大腦人機介面為近幾年來很活躍的研究領域，然而腦電波包含著許多雜訊，並且具有非線性、非穩態等特性，所以腦電波的「特徵擷取」和「分類」為世界各國相關研究團隊共同努力的方向。本研究針對「特徵擷取」提供一套單筆腦電波分析法，可應用於大腦人機介面。此方法同時結合了獨立成份分析法和希爾伯特黃轉換來分析腦電波訊號。整個分析方法可以分為兩個階段：第一階段是利用獨立成份分析法的盲源分離特性把腦電波紀錄分解成具有時間、空間特性的成分；第二階段是利用一種時頻分析法，希爾伯特黃轉換將第一個階段所得到的成分做時頻分析。其中希爾伯特黃轉換的前半段架構被稱為經驗模態分解法，在本論文中用於訊號重建以及雜訊去除。當腦電波經過前面兩個階段的處理後，將可以同時保留腦電波的時間、頻率、空間特徵，建立時頻空間樣本。另外，本研究同時比較三種時頻分析法在處理腦電波訊號上的表現，分別為希爾伯特黃轉換、短時距傅立葉轉換、莫雷轉換。實驗結果為希爾伯特黃轉換在頻譜圖上不論高頻和低頻皆有較佳的解析度。
在研究流程的最後一個階段，選擇支持向量機分類四種想像動作(想像左手動、想像右手動、想像腳動、想像舌頭動)的腦電波資料，對於想像右手動可到達83.33%的辨識率，而整體四種想像動作的平均辨識率為54.17%。另外，本研究結合經驗模態分解與快速獨立成份分析法定位想像動作之訊號源，平均準確率可達到72.12%。

Brain computer interface (BCI) is a highly active field in the recent years. However, electroencephalogram (EEG) is a non-linear and non-stationary signal which contains various noises. Hence, the research groups in the world put effort into “feature extraction” and “classification” of EEG signal. In this study, an analysis strategy of single-trial EEG is developed for BCI and focus on “feature extraction”. The proposed method is to combine Independent Component Analysis (ICA) and Hilbert-Huang Transform (HHT) to analyze EEG. The EEG signal processing is performed in two stages: (1) ICA, a kind of Blind Source Separation (BSS), is used for dividing temporal-spatial components of EEG recordings; (2) HHT, a kind of temporal-frequency analysis (TFA), is used for processing these components. The empirical mode decomposition (EMD) in part of HHT is used for signal reconstruction and noise rejection. When the EEG is processed by above mentioned methods, the time-frequency-spatial features will be preserved. In additional, three kinds of TFAs (HHT, short time Fourier transform (STFT), and Morlet Transform) are experimented to compare the performance. The results show that the HHT has the better resolution in high frequency and low frequency.
The last stage of the research, Support vector machine (SVM) is selected to classify EEG data of four motor imageries (left hand, right hand, foot, and tongue). The experiment result presents that the classification accuracy of imaging “right hand” moving is 83.33%, and total averaged accuracy is 54.17%. In additional, the method combined with both EMD and FastICA has 72.12% averaged accuracy of signal source localization of motor imagery.

[1]Guest Editors, “Guest editorial the third international
meeting on brain-computer interface technology:
making a difference,” IEEE Trans. Rehabil. Eng., vol.
14, no. 2, pp. 126-127, 2006.
[2]F. Cincotti, L. Bianchi, G. Birch, C. Guger, J.
Mellinger, R. Scherer, R. N. Schmidt, O. Y. Suárez, and
G. Schalk, “BCI meeting 2005—workshop on technology:
hardware and software,” IEEE Trans. Rehabil. Eng., vol.
14, no. 2, pp. 128-131, 2006.
[3]A. Kübler, V. K. Mushahwar, L. R. Hochberg, and J. P.
Donoghue, “BCI meeting 2005—workshop on clinical
issues and applications,” IEEE Trans. Rehabil. Eng.,
vol. 14, no. 2, pp. 131-134, 2006.
[4]G. Dornhege, J. d. R. Millán, T. Hinterberger, D. J.
McFarland, and K. R. Müller, “Toward brain-computer
interfacing,” Cambridge, Mass.: MIT Press, 2007.
[5]S. G. Mason and G. E. Birch, “A brain-controlled switch
for asynchronous control applications,” IEEE Trans.
Biomed. Eng., vol. 47, no. 10, pp. 1297-1307, 2000.
[6]J. d. R. Millán and J. Mouriño, “Asynchronous BCI and
local neural classifiers: an overview of the adaptive
brain interface project,” IEEE Trans. Rehabil. Eng.,
vol. 11, no. 2, pp. 159-161, 2003.
