The ICA applied to power spectrum of ICA components can successfully remove most of EEG artifacts and also estimate people’s drowsiness degree. In order to achieve the target of window-based and real-time EEG signal processing on the DSP-based BCI system, this thesis proposes the window-based ICA described in Chapter 4 and can achieve good results of subject’s drowsiness state. Due to updating results in time (inside 2s), the execution time of signal processing (ICA and spectrum analysis) has to be limited. Even if the iteration is restricted, there is still the good outcome of all ICA components and EEG-based drowsiness estimation. This result will be applied to live drowsiness estimation.
The unavailability of a BCI capable of window-based signal processing and artifact correction or separation has long limited the use of BCI in operational environments. This study implemented a moving-windowed window-based ICA and spectral estimation on a miniaturized, battery-powered and light-weight embedded BCI. The empirical results showed that the efficacy of window-based signal separation was comparable to that of the offline implementation. The remaining issue is to develop an algorithm to automatically select the performance-related independent component(s).
The window-based ICA algorithm can be implemented with FPGA or DSP to achieve the capability of real-time processing. In the future, the
next importance issue of window-based ICA is focused on the informative component selected automatically during the restricted time.
In conclusion, this study demonstrated the feasibility of window-based signal processing and source separation on a wearable miniature embedded BCI. This demonstration could lead to a practical wearable BCI for the monitoring of the brain functions of unconstrained participants performing normal tasks in the workplace and home.
References
[1] S.G. Mason, A. Bashashati, M. Fatourechi, K.F. Navarro, and G.E. Birch,
“Acomprehensive survey of brain interface technology designs,’’ Ann. Biomed.
Eng.,vol. 35, no. 2, pp. 137–169, 2007.
[2] A. Kachenoura, L. Albera, L. Senhadji, and P. Comon, “ICA: A Potential Tool for BCI Systems,” IEEE Signal Processing Magazine, pp. 57-68, Jan 2008.
[3] J. Kalcher, D. Flotzinger, C. Neuper, S. Gölly, and G. Pfurtscheller, “Graz brain–computer interface II: Toward communication between humans and computers based on online classification of three different EEG patterns,” Med.
Biol. Eng. Comput., vol. 34, pp. 382–388, 1996.
[4] C. Guger, W. Harkam , C. Hertnaes, G. Pfurtscheller, “Prosthetic Control by an EEG-based Brain-Computer Interface (BCI)”, Proceedings of AAATE 5th European Conference for the Advancement of Assistive Technology. Düsseldorf, Germany, November 3-6, 1999.
[5] M. Falkenstein, J. Hohnsbein, J. Hoormann, and L. Blanke, “Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks,” Electroencephalography and Clinical Neurophysiology, vol. 78, pp. 447-55, 1991.
[6] S. Makeig, M. Westerfield, T.P. Jung, S. Enghoff, J. Townsend, E. Courchesne, and T. J. Sejnowski, “Electroencephalographic Sources of Visual Evoked Responses,” Science, vol. 295, pp. 690-94, Jan. 25,2002.
[7] M.D. Serruya, N.G. Hatsopoulos, L. Paninski, M.R. Fellows, J.P. Donoghue,
“Instant neural control of a movement signal,” Nature, vol. 416,pp. 141–142, 2002.
[8] D.M. Taylor, S.I. Tillery, A.B. Schwartz, “Direct cortical control of 3D neuroprosthetic devices,” Science, vol. 296, pp. 1829–1832, 2002.
[9] J.K. Chapin, K.A. Moxon, R.S. Markowitz, M.A.L. Nicolelis, “Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex,”
Nature Neuroscience, vol. 2, pp. 664–670, 1999.
[10] J. Wessberg, C.R. Stambaugh, J.D. Kralik, P.D. Beck, M. Laubach, “Realtime prediction of hand trajectory by ensembles of cortical neurons in primates,”
Nature, vol. 408, pp. 361–365, 2000.
