Categorical-Data-Based BCI with Motor Imagery Using Equivalent Current Dipole Source Localization 

Authors

  • Toshimasa Yamazaki Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
  • Kazufumi Tanaka CAROLsystem Ltd., Tokyo 150-0036, Japan
  • Takahiro Shibata Olympus Software Technology Corporation, Tokyo 163-1414, Japan
  • Hiromi Yamaguchi Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
  • Minami Ouda Software Vision Company, Kumamoto 862-0926, Japan
  • Yukari Sasaguri Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
  • Shun Hirose
  • Hiroshi Takayanagi Future University Hakodate, Tokyo 101-0021, Japan
  • Hideyuki Maki Japan Technical Software Co., Ltd, Sapporo 001-0021, Japan
  • Takahiro Yamanoi Hokkai Gakuen University, Sapporo 064-0926
  • Ken-Ichi Kamijo NEC Corporation, Tokyo 108-8558, Japan

DOI:

https://doi.org/10.12970/2308-8354.2014.02.01

Keywords:

 Hepatitis C virus (HCV), Incidental detection, HCV RNA, HCV genotypes, Risk factors, Southern India.

Abstract

 Purpose: In order to enable data reduction in single-trial-electroencephalograms (EEGs)-based Brain-Computer Interfaces (BCIs), a new algorithm based on categorical data is proposed. Method: The EEGs were categorized by independent component analysis (ICA) and equivalent current dipole source localization (ECDL), and Hayashi’s second method of quantification (H2MQ) was applied to the categorical data. Ten healthy subjects performed left- and right-hand movement imagery tasks, while EEGs were recorded from 32 electrodes on the scalp. Using the categorical data with respect to the brain sites where dipoles were located by ECDL after ICA, a learning model for the discrimination between the left- and right-hand imageries was conducted by H2MQ. Results: Using 16 ICs, all the H2MQ models had the correlation ratios of more than 0.90, where the numbers of trials and categories were 60 and 4, respectively. The accuracy averages across all the subjects for the left- and right-hand imageries in each 10-trial validation phase were 94.5 % and 91.5 %, respectively, which was better than the previous common-spatial-pattern (CSP)-based BCIs. Conclusion: The above results led us to a new paradigm for single-trial-EEG-based BCIs, quite different from continuous-valued EEGs and their spectral analysis, yielding the data reduction. Keywords: Brain-Computer Interface, independent component analysis, equivalent current dipole source localization, movement imagery.

