All Title Author
Keywords Abstract

Common Spatio-Time-Frequency Patterns for Motor Imagery-Based Brain Machine Interfaces

DOI: 10.1155/2013/537218

Full-Text   Cite this paper   Add to My Lib


For efficient decoding of brain activities in analyzing brain function with an application to brain machine interfacing (BMI), we address a problem of how to determine spatial weights (spatial patterns), bandpass filters (frequency patterns), and time windows (time patterns) by utilizing electroencephalogram (EEG) recordings. To find these parameters, we develop a data-driven criterion that is a natural extension of the so-called common spatial patterns (CSP) that are known to be effective features in BMI. We show that the proposed criterion can be optimized by an alternating procedure to achieve fast convergence. Experiments demonstrate that the proposed method can effectively extract discriminative features for a motor imagery-based BMI. 1. Introduction Brain machine computer interfacing (BMI BCI) is a challenging technology of signal processing, machine learning, and neuroscience [1]. BMIs capture brain activities associated with mental tasks and external stimuli, realize nonmuscular communication, and control channel for conveying messages and commands to the external world [1–3]. Basically, noninvasively measured data such as electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) are widely used to observe brain activities. Among them, because of its simplicity and low cost, EEG is practical for use in engineering applications [4, 5]. Efficient decoding around motor-cortex is a crucial technique for the realization of BMI associated with motor-imagery (MI-BMI) [6, 7] with the application to controlling external devices [7], prostheses [4], rehabilitation [8], and so forth. For instance, it is also known that the real and imaginary movements of hands and feet evoke the change of the so-called mu rhythm in different brain regions [2, 3]. Therefore, the accurate extraction of these changes from the measured EEG signals in the presence of measurement noise and spontaneous components which are related to other brain activities enables us to classify the EEG signal associated with the different motor (imagined) actions such as movement of the right hand, left hand, or feet. In classification of EEG signals in MI-BMI and analyzing of the brain activities during motor imagery, signal processing techniques such as bandpass filtering and spatial weighting are used [1]. For the processing, presuming the parameters such as coefficients of the filters and weights that extract the related components is a crucial issue. Moreover, the optimum parameters in classification are highly dependent on users and


[1]  G. Dornhege, J. R. Millan, T. Hinterberger, D. McFarland, and K.-R. Muller, Eds., Toward Brain-Computer Interfacing, The MIT Press, Cambridge, Mass, USA, 2007.
[2]  S. Sanei and J. Chambers, EEG Signal Processing, Wiley-Interscience, New York, NY, USA, 2007.
[3]  J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002.
[4]  D. J. McFarland and J. R. Wolpaw, “Brain-computer interface operation of robotic and prosthetic devices,” Computer, vol. 41, no. 10, pp. 52–56, 2008.
[5]  C. Zhang, Y. Kimura, H. Higashi, and T. Tanaka, “A simple platform of brain-controlled mobile robot and its implementation by SSVEP,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '12), pp. 1–7, 2012.
[6]  D. J. McFarland and J. R. Wolpaw, “Brain-computer interfaces for communication and control,” Communications of the ACM, vol. 54, no. 5, pp. 60–66, 2011.
[7]  J. R. Wolpaw and D. J. McFarland, “Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 51, pp. 17849–17854, 2004.
[8]  J. J. Daly and J. R. Wolpaw, “Brain-computer interfaces in neurological rehabilitation,” The Lancet Neurology, vol. 7, no. 11, pp. 1032–1043, 2008.
[9]  J. Müller-Gerking, G. Pfurtscheller, and H. Flyvbjerg, “Designing optimal spatial filters for single-trial EEG classification in a movement task,” Clinical Neurophysiology, vol. 110, no. 5, pp. 787–798, 1999.
[10]  H. Ramoser, J. Müller-Gerking, and G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 4, pp. 441–446, 2000.
[11]  S. Lemm, B. Blankertz, G. Curio, and K.-R. Müller, “Spatio-spectral filters for improving the classification of single trial EEG,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 9, pp. 1541–1548, 2005.
[12]  G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, and K.-R. Müller, “Combined optimization of spatial and temporal filters for improving brain-computer interfacing,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 11, pp. 2274–2281, 2006.
[13]  R. Tomioka, G. Dornhege, G. Nolte, B. Blankertz, K. Aihara, and K. R. M. Müller, “Spectrally weighted common spatial pattern algorithm for single trial eeg classification,” Tech. Rep. 40, Department of Mathematical Engineering, University of Tokyo, Tokyo, Japan, 2006.
[14]  H. Higashi and T. Tanaka, “Classification by weighting for spatio-frequency components of EEG signal during motor imagery,” in Proceedings of the 36th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '11), pp. 585–588, May 2011.
[15]  H. Higashi and T. Tanaka, “Simultaneous design of FIR filter banks and spatial patterns for EEG signal classification,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 4, pp. 1100–1110, 2013.
[16]  N. Tomida, H. Higashi, and T. Tanaka, “A joint tensor diagonalization approach to active data selection for EEG classification,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '13), pp. 983–987, 2013.
[17]  K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, “Filter Bank Common Spatial Pattern (FBCSP) in brain-computer interface,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '08), pp. 2390–2397, June 2008.
[18]  K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, “Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs,” Pattern Recognition, vol. 45, no. 6, pp. 2137–2144, 2012.
[19]  H. Lu, H.-L. Eng, C. Guan, K. N. Plataniotis, and A. N. Venetsanopoulos, “Regularized common spatial pattern with aggregation for EEG classification in small-sample setting,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 12, pp. 2936–2946, 2010.
[20]  F. Lotte and C. Guan, “Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 2, pp. 355–362, 2011.
[21]  G. Der and I. J. Deary, “Age and sex differences in reaction time in adulthood: results from the United Kingdom health and lifestyle survey,” Psychology and Aging, vol. 21, no. 1, pp. 62–73, 2006.
[22]  B. Blankertz, K.-R. Müller, D. J. Krusienski et al., “The BCI competition III: validating alternative approaches to actual BCI problems,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 153–159, 2006.
[23]  G. Dornhege, B. Blankertz, G. Curio, and K.-R. Müller, “Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993–1002, 2004.
[24]  B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Müller, and G. Curio, “The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects,” NeuroImage, vol. 37, no. 2, pp. 539–550, 2007.
[25]  S. Varma and R. Simon, “Bias in error estimation when using cross-validation for model selection,” BMC Bioinformatics, vol. 7, no. 1, article 91, 2006.
[26]  J. D. R. Millán and J. Mouri?o, “Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 159–161, 2003.


comments powered by Disqus