[11] 連怡仲，“數位腦波機系統之設計與研製＂，國立台灣大學，碩士論文，民
[18] M. Cheng, X. Gao, S.K. Gao, and D.F. Xu, “Design and Implementation of a Brain-Computer Interface With High Transfer Rates,” IEEE Transaction biomedical Engineering, vol. 49, pp. 1181-1186, 2002.
[19] X.R, Gao, D.F. Xu, M. Cheng, and S.K. Gao, “A BCI-Based Environmental Controller for the Motion-Disabled,” IEEE Transaction Neural Systems and Rehabilitation Engineering, vol. 11, pp. 137-140, June 2003.
[20] B. Obermaier, “Design and implementation of an EEG based virtual keyboard using hidden Markov models”, Ph.D. dissertation, Technical University-Graz, Graz, Austria, 2001.
[21] B. Obermaier, C. Neuper, and G. Pfurtscheller, “Information transfer rate in a 5 classes brain-computer interface,” IEEE Transaction Rehabilitation Engineering, vol. 9, pp. 283-288, 2001.
[22] G. Edlinger, G. Krausz, F. Laundl, I. Niedermayer, C. Guger, “Architectures of Laboratory-PC and Mobile Pocket PC Brain-Computer Interfaces,” in Proceedings of the 2 International IEEE EMBS Conference on Neural Engineering, Arlington, Virginia, March 16 - 19, 2005.
[23] K. Ashwin Whitchurch, B. Han Ashok, R. Vinod Kumaar and K. Sarukesi, and K. Vijay Varadan, “Wireless system for long term EEG monitoring of Absence Epilepsy,” Biomedical Applications of Micro- and Nanoengineering, Proceedings of SPIE, Vol. 4937, pp. 343-349, November 2002.
[24] P. Comon, “Independent component analysis, a new concept?’’ Signal Process., vol. 36, no. 3, pp. 287–314, Apr. 1994
[25] T. P. Jung, S. Makeig, T. W. Lee, M. J. Mckeown, G. Brown, A. J. Bell, and T. J.
Dejnowski, “Independent Component Analysis of Biomedical Signals,” in Proceedings of the 2nd International Workshop on Independent Component Analysis and Blind Signal Separation, pp. 633-44, 2000
[26] R. Vigário, J. Särelä, V. Jousmäki, M. Hämäläinen, and E. Oja, “Independent Component Approach to the Analysis of EEG and MEG Recordings,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 5, pp. 589-593, May 2000 [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 recordings,” Advances in Neural Information Process. Syst., vol. 10, pp. 894–900, 1998.
[28] N.J. Hill, T.N. Lal, K. Bierig, N. Birbaumer, and B. Scholkopf, “ Attentional modulation of auditory event-related potentials in a brain-computer interface,’’
in Proc. IEEE Int. Workshop Biomedical Circuits and Systems, Singapore, 2004, pp.17–20.
[29] B. Kamousi, Z. Liu, and B. He, “Classification of Motor Imagery Tasks for Brain-Computer Interface Applications by Means of Two Equivalent Dipoles Analysis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, No. 2, pp. 166-171June 2005.
[30] C. J. James and S. Wang, “Blind source separation in single-channel EEG analysis: An application to BCI,” in Proc. 28th Annu. Int.Conf. IEEE Engineering in Medicine and Biology Society, New York, USA, pp. 6544-6547, Aug. 2006.
[31] UW Computing & Communications
URL: http://faculty.washington.edu/chudler/1020.html
[32] A. Papoulis, “Minimum bias windows for high resolution spectral estimation,”
IEEE Trans. Info. Theory, vol. IT-19, pp. 9–12, 1973.
[33] M. Steriade, “Central core modulation of spontaneous oscillations and sensory transmission in thalamocortical systems,” Current Opin. Neurobiol., vol. 3, no. 4, pp. 619–625, 1993.