References


[1] Wolpaw J, Birbaumer N, McFarland D, Pfurtscheller G, Vaughhan T. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002; 113: 767-91. http://dx.doi.org/10.1016/S1388-2457(02)00057-3
[2] Townsend G, Graimann B, Pfurtscheller G. Continuous EEG classification during motor imagery-Simulation of an asynchronous BCI. IEEE Trans Neural Syst Rehabil Eng 2004; 12: 258-65. http://dx.doi.org/10.1109/TNSRE.2004.827220
[3] Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999; 110: 1842-57. http://dx.doi.org/10.1016/S1388-2457(99)00141-8
[4] Miller KJ, Schalk G, Fetz EE, den Nijs M, Ojemann JG, Rao RPN. Cortical activity during motor execution, motor imagery, and imagery-based online feedback. PNAS 2010; 107: 4430- 35. http://dx.doi.org/10.1073/pnas.0913697107
[5] Leocani L, Toro C, Manganotti P, Zhuang P, Hallett M. Event-related coherence and event-related desynchronization/synchronization in the 10 Hz and 20 Hz EEG during self-paced movements. Electroenceph Clin Neurophysiol 1997; 104: 199-206. http://dx.doi.org/10.1016/S0168-5597(96)96051-7
[6] Pfurtscheller G, Stancák Jr A, Edlinger G. On the existence of different types of central beta rhythms below 30 Hz. Electroenceph Clin Neurophysiol 1997; 102: 316-25. http://dx.doi.org/10.1016/S0013-4694(96)96612-2
[7] Zhou J, Yao J, Deng J, Dewald JPA. EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects. Comput Biol Med 2009; 39: 443- 52. http://dx.doi.org/10.1016/j.compbiomed.2009.02.004
[8] Wang Y, Wang Y-T, Jung T-P. Translation of EEG spatial filters from resting to motor imagery using independent component analysis. PLoS ONE 2012; 7: e37665. http://dx.doi.org/10.1371/journal.pone.0037665
[9] Hsu W-Y. EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features. J Neurosci Methods 2010; 189: 295-302. http://dx.doi.org/10.1016/j.jneumeth.2010.03.030
[10] Krusienski DJ, Sellers EW, McFarland DJ, Vaughan TM, Wolpaw JR. Toward enhanced P300 speller performance. J Neurosci Methods 2008; 167: 15-21. http://dx.doi.org/10.1016/j.jneumeth.2007.07.017
[11] Rakotomamonjy A, Guigue V. BCI competition III: dataset IIensemble of SVMs for BCI p300 speller. IEEE Trans Biomed Eng 2008; 55: 1147-54. http://dx.doi.org/10.1109/TBME.2008.915728
[12] Kamrunnahar M, Dias NS, Schiff SJ. Optimization of electrode channels in brain computer interfaces. Conf Proc IEEE Eng Med Biol Soc 2009; pp. 6477-80.
[13] Sannelli C, Dickhaus T, Halder S, Hammer E-M, Müller K-R, Blankertz B. On optimal channel configurations for SMRbased brain-computer interfaces. Brain Topogr 2010; 23: 186-93. http://dx.doi.org/10.1007/s10548-010-0135-0
[14] Arvaneh M, Guan CT, Ang KK, Quek C. Optimizing the channel selection and classification accuracy in EEG-based BCI source. IEEE Trans Biomed Eng 2011; 58: 1865-73. http://dx.doi.org/10.1109/TBME.2011.2131142
[15] Qin L, Ding L, He B. Motor imagery classification by means of source analysis for brain computer interface applications. J Neural Eng 2004; 1: 135-41. http://dx.doi.org/10.1088/1741-2560/1/3/002
[16] Kamousi B, Liu Z, He B. Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis. IEEE Trans Neural Syst Rehabil Eng 2005; 13: 166-71. http://dx.doi.org/10.1109/TNSRE.2005.847386
[17] Congedo M, Lotte F, Lécuyer A. Classification of movement intention by spatially filtered electromagnetic inverse solutions. Phys Med Biol 2006; 51: 1971-89. http://dx.doi.org/10.1088/0031-9155/51/8/002
[18] Noirhomme Q, Kitney RL, Macq B. Single-trial EEG source reconstruction for brain-computer interface. IEEE Trans Biomed Eng 2008; 55: 1592-601. http://dx.doi.org/10.1109/TBME.2007.913986
[19] Makeig S, Bell AJ, Jung TP, Sejnowski TJ. Independent component analysis of electroencephalographic data. In Proceedings of the Advances in Neural Information Processing Systems 8 (NIPS 1995) (Touretzky DS, Mozer MC and Hasselmo ME, Ed.) 1995.
[20] Delorme A, Sejnowski TJ, Maikeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 2007; 34: 1443-9. http://dx.doi.org/10.1016/j.neuroimage.2006.11.004
[21] Hoffman S, Falkenstein M. The correction of eye blink artefacts in the EEG: a comparison of two prominent methods. PLoS ONE 2008; 3: e3004.
[22] Xu N, Gao X, Hong B, Miao X, Gao S, Yang F. BCI competition 2003-Data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications. IEEE Trans Biomed Eng 2004; 51: 1067-72. http://dx.doi.org/10.1109/TBME.2004.826699
[23] Wang D, Miao D, Blohm G. Multi-class motor imagery EEG decoding for brain-computer interfaces. Front Neurosci 2012; 6(151): 1-13.
[24] Kamijo K, Yamazaki T, Kiyuna T, Takaki Y, Kuriowa Y. Visual event-related potentials during movement imagery and the dipole analysis. Brain Topogr 2002; 14: 279-92. http://dx.doi.org/10.1023/A:1015733127075
