本論文基於時間序列計算相關係數，用於偵測想像運動(Motor Imagery, MI)開始時間，擷取有效的腦電圖(Electroencephalography, EEG)訊號，提出方法利用𝜇波的相關係數，能夠找到有效EEG 訊號的起始時間點。因而降低計算負擔並有效減少共空間形樣法(Common Spatial Patterns, CSP)特徵提取的資料量大小，最後使用支持向量機(Support Vector Machine, SVM)達成分類準確度的提升。此外，提出方法在腦機介面(Brain-Computer Interface, BCI)的應用上，結合虛擬實境(Virtual Reality, VR)提出偵測想像運動的演算法。

The thesis, based on time series, calculates the correlation coefficient, and then detects the start time of motor imagery (MI). Moreover, the thesis proposes a method to capture the effective Electroencephalography (EEG) signal. Then using the correlation coefficient of the 𝜇 wave, the method could find out the starting position of the effective EEG signal. Therefore, it dramatically reduces the amount of EEG data, which effectively reduces the computation load for feature extracted by common spatial patterns (CSP). Finally support vector machine (SVM) is used to improve the classification accuracy. Furthermore, in the application of brain-computer interface (BCI) combined with virtual reality (VR), an algorithm for detecting MI is demonstrated.

[1] X. An, D. Kuang, X. Guo, Y. Zhao, and L. He, “A deep learning method for classification of EEG data based on motor imagery,” Proceedings of the International Conference on Intelligent Computing in Bioinformatics, vol. 8590, D.-S. Huang, K. Han, and M. Gromiha, Eds., August, 2014, pp. 203–210.
[2] T.-H. Nguyen, D.-L. Yang, and W.-Y. Chung, “A High-Rate BCI Speller Bases on Eye-Closed EEG Signal,” IEEE Access, vol. 6, pp.33995-34003, June, 2018.
[3] L. F. Nicolas-Alonso, R. Corralejo, J. Gomez-Pilar, D. Álvarez, and R. Hornero, “Adaptive Stacked Generalization for Muticlass Motor Imagery-Based Brain Computer Interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 4, pp.702-712, February, 2015.
[4] N. Manmmone, F. L. Foresta, and F. C. Morabito, “Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA,” IEEE Sensors Journal, vol. 12, no. 3, pp. 533-542, April, 2011.
[5] L. F. Nicolas-Alonso, and J. Gomez-Gil, “Brain Computer Interfaces, a Review,” Sensors, vol. 12, no. 2, pp. 1211-1279, Jane, 2012.
[6] G. Pfurtscheller, C. Neuper, D. Flotzinger, and M. Pregenzer, “EEG-based discrimination between imagination of right and left hand movement,” Electroencephalography and Clinical Neurophysiology, vol. 103, no. 6, pp. 642–651, July, 1997.
[7] H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller, “Optimal Spatial Filtering of Single Trial EEG During Imagined Hand Movement,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 4, pp. 441-446, December, 2000.
[8] Q. Novi, C. Guan, T. H. Dat, and P. Xue, “Sub-band common spatial pattern (SBCSP) for brain-computer interface,” in Proc. 3rd Int.IEEE/EMBS Conf. Neural Eng., May 2007, pp. 204–207.
[9] K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, “Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface,” 2008 IEEE International Joint Conference on Neural Networks, pp. 2390-2397, June, 2008.
[10] C. Junli, and J. Licheng, “Classification mechanism of support vector machines,” WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000, August, 2002.
[11] B. Xu, L. Zhang, A. Song, C. Wu, W. Li, D. Zhang, G. Xu, H. Li, and H. Zeng, “Wavelet Transform Time-Frequency Image and Convolutional Network-Based Motor Imagery EEG Classification,” IEEE Access, vol. 7, pp. 6084-6093, December, 2018.
[12] W.-L. Zheng, J.-Y. Zhu, Y. Peng, and B.-L. Lu, “EEG-based emotion classification using deep belief networks,” in Proc. IEEE Int. Conf. Multimedia Expo (ICME), July 2014, pp. 1-6.
[13] D. Jenson, A. L. Bowers, D. Hudock, and T. Saltuklaroglu, “The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering,” Front. Hum. Neurosci., vol. 13, January, 2020.
[14] C. Brunner, R. Leeb, G. R. Mu ̈ller-Putz, A. Schlo ̈gl, and G. Pfurtscheller, “BCI Competition 2008 – Graz data set A,” Institute for Human-Computer Interfaces, Graz University of Technology, 2008
[15] F. Shahlaei, N. Bagh, A. D. Shaligram, M. R. Reddy and M. S. Zambare, “Classification of Motor Imagery Tasks Using Inter Trial Variance In The Brain Computer Interface,” 2018 IEEE International Symposium on Medical Measurements and Applications, pp. 11-13, June, 2018.
[16] Z. J. Koles, M. S. Lazar, and S. Z. Zhou, “Spatial Patterns Underlying Population Differences in the Background EEG,” Brain Topography, vol. 2, no.4, pp. 275-284, June, 1990.
[17] J. Mu ̈ller-Gerking, G. Pfurtscheller, and H. Flyvbjerg, “Designing optimal spatial filters for single-trial EEG classification in a movement task,” Clinical Neurophysiology, vol. 110, no. 5, pp. 787-798, May 1999.
[18] Y. Yu, Y. Liu, J. Jiang, E. Yin, Z. Zhou, and D. Hu, “An Asynchronous Control Paradigm Based on Sequential Motor Imagery and Its Application in Wheelchair Navigation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 12, pp. 2367-2375, November, 2018.