[34] A. Destexhe, D. Contreras, and M. Steriade, “Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex duringnatural wake and sleep states,” J. Neurosci., vol. 19, pp. 4595–4608,1999.
[35] C. T. Lin, R. C.Wu, T. P. Jung, S. F. Liang, and T. Y. Huang, “Estimating driving performance based on EEG spectrum analysis,” EURASIP J. Applied Signal Process., Vol. 2005, No. 19, pp. 3161-3174, Mar. 2005.
[36] Altera Inc. ,URL: http://www.altera.com/
[37] CSR Inc. ,URL: http://www.csrsupport.com/BC4Ext [38] Analog Devices Inc. ,URL:http://www.analog.com/
[39] B. Ans, J. Herautl, and C. Jutten, “Adaptative neural architectures: Detection of primitives,’’ in COGNITIVA’85, Paris, France, June 1985, pp. 593–597.
[40] A. J. Bell and T. J. Sejnowski, “An Information-Maximization Approach to Blind Separation and Blind Deconvolution,” Neural computation, pp. 1129-1159,1995 [41] M. Falkenstein, J. Hohnsbein, J. Hoormann, and L. Blanke, “Effects of
crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks,” Electroencephalography and Clinical Neurophysiology, vol. 78, pp. 447-55, 1991.
[42] S. Makeig, M. Westerfield, T.P. Jung, S. Enghoff, J. Townsend, E. Courchesne, and T. J. Sejnowski, “Electroencephalographic Sources of Visual Evoked Responses,” Science, vol. 295, pp. 690-94, Jan. 25,2002.
[43] C. T. Lin, R. C. Wu, S. F. Liang, W. H. Chao, Y. J. Chen, and T. P. Jung,
“EEG-based drowsiness estimation for safety driving using independent component analysis,” IEEE Trans on Circuits and Systems I, vol. 52, no. 12, pp.
2726-2738, 2005.
[44] T. W. Lee, M. Girolami, and T. J. Sejnowski, “Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources,” Neural Computation, vol. 11, pp. 606-633, 1999.
[45] S. Makeig, T.P. Jung, and T.J. Sejnowski, “Awareness during drowsiness:
dynamics and electrophysiological correlates,” Canadian Journal of Experimental Psychology, vol. 54, pp.266-273, 2000
[46] S. Makeig and T.P. Jung, “Tonic, phasic, and transient EEG correlates of auditory awareness in drowsiness,” Cognitive Brain Research, vol. 4, pp. 15-25, 1996.
[47] M. A, Schier, “Changes in EEG alpha power during simulated driving: a demonstration,” International Journal of Psychophysiology, vol. 37, pp.155-162, 2000.
[48] W. Wang and C. James, “Enhancing evoked responses for BCI through advanced ICA techniques,’’ in Proc. 3rd International Conf. Advances in Medical, Signal, and Information Processing, Glasgow, UK, pp. 38-41, July 2006.
[49] S. L. Joutsiniemi, S. Kaski, and T.AndreoLarsen, “Self-organizing map in recignition of topographic patterns of EEG spectra,” IEEE Transactions on Biomedical Engineering, vol. 42, no. 11, 1995.
[50] I. Obeid, M. A.L. Nicolelis, and P. D. Wolf, “A multichannel telemetry system for single unit neural recordings,” Journal of Neuroscience Methods, vol. 133, issues 1-2, pp. 33-38, Feb 2004.
[51] I. Obeid, M. A.L. Nicolelis, and P. D. Wolf, “ A low power multichannel analog front end for portable neural signal recordings.” Journal of Neuroscience Methods, vol. 133, issues 1-2, pp. 27-32, Feb 2004.
[52] 黃冠智，”即時 DSP 嵌入式 Linux 系統開發及其於腦波訊號處理之應用” ， 國立交通大學，碩士論文，民國九十三年。
[53] 謝弘義，”即時 DSP 嵌入式 Linux 系統開發及其於腦波訊號處理之應用” ， 國立交通大學，碩士論文，民國九十四年。