[7]A. Kübler, B. Kotchoubey, T. Hinterberger, N. Ghanayim,
J. Perelmouter, M. Schauer, C. Fritsch, E. Taub, and N.
Birbaumer, “The thought translation device: a
neurophysiological approach to communication in total
motor paralysis,” Experiment Brain Research, vol.124,
no.2, pp. 223-232, 1999.
[8]R. Caton, “The Electric Currents of the Brain,”
British Medical Journal, Vol. 2, pp. 278, 1875.
[9]H. Berger, “Über das Elektrenkephalogramm des
Menschen,” European Archives of Psychiatry and Clinical
Neuroscience, vol. 87, pp. 527-570, 1929.
[10]H. Jasper, “Report of committee on methods of clinical
exam in EEG,” Electroencephalogr. Clin.
Neurophysiol. , vol. 10, pp. 370-375, 1958.
[11]E. H. Chudler, “Neuroscience for kids,” available at
the links for on-line courses at the author’s homepage
at
http://faculty.washington.edu/chudler/1020.html, 1996-
2008.
[12]L. Hu, B. H. Jansen, and N. N. Boutros, “Is P50 an
epiphenomenon?” Proc. of 27st Annual International
Conference of the IEEE EMBS, pp. 1166-1169, 2005.
[13]S. Sanei and J. A. Chambers, “EEG Signal Processing,”
John Wiley & Sons, Ltd, 2007.
[14]J. Bhattacharya and H. Petsche, “Phase Synchrony
analysis of EEG during music perception reveals
changes in functional connectivity due to musical
expertise,” Signal Processing, Vol. 85, pp. 2161-2177,
2005.
[15]A. R. Haig, E. Gordon, G. Roger, and J.
Anderson, “Classification of single-trial ERP sub-
types: application of globally optimal vector
quantization using simulated annealing,”
Electroencephalogr. Clin. Neurophysiol., vol. 94, pp.
288-297, 1995.
[16]H. Yabe, F. Satio, and Y. Fukushima, “Median method
for detecting endogenous event-related brain
potentials,” Electroencephalogr. Clin. Neurophysio.,
vol. 87, pp. 403-407, 1993.
[17]D. G. Childers, P. A. Bloom, A. A. Arroyo, S. E.
Roucos, I. S. Fischler, T. A. Apaopan, and N. W.
Perry, “Classification of cortical responses using
features from single EEG records,” IEEE Trans. Biomed.
Eng., vol. BME-29, 1982.
[18]T. P. Jung, S. Makeig, M. Westerfield, J. Townsend, E.
Courchesne, and T. J. Sejnowski, “Analysis and
visualization of single-trial event-related
potential,” Hum. Brain Mapp., vol. 14, Issue 3, pp.
166-185, 2001.
[19]J. Kalcher and G. Pfurtscheller, “Discrimination
between phase-locked and non-phase-locked event-related
EEG activity,” Electroencephalogr. Clin.
Neurophysiol., vol. 94, pp. 381-384, 1995.
[20]H. Ramoser, J. M. Gerking, and G.
Pfurtscheller, “Optimal spatial filtering of single
trial EEG during imagined hand movement,” IEEE Trans.
Rehab. Eng., vol. 8, pp. 441-446, 2000.
[21]B. Obermaier, C. Guger, C. Neuper, and G.
Pfurtscheller, “Hidden Markov models for online
classification of single trial EEG data,” Pattern
Recogn. Lett., vol.22, pp. 1299-1309, 2001.
[22]S. Lemm, B. Blankertz, G. Curio, and K. R.
Müller, “Spatio-spectral filters for improving the
classification of single trial EEG,” IEEE Trans.
Biomed. Eng., vol. 52, pp. 1541-1548, 2005.
[23]G. Pfurtsheller, C. Brunner, A. Schlögl, and F.H. Lopes
da Silva, “Mu rhythm (de)synchronization and EEG
single-trial classification of different motor imagery
tasks,” NeuroImage, vol. 31, pp. 153-159, 2006.
[24]K. R. Müller, M. Tangermann, G. Dornhege, M. Krauledat,
G. Curio, and B. Blankertz, “Machine learning for real-
time single-trial EEG-analysis: From brain-computer
interfacing to mental state monitoring,” Journal of
Neuroscience Methods, vol. 167, pp. 82-90, 2008.
[25]J. Britton, B. W. Jervis, and R. A.
Grünewald, “Extracting single trial event related
potentials,” IEE Proc. Sci. Meas. Technol., vol. 147,
pp. 382-388, 2000.
[26]S. Makeig, A. J. Bell, T. P. Jung, and T. J.
