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Open Access

Cross-Target Transfer Algorithm Based on the Volterra Model of SSVEP-BCI

Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China.
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Abstract

In general, a large amount of training data can effectively improve the classification performance of the Steady-State Visually Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system. However, it will prolong the training time and considerably restrict the practicality of the system. This study proposed a SSVEP nonlinear signal model based on the Volterra filter, which could reconstruct stable reference signals using relatively small number of training targets by transfer learning, thereby reducing the training cost of SSVEP-BCI. Moreover, this study designed a transfer-extended Canonical Correlation Analysis (t-eCCA) method based on the model to achieve cross-target transfer. As a result, in a single-target SSVEP experiment with 16 stimulus frequencies, t-eCCA obtained an average accuracy of 86.96% ±12.87% across 12 subjects using only half of the calibration time, which exhibited no significant difference from the representative training classification algorithms, namely, extended canonical correlation analysis (88.32% ±13.97%) and task-related component analysis (88.92% ±14.44%), and was significantly higher than that of the classic non-training algorithms, namely, Canonical Correlation Analysis (CCA) as well as filter-bank CCA. Results showed that the proposed cross-target transfer algorithm t-eCCA could fully utilize the information about the targets and its stimulus frequencies and effectively reduce the training time of SSVEP-BCI.

Keywords

Steady-State Visually Evoked Potential (SSVEP)Brain-Computer Interface (BCI)Volterra filtercross-target informationtransfer learning

