Abstract
In this paper, the categorization of single-trial EEG data recorded during the MI-related task, as another data reduction, will be attempted, because the categorical data would require less storage and computational time than continuous one. The categorization will be realized by equivalent current dipole source localization (ECDL). To analyze this, we used EEG data and visually evoked related potentials (v-ERP) led by 32 electrodes. From the result of single-trial v-ERP, only 6 electrode v-ERPs have a remarkable reaction. Therefore, from the view point of business, it is found that the minimum number of electrodes have been seven.
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Wolpow, J., Birbaumer, N., McFarland, D., Pfurtscheller, G., Vaughan, T.: Brain-Computer Interfaces for communication and control. Clinical Neurophysiology 113, 767–791 (2002)
Townsend, G., Graimann, B., Pfurtscheller, G.: Continuous EEG classification during motor imagery-Simulation of an asynchronous BCI. IEEE Transaction of Neural System and Rehabilitation Engineering 12, 258–265 (2004)
Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology 110(11), 1842–1857 (1999)
Miller, K.J., Schalk, G., Fetz, E.E., den Nijs, M., Ojemann, J.G., Rao, R.P.N.: Cortical activity during motor execution, motor imagery, and imagery-based online feedback. PNAS 107, 4430–4435 (2010)
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. Electroencephalography and Clinical Neurophysiology / Evoked Potentials Section 104(3), 199–206 (1997)
Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology 103(6), 642–651 (1997)
Zhou, J., Yao, J., Deng, J., Dewald, J.P.A.: EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects. Computers in Biology and Medicine, 443–452 (2009)
Wang, D., Miao, D., Blohm, G.: Multi-class motor imagery EEG decoding for brain-computer interfaces. Frontier in Neuroscience 6, article 151, 1–13 (2012)
Hsu, W.Y.: EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features. Journal of Neuroscience and Methods 189, 295–302 (2010)
Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: Toward enhanced P300 speller performance. Journal of Neuroscience Methods 167, 15–21 (2008)
Sakamoto, Y., Aono, M.: Supervised Adaptive Downsampling for P300-Based Brain-Computer Interface. In: 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009 (2009)
Arvaneh, M., Guan, C.T., Ang, K.K., Quek, C.: Optimizing the channel selection and classification accuracy in EEG-based BCI source. IEEE Transaction of Biomedical Engineering 58, 1865–1873 (2011)
Kamrunnahar, M., Dias, N.S., Schiff, S.J.: Optimization of Electrode Channels in Brain Computer Interfaces. In: Conference Proceedings of IEEE Engineering and Medical Biological Society, pp. 6477–6480 (2009)
Sannelli, C., Dickhausa, T., Halderc, S., Hammerc, E., Mullera, K., Blankertz, B.: On optimal channel configurations for SMR based braincomputer interfaces. Brain Topography, 186–193 (2010)
Qin, L., Ding, L., He, B.: Motor Imagery Classification by Means of Source Analysis for Brain Computer Interface Applications. Journal of Neural Engineering, 135–141 (2004)
Kamousi, B., Liu, Z., He, B.: Classificationi of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis. IEEE Transaction of Neural System and Rehabilitation Engineering 13(2), 166–171 (2005)
Congedo, M., Lotte, F., Lecuyer, A.: Classification of movement intention by spatially filtered electromagnetic inverse solution. Physics in Medicine and Biology 51, 1971–1989 (2006)
Noirhomme, Q., Kitney, R.L., Macq, B.: Single-trial EEG source reconstruction for brain-computer interface. IEEE Transaction of Biomedical Engineering 55, 1592–1601 (2008)
Makeig, S., Bell, A.J., Jung, T.P., Sejnowski, T.J.: Independent component analysis of electroencephalographic data. Advances in Neural Information Processing Systems, 145–151 (1996)
Delorme, A., Sejnowski, T.J., Maikeig, S.: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 34, 1443–1449 (2007)
Hoffman, S., Falkenstein, M.: The correction of eye blink artefacts in the EEG: a comparison of two prominent methods. PLoS One 3 (2008)
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 Transaction of Biometdical Engieeing 51, 1067–1072 (2004)
Wang, Y., Wang, Y.T., Jung, T.P.: Translation of EEG spatial filters from resting to motor imagery using independent component analysis. PLoS One 7, e37665 (2012)
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. Frontier Medical and Biological Engineering 10(4), 285–301 (2001)
Kamijo, K., Kawashima, R., Yamazaki, T., Kiyuna, T., Takaki, Y.: An event-related functional magnetic resonance imaging study of movement imagery. Transaction of the Japanese Society for Medical and Biological Engineering 42, 16–21 (2004)
Oldfield, R.C.: The assessment and analysis of handedness: the Edinburgh Inventory. Neuropsychologia 9, 97–113 (1971)
Soufflet, L., Toussaint, M., Luthringer, R., Gresser, J., Minot, R., Macher, J.P.: A statistical evaluation of the main interpolation methods applied to 3-dimensional EEG mapping. Electroenceph. Clin. Neurophysiol. 79, 393–402 (1991)
Kamijo, K., Yamazaki, T., Kiyuna, T., Takaki, Y., Kuroiwa, Y.: Brain Topgraphy 14(4), 279–292 (2002)
Yamamoto, K., Yamazaki, T., Kamijo, K., Yamanoi, T., Fukuzumi, S.: Ailent speech BCI: Learning and decoding algorithms using single-trial EEGs and speech signals. In: Proceedings of BPES 2011 (2011)
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Fukuzumi, S., Yamaguchi, H., Tanaka, K., Yamazaki, T., Yamanoi, T., Kamijo, Ki. (2014). A New Computational Method for Single-Trial-EEG-Based BCI. In: Yamamoto, S. (eds) Human Interface and the Management of Information. Information and Knowledge Design and Evaluation. HIMI 2014. Lecture Notes in Computer Science, vol 8521. Springer, Cham. https://doi.org/10.1007/978-3-319-07731-4_15
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DOI: https://doi.org/10.1007/978-3-319-07731-4_15
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