Sejnowski, “Independent component analysis of
electroencephalographic data,” Adv. Neural Inf.
Process. Syst., vol. 8, pp. 145-151, 1996.
[27]T. P. Jung, C. Humphries, T. W. Lee, S. Makeig, M. J.
Mckeown V. Iragui, and T. J. Sejnowski, “Extended ICA
removes artifacts from electroencephalographic data,”
Adv. Neural Inf. Proc. Syst., vol.10, pp.894-900, 1998.
[28]T. Demiralp, J. Yordanova, V.Kolev, A. Ademoglu, M.
Devrim, and V. J. Samar, “Time-frequency analysis of
single-sweep event-related potentials by means of fast
wavelet transform,” Brain and Language, vol. 66, pp.
129-145, 1999.
[29]T. Wang, J. Deng, B. He, “Classifying EEG-based motor
imagery tasks by means of time-frequency synthesized
spatial patterns,” Clin. Neurophysiol., vol. 115, pp.
2744-2753, 2004.
[30]N. Yamawaki, C. Wilke, and Z. Liu, “An enhanced time-
frequency-spatial approach for motor imagery
classification,” IEEE Trans. Neural Syst. And Rehab.
Eng., vol. 14, pp. 250-254, 2006.
[31]A. Delorme and S. Makeig, “EEGLAB: an open source
toolbox for analysis of single-trial EEG dynamics
including independent component analysis,” Journal of
Neuroscience Methods, vol. 134, pp. 9-21, 2004.
[32]N. E. Huang, Z. Shen, S. R. Long, M. Wu, H. Zheng, C.
Yen, C. C. Tung, and H. H. Liu, “The empirical mode
decomposition and the Hilbert spectrum for non-linear
and non-stationary time series analysis,” Proc. of the
Royal Society of London Series A—Math. Physic. And
Eng. Science, vol. 454, pp. 903-995, 1998.
[33]Z. D. Xiang, X. P. Wu, and X. J. Guo, “The EEG signal
preprocessing based on empirical mode decomposition,”
The 2nd International Conference on ICBBE, pp. 2131-
2134, 2008.
[34]M. Schwartz, W. Bennett, S. Stein, and W.
R., “Communications systems and techniques,” New
York: McGraw-Hill, 1966.
[35]P. Jahankhani, V. Kodogiannis ,and K. Revett, “EEG
signal classification using wavelet feature extraction
and neural networks,” IEEE 2006 International
Symposium on Modern Computing, pp. 52-57, 2006.
[36]A. Schlögl, F. Lee, H. Bischof, and G.
Pfurtscheller, “Characterization of four-class motor
imagery EEG data for the BCI competition 2005,”
Journal of Neural Engineering, vol. 51, pp. L14-L22,
2005.
[37]B. Blankertz, K. R. Müller, D. J. Krusienski, G.
Schalk, J. R. Wolpaw, A. Schlögl, G. Pfurtsheller, J.
R. Millán, M. Schröder, and N. Birbaumer, “The BCI
competition III: alternative approaches to actual BCI
problems,” IEEE Trans. Neural Syst. And Rehab. Eng.,
vol. 14, pp. 100-107, 2006.
[38]B. Hjorth, “An on-line transformation of EEG scalp
potentials into orthogonal source deviation,”
Electroencephalogr. Clin. Neurophysiol., vol. 39, pp.
526-530, 1975.
[39]T. Wang, J. Deng, and B. He, “Classifying EEG-based
motor imagery tasks by means of time-frequency
synthesized spatial patterns,” Clin. Neurophysiol.,
vol. 115, pp. 2744-2753, 2004.
[40]C. C. Chen, J. C. Hsieh, Y. Z. Wu, P. L. Lee, S. S.
Chen, D. M. Niddam, T. C. Yeh, and Y. T. Wu, “Mutual-
information-based approach for neural connectivity
during self-paced finger lifting task,” Human Brain
Mapping, vol. 29, pp. 265-280, 2008.
[41]Public editors, “Window function,” available at the
links at Wikipedia, the free encyclopedia at
http://en.wikipedia.org/wiki/Window_function .
[42]F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B.
Arnaldi, “A review of classification algorithms for
EEG-based brain computer interface,” Journal of Neural
Engineering, vol. 4, pp. R1-R13, 2007.
[43]S. Theodoridis and K. Koutroumbas,“Pattern
Recognition,”ACADEMIC PRESS, second edition, 2003.
[44]C. C. Chang and C. J. Lin, “LIBSVM: a library for
support vector machines,” Software available at
http://www.csie.ntu.edu.tw/~cjlin/libsvm , 2001.