[25] Kamijo K, Kawashima R, Yamazaki T, Kiyuna T, Takaki Y. An event-related functional magnetic resonance imaging study of movement imagery. Trans Japanese Soc Med Biol Eng 2004; 42: 16-21.
[26] Hayashi C. On the prediction of phenomena from qualitative data and the quantification of qualitative data from the mathematico-statistical point of view. Ann Inst Statist Math 1952; 3: 69-98. http://dx.doi.org/10.1007/BF02949778
[27] Tanaka Y. Review of the methods of quantification. Environmental Health Perspectives 1979; 32: 113-23. http://dx.doi.org/10.1289/ehp.7932113
[28] Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 1971; 9: 97-113. http://dx.doi.org/10.1016/0028-3932(71)90067-4
[29] Soufflet L, Toussaint M, Luthringer R, Gressor J, Minot R, Macher JP. A statistical evaluation of the main interpolation methods applied to 3-dimensional EEG mapping. Electroencephalogr Clin Neurophysiol 1991; 79: 393-402. http://dx.doi.org/10.1016/0013-4694(91)90204-H
[30] Shibasaki H, Hallett M. What is the Bereitschaftspotential? Clin Neurophysiol 2006; 117: 2341-56. http://dx.doi.org/10.1016/j.clinph.2006.04.025
[31] Yamazaki T, Kamijo K, Kenmochi A, et al. Multiple equivalent current dipole source localization of visual event-related potentials during oddball paradigm with motor response. Brain Topogr 2000; 12: 159-75. http://dx.doi.org/10.1023/A:1023467806268
[32] Hyvärinen A, Oja E. A fast fixed-point algorithm for independent component analysis. Neural Comput 1997; 9: 1483-92. http://dx.doi.org/10.1162/neco.1997.9.7.1483
[33] Brain Science Institute RIKEN. ICALAB version 3 toolbox. Cichocki A, Amari S, Siwek K, et al. http://www.bsp.brain. riken.jp/ICALAB/. (accessed May 19, 2014).
[34] Delorme A, Palmer J, Onton J, Oostenveld R, Makeig S. Independent EEG sources are dipolar. PLoS ONE 2012; 7(2): e30135. http://dx.doi.org/10.1371/journal.pone.0030135
[35] Mosher JC, Lewis PS, Leahy RM. Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Trans Biomed Eng 1992; 39: 541-57. http://dx.doi.org/10.1109/10.141192
[36] Kamijo K, Kiyuna T, Takaki Y, Kenmochi A, Tanigawa T, Yamazaki T. Integrated approach of an artificial neural network and numerical analysis to multiple equivalent current dipole source localization. Frontiers Med Biol Eng 2001; 10: 285-301. http://dx.doi.org/10.1163/156855700750265468
[37] Porro CA, Francescato MP, Cettolo V, et al. Primary motor and sensory cortex activation during motor performance and motor imagery: A functional magnetic resonance imaging study. J Neurosci 1996; 16: 7688-98.
[38] Deiber MP, Ibanez V, Honda M, Sadato N, Raman R, Hallett M. Cerebral processes related to visuomotor imagery and generation of simple finger movements studied with positron emission tomography. NeuroImage 1998; 7: 73-85. http://dx.doi.org/10.1006/nimg.1997.0314
[39] Gerardin E, Sirigu A, Lehéricy S, et al. Partially overlapping neural networks for real and imagined hand movements. Cereb Cortex 2000; 10: 1093-104. http://dx.doi.org/10.1093/cercor/10.11.1093
[40] Hanakawa T, Immisch I, Toma K, Dimyan MA, Van Gelderen P, Hallett M. Functional properties of brain areas associated with motor execution and imagery. J Neurophysiol 2003; 89: 989-1002. http://dx.doi.org/10.1152/jn.00132.2002
[41] Ehrsson HH, Geyer S, Naito E. Imagery of voluntary movement of fingers, toes, and tongue activates corresponding body-part-specific motor representations. J Neurophysiol 2003; 90: 3304-16. http://dx.doi.org/10.1152/jn.01113.2002
[42] Yuan H, Liu T, Szarkowski R, Rios C, Ashe J, He B. Negative covariation between task-related responses in alpha/betaband activity and BOLD in human sensorimotor cortex: an EEG and fMRI study of motor imagery and movement. Neuroimage 2010; 49: 2596-606. http://dx.doi.org/10.1016/j.neuroimage.2009.10.028
[43] Visani E, Minai L, Canafoglia L, et al. Abnormal ERD/ERS but unaffected BOLD response in patients with UnverrichtLundborg disease during index extension: A simultaneous EEG-fMRI study. Brain Topogr 2011; 24: 65-77. http://dx.doi.org/10.1007/s10548-010-0167-5
[44] Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller K-R. IEEE Signal Proc Magazine 2008; 25: 41-56. http://dx.doi.org/10.1109/MSP.2008.4408441
[45] Bai O, Maria Z, Vorbacha S, Hallett M. Asymmetric spatiotemporal patterns of event-related desynchronization preceding voluntary sequential finger movements: A high resolution EEG study. Clin Neurophysiol 2005; 116: 1213-21. http://dx.doi.org/10.1016/j.clinph.2005.01.006

Downloads

Published

2014-04-05

Issue

Vol. 2 No. 1 (2014)

Section

Articles