Abstract
This work presents a study that evaluates different scenarios of preprocessing and processing of EEG registers, with the aim to predict fist imaginary movements utilizing the data of the EEG Motor Movement/Imaginary Dataset. Three types of imaginary fist movements have been decoded: sustained opening and closing of right fist, sustained opening and closing of left fist and rest. Initially, the registers were band-pass filtered to separate frequency ranges given by mu rhythms (7.5-12.5 Hz), beta rhythms (12.5-30 Hz), mu&beta rhythms, and a broad range of 0.5-30 Hz. Afterward, the signals of the separated subbands were epoched in time windows of 0-0.5, 0-1, 0-1.5 and 0-2 seconds, as well as preprocessed with two techniques of spatial filtering: common spatial patterns and independent component analysis. In both cases, a set of selected channels was established for feature extraction, by calculation of the logarithms of the variance in the time series corresponding to each preprocessed and selected channel. The classification stage was based on linear discriminant analysis and support vector machines. The results showed that the combination given by common spatial patterns and support vector machines allowed to reach a mean decoding accuracy close to 99.9%, where epoching and filtering to separate subbands did not influence the results in a noticeable way.
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References
Sanei, S.: Adaptive Processing of Brain Signals, 1st edn., pp. 295–324. John Wiley & Sons (2013)
Hurtado-Rincón, J., Rojas-Jaramillo, S., Ricardo-Céspedes, Y., Alvarez-Meza, A.M., Castellanos-Domínguez, G.: Motor Imagery Classification using Feature Relevance Analysis: An Emotiv-based BCI System. In: XIX Symposium on Image, Signal Processing and Artificial Vision (STSIVA 2014), September 17-19 (2014)
Krusienski, D.J., McFarland, D.J., Wolpaw, J.R.: Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based braincomputer interface. Brain Research Bulletin 87, 130–134 (2012)
Bai, O., Lin, P., Vorbach, S., Li, J., Furlani, S., Hallett, M.: Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG. Clin. Neurophysiol. 118, 2637–2655 (2007)
Rejer, I.: EEG feature selection for BCI based on motor imaginary task. Foundations of Computing and Decision Sciences 37, 283–292 (2012)
Liu, G., Huang, G., Meng, J., Zhu, X.: A frequency-weighted method combined with Common Spatial Patterns for electroencephalogram classification in braincomputer interface. Biomedical Signal Processing and Control 5, 174–180 (2010)
Mousavi, E.A., Maller, J.J., Fitzgerald, P.B., Lithgow, B.J.: Wavelet Common Spatial Pattern in asynchronous offline brain computer interfaces. Biomedical Signal Processing and Control 6, 121–128 (2011)
Asensio-Cubero, J., Gan, J.Q., Palaniappan, R.: Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing. Biomedical Signal Processing and Control 8, 772–778 (2013)
Velásquez-Martínez, L.F., Álvarez-Meza, A.M., Castellanos-Domínguez, C.G.: Motor Imagery Classification for BCI Using Common Spatial Patterns and Feature Relevance Analysis. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F. J. (eds.) IWINAC 2013, Part II. LNCS, vol. 7931, pp. 365–374. Springer, Heidelberg (2013)
Kanoh, S., Miyamoto, K., Yoshinobu, T.: Generation of Spatial Filters by ICA for Detecting Motor-related Oscillatory EEG. In: 34th Annual International Conference of the IEEE EMBS, San Diego, California USA, August 28- September 1, pp. 1703–1706 (2012)
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)
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004)
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 20, e215–e220 (2000)
Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9–21 (2004)
Alomari, M.H., Samaha, A., AlKamha, K.: Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning (IJACSA) International Journal of Advanced Computer Science and Applications 4, 207–212 (2013)
Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110, 1842–1857 (1999)
Jeona, Y., Namb, C.S., Kimc, Y.J., Whangd, M.C.: Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for braincomputer interfaces. International Journal of Industrial Ergonomics 41, 428–436 (2011)
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Mueller, K.R.: Optimizing Spatial Filters for Robust EEG Single-Trial Analysis. IEEE Signal Processing Magazine 25, 41–56 (2008)
Khan, Y.U., Sepulveda, F.: Brain-computer interface for single-trial eeg classification for wrist movement imagery using spatial filtering in the gamma band. IET Signal Process. 4, 510–517 (2010)
Mueller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H.: Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin. Neurophysiol. 110, 787–798 (1999)
Liao, K., Xiao, R., Gonzalez, J., Ding, L.: Decoding Individual Finger Movements from One Hand Using Human EEG Signals. PLOS ONE 9, e85192, 1–12 (2014)
Huang, D., Lin, P., Fei, D.Y., Chen, X., Bai, O.: Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control. J. Neural. Eng. 6 (2009)
Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)
Girden, E.R.: ANOVA: Repeated Measures, vol. 84. SAGE Publications (1992)
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Boelts, J., Cerquera, A., Ruiz-Olaya, A.F. (2015). Decoding of Imaginary Motor Movements of Fists Applying Spatial Filtering in a BCI Simulated Application. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_16
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DOI: https://doi.org/10.1007/978-3-319-18914-7_16
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