References

[1]
S. K. Gao, Y. J. Wang, X. R. Gao, and B. Hong, Visual and auditory brain-computer interfaces, IEEE Transactions on Biomedical Engineering, vol. 61, no. 5, pp. 1436-1447, 2014.
Google Scholar
[2]
K. Lin, S. K. Gao, and X. R. Gao, Boosting the information transfer rate of an SSVEP-BCI system using maximal-phase-locking value and minimal-distance spatial filter banks, Tsinghua Science and Technology, vol. 24, no. 3, pp. 262-270, 2019.
Google Scholar
[3]
S. G. Zhang, X. Han, and X. R. Gao, Studying the effect of the pre-stimulation paradigm on steady-state visual evoked potentials with dynamic models based on the zero-pole analytical method, Tsinghua Science and Technology, vol. 25, no. 3, pp. 435-446, 2020.
Google Scholar
[4]
D. Regan, Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine. New York, NY, USA: Elsevier, 1989.
[5]
A. M. Norcia, L. G. Appelbaum, J. M. Ales, B. R. Cottereau, and B. Rossion, The steady-state visual evoked potential in vision research: A review, Journal of Vision, vol. 15, no. 6, p. 4, 2015.
Google Scholar
[6]
O. Friman, I. Volosyak, and A. Graeser, Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces, IEEE Transactions on Biomedical Engineering, vol. 54, no. 4, pp. 742-750, 2007.
Google Scholar
[7]
Y. S. Zhang, P. Xu, K. W. Cheng, and D. Z. Yao, Multivariate synchronization index for frequency recognition of SSVEP-based brain-computer interface, Journal of Neuroscience Methods, vol. 221, pp. 32-40, 2014.
Google Scholar
[8]
M. Nakanishi, Y. J. Wang, Y. T. Wang, and T. P. Jung, A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials, Plos One, vol. 10, no. 10, p. e0140703, 2015.
Google Scholar
[9]
G. Y. Bin, X. R. Gao, Z. Yan, B. Hong, and S. K. Gao, An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method, Journal of Neural Engineering, vol. 6, no. 4, p. 046002, 2009.
Google Scholar
[10]
X. G. Chen, Y. J. Wang, S. K. Gao, T. P. Jung, and X. R. Gao, Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface, Journal of Neural Engineering, vol. 12, no. 4, p. 046008, 2015.
Google Scholar
[11]
C. Yang, X. Han, Y. J. Wang, R. Saab, S. K. Gao, and X. R. Gao, A dynamic window recognition algorithm for SSVEP-based brain-computer interfaces using a spatio-temporal equalizer, International Journal of Neural Systems, vol. 28, no. 10, p. 1850028, 2018.
Google Scholar
[12]
X. G. Chen, Y. J. Wang, M. Nakanishi, X. R. Gao, T. P. Jung, and S. K. Gao, High-speed spelling with a noninvasive brain-computer interface, Proceedings of the National Academy of Sciences of the United States of America, vol. 112, no. 44, pp. 6058-6067, 2015.
Google Scholar
[13]
M. Nakanishi, Y. J. Wang, X. G. Chen, Y. T. Wang, X. R. Gao, and T. P. Jung, Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis, IEEE Transactions on Biomedical Engineering, vol. 65, no. 1, pp. 104-112, 2018.
Google Scholar
[14]
D. Regan, Some characteristics of average steady-state and transient responses evoked by modulated light, Electroencephalography and Clinical Neurophysiology, vol. 20, no. 3, pp. 238-248, 1966.
Google Scholar
[15]
C. O. Sakar, O. Kursun, and F. Gurgen, Ensemble canonical correlation analysis, Applied Intelligence, vol. 40, no. 2, pp. 291-304, 2014.
Google Scholar
[16]
M. Cheng and S. K. Gao, An EEG-based cursor control system, in Proceedings of the First Joint BMES/EMBS Conference, Atlanta, GA, USA, 1999, p. 669.
[17]
M. Nakanishi, Y. J. Wang, Y. T. Wang, Y. Mitsukura, and T. P. Jung, A high-speed brain speller using steady-state visual evoked potentials, International Journal of Neural Systems, vol. 24, no. 6, p. 1450019, 2014.
Google Scholar
[18]
M. Xu, J. Han, Y. Wang, T. Jung, and D. Ming, Implementing over 100 command codes for a high-speed hybrid brain-computer interface using concurrent P300 and SSVEP features, .
Crossref
[19]
T. Cao, F. Wan, C. M. Wong, J. N. Da Cruz, and Y. Hu, Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces, .
Crossref
[20]
P. Yuan, X. G. Chen, Y. J. Wang, X. R. Gao, and S. K. Gao, Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information, Journal of Neural Engineering, vol. 12, no. 4, p. 046006, 2015.
Google Scholar
[21]
N. R. Waytowich, J. Faller, J. Garcia, J. M. Vettel, and P. Sajda, Unsupervised adaptive transfer learning for steady-state visual evoked potential brain-computer interfaces, in Proceedings of 2016 IEEE International Conference on Systems, Man, and Cybernetics, Budapest, Hungary, 2016, pp. 4135-4140.
