連結頭部外層機器所讀取到的微弱電波訊號，代表大腦内部不同區域的狀態變化。本研究使用隱馬可夫模式方法，應用於澳洲旋濱大學建構美學偏好所得的EEG腦電波資料，計算其機率模型，推估美學偏好機率，並根據結果反推估其成立的可能性組合，並透過解出的結構學習預測其可能的判讀結果。
[參考文獻] :
[1] W. R.Gilks, S.Richardson, and D.Spiegelhalter, Markov chain Monte Carlo in practice. CRC press, 1995.
[2] P. J.GREEN, “Reversible jump Markov chain Monte Carlo computation and Bayesian model determination,” Biometrika, vol. 82, no. 4, pp. 711–732, 1995.
[3] R. M.Neal, “Markov chain sampling methods for Dirichlet process mixture models,” J. Comput. Graph. Stat., vol. 9, no. 2, pp. 249–265, 2000.
[4] G.Tauchen, “Finite state markov-chain approximations to univariate and vector autoregressions,” Econ. Lett., vol. 20, no. 2, pp. 177–181, 1986.
[5] S. R.Eddy, “Hidden Markov models,” Current Opinion in Structural Biology, vol. 6, no. 3. Elsevier Current Trends, pp. 361–365, 01-Jun-1996.
[6] L. R.Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proc. IEEE, vol. 77, no. 2, pp. 257–286, 1989.
[7] M.McCormick, R.Ma, andT.Coleman, “An analytic spatial filter and a hidden Markov model for enhanced information transfer rate in EEG-based brain computer interfaces,” Acoust. Speech Signal Process. (ICASSP), 2010 IEEE Int. Conf., vol. 1, pp. 602–605, 2010.
[8] G.Moruzzi andH. W.Magoun, “Brain stem reticular formation and activation of the EEG,” Electroencephalogr. Clin. Neurophysiol., vol. 1, no. 1, pp. 455–473, 1949.
[9] F.Lotte, “A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces,” in Guide to Brain-Computer Music Interfacing, 2014.
[10] K.Sivasankari andK.Thanushkodi, “An Improved EEG Signal Classification Using Neural Network with the Consequence of ICA and STFT,” J Electr Eng Technol, vol. 9, no. 3, pp. 1060–1071, 2014.
[11] B.Direito, J.Duarte, M.LeVan Quyen, F.Sales, andA.Schulze-Bonhage, “Feature selection in high dimensional EEG features spaces for epileptic seizure prediction,” in 18th International Federation of Automatic Control Wolrd Congress, 2011, pp. 6206–6211.
[12] P.Jahankhani, V.Kodogiannis, andK.Revett, “EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks,” in IEEE International Symposium on Modern Computing, 2006.
[13] L.Guo, D.Rivero, J.Dorado, C. R.Munteanu, andA.Pazos, “Automatic feature extraction using genetic programming: An application to epileptic EEG classification,” Expert Syst. Appl., vol. 38, no. 8, pp. 10425–10436, 2011.
[14] S.Banerjee, “ECG Feature Extraction and Classification of Anteroseptal Myocardial Infarction and Normal Subjects using Discrete Wavelet Transform,” Syst. Med. Biol., no. December, pp. 55–60, 2010.
[15] F.Lotte andM.Congedo, “A review of classification algorithms for EEG-based brain–computer interfaces,” J. neural …, vol. 7, no. 10, p. e44439, 2007.
[16] Y.Zheng, Q.Liu, E.Chen, Y.Ge, andJ. L.Zhao, “Time series classification using multi-channels deep convolutional neural networks,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8485 LNCS, pp. 298–310, 2014.
[17] Q.Yuan, W.Zhou, S.Li, andD.Cai, “Epileptic EEG classification based on extreme learning machine and nonlinear features,” Epilepsy Res., vol. 96, no. 1–2, pp. 29–38, 2011.
[18] I.Guler andE. D.Ubeyli, “Multiclass Support Vector Machines for EEG-Signals Classification,” Inf. Technol. Biomed. IEEE Trans., vol. 11, no. 2, pp. 117–126, 2007.
[19] R.Panda et al., “Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction,” in 2010 International Conference on Systems in Medicine and Biology, 2010, no. December, pp. 405–408.
[20] A. M.Maran andS.Saravanan, “Artificial Neural Networks ( ANNs ) for EEG Purging using Wavelet Analysis,” Int. J. Electron. Commun. Eng., vol. 4, no. 5, pp. 563–570, 2011.
[21] N.Gupta, “Artificial Neural Network,” vol. 3, no. 1, pp. 24–28, 2013.