[22]
K. Suefuda and T. Tanaka, Reduced calibration by efficient transformation of templates for high speed hybrid coded SSVEP brain-computer interfaces, presented at the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017.
[23]
C. M. Wong, F. Wan, B. Y. Wang, Z. Wang, W. Y. Nan, K. F. Lao, P. U. Mak, M. I. Vai, and A. Rosa, Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs, Journal of Neural Engineering, vol. 17, no. 1, p. 016026, 2020.
Google Scholar
[24]
S. G. Zhang, X. Han, X. G. Chen, Y. J. Wang, S. K. Gao, and X. R. Gao, A study on dynamic model of steady-state visual evoked potentials, Journal of Neural Engineering, vol. 15, no. 4, p. 046010, 2018.
Google Scholar
[25]
X. Han, S. G. Zhang, and X. R. Gao, A study on reducing training time of BCI system based on an SSVEP dynamic model, presented at the 2019 7th International Winter Conference on brain-computer interface (BCI), Gangwon, South Korea, 2019.
[26]
J. Pan, X. R. Gao, F. Duan, Z. Yan, and S. K. Gao, Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis, Journal of Neural Engineering, vol. 8, no. 3, p. 036027, 2011.
Google Scholar
[27]
V. J. Mathews and G. L. Sicuranza, Polynomial Signal Processing. New York, NY, USA: Wiley, 2000.
[28]
J. J. Bussgang, L. Ehrman, and J. W. Graham, Analysis of nonlinear systems with multiple inputs, Proceeding of the IEEE, vol. 62, no. 8, pp. 1088-1119, 1974.
Google Scholar
[29]
R. Kus, A. Duszyk, P. Milanowski, M. Labecki, M. Bierzynska, Z. Radzikowska, M. Michalska, J. Zygierewicz, P. Suffczynski, and P. J. Durka, On the quantification of SSVEP frequency responses in human EEG in realistic BCI conditions, Plos One, vol. 8, no. 10, p. e77536, 2013.
Google Scholar
[30]
D. H. Brainard, The psychophysics toolbox, Spatial Vision, vol. 10, no. 4, pp. 433-436, 1997.
Google Scholar
[31]
G. G. Celesia, Steady-state and transient visual evoked-potentials in clinical-practice, Annals of the New York Academy of Sciences, vol. 388, pp. 290-305, 1982.
Google Scholar
[32]
O. Sharon and Y. Nir, Attenuated fast steady-state visual evoked potentials during human sleep, Cerebral Cortex, vol. 28, no. 4, pp. 1297-1311, 2018.
Google Scholar
[33]
G. R. Mueller-Putz, R. Scherer, C. Brauneis, and G. Pfurtscheller, Steady-State Visual Evoked Potential (SSVEP)-based communication: Impact of harmonic frequency components, Journal of Neural Engineering, vol. 2, no. 4, pp. 123-130, 2015.
Google Scholar
[34]
R. D. Nowak and B. D. Vanveen, Random and pseudorandom inputs for Volterra filter identification, IEEE Transactions on Signal Processing, vol. 42, no. 8, pp. 2124-2135, 1994.
Google Scholar
[35]
G. Birpoutsoukis and J. Schoukens, Nonparametric Volterra kernel estimation using regularization, in Proceedings of 2015 IEEE International Instrumentation and Measurement Technology Conference, Pisa, Italy, 2015, pp. 222-227.
[36]
Y. J. Wang, X. G. Chen, X. R. Gao, and S. K. Gao, A benchmark dataset for SSVEP-based brain-computer interfaces, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 10, pp. 1746-1752, 2017.
Google Scholar
[37]
F. D. Russo, S. Pitzalis, T. Aprile, G. Spitoni, F. Patria, A. Stella, D. Spinelli, and S. A. Hillyard, Spatiotemporal analysis of the cortical sources of the steady-state visual evoked potential, Human Brain Mapping, vol. 28, no. 4, pp. 323-334, 2007.
Google Scholar
[38]
J. J. Chen, D. Zhang, A. K. Engel, Q. Gong, and A. Maye, Application of a single-flicker online SSVEP BCI for spatial navigation, Plos One, vol. 12, no. 5, p. e0178385, 2017.
Google Scholar
[39]
J. J. Chen, A. Maye, A. K. Engel, Y. J. Wang, X. R. Gao, and D. Zhang, Simultaneous decoding of eccentricity and direction information for a single-flicker SSVEP BCI, Electronics, vol. 8, no. 12, p. 1554, 2019.
Google Scholar
Tsinghua Science and Technology
Volume 26 Issue 4,
August 2021
Pages 505-522
DOI: 10.26599/TST.2020.9010015
Cite this article:
Lin J, Liang L, Han X, et al. Cross-Target Transfer Algorithm Based on the Volterra Model of SSVEP-BCI. Tsinghua Science and Technology, 2021, 26(4): 505-522. https://doi.org/10.26599/TST.2020.9010015

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Received: 06 April 2020
Revised: 14 May 2020
Accepted: 20 May 2020
Published: 04 January 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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