Review of training-free event-related potential classification approaches in the World Robot Contest 2021

Huanyu Wu; Dongrui Wu

doi:10.26599/BSA.2022.9050001

Brain Science Advances 2022, 8(2): 82-98 https://doi.org/10.26599/BSA.2022.9050001

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Open Access | Issue | Published: 29 June 2022

Review of training-free event-related potential classification approaches in the World Robot Contest 2021

Show Author's Information Hide Author's Information Huanyu Wu, Dongrui Wu(

)

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

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brain-computer interfaces, electroencephalogram, rapid serial visual presentation (RSVP), data imbalance, training-free

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Wu H, Wu D. Review of training-free event-related potential classification approaches in the World Robot Contest 2021. Brain Science Advances, 2022, 8(2): 82-98. https://doi.org/10.26599/BSA.2022.9050001

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Abstract

Recently, rapid serial visual presentation (RSVP), as a new event- related potential (ERP) paradigm, has become one of the most popular forms in electroencephalogram signal processing technologies. Several improvement approaches have been proposed to improve the performance of RSVP analysis. In brain-computer interface systems based on RSVP, the family of approaches that do not depend on training specific parameters is essential. The participating teams proposed several effective training-free frameworks of algorithms in the ERP competition of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper discusses the effectiveness of various approaches in improving the performance of the system without requiring training and suggests how to apply these approaches in a practical system. First, appropriate preprocessing techniques will greatly improve the results. Then, the non-deep learning algorithm may be more stable than the deep learning approach. Furthermore, ensemble learning can make the model more stable and robust.

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About this article

Review of training-free event-related potential classification approaches in the World Robot Contest 2021

Show Author's information Hide Author's Information Huanyu Wu, Dongrui Wu(

)

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

Abstract

Keywords: brain-computer interfaces, electroencephalogram, rapid serial visual presentation (RSVP), data imbalance, training-free

References(30)

[1]

Li YQ, Pan JH, Wang F, et al. A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control. IEEE Trans Biomed Eng 2013, 60(11): 3156-3166.

DOI Google Scholar

[2]

Zhang X, Wu DR, Ding LY, et al. Tiny noise, big mistakes: adversarial perturbations induce errors in brain-computer interface spellers. Natl Sci Rev 2021, 8(4): nwaa233.

DOI Google Scholar

[3]

Park H, Myung B, Yoo SK. Power consumption of wireless EEG device for BCI application: portable EEG system for BCI. In 2013 International Winter Workshop on Brain-Comput Interface (BCI), Gangwon, Korea, 2013, pp 100-102.

DOI Google Scholar

[4]

Sourina O, Liu Y. EEG-Enabled Affective Applications. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, 2013, pp 707-708.

DOI Google Scholar

[5]

Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 1988, 70(6): 510-523.

DOI Google Scholar

[6]

Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proc IEEE 2001, 89(7): 1123-1134.

DOI Google Scholar

[7]

Panwar S, Rad P, Jung TP, et al. Modeling EEG data distribution with a Wasserstein generative adversarial network to predict RSVP events. IEEE Trans Neural Syst Rehabilitation Eng 2020, 28(8): 1720-1730.

DOI Google Scholar

[8]

Meng J, Meriño LM, Robbins K, et al. Exploiting correlated discriminant features in time frequency and space for characterization and robust classification of image RSVP events with EEG data. 2012 IEEE Stat Signal Process Work SSP 2012: 668-671.

DOI Google Scholar

[9]

Fuhrmann Alpert G, Manor R, Spanier AB, et al. Spatiotemporal representations of rapid visual target detection: a single-trial EEG classification algorithm. IEEE Trans Biomed Eng 2014, 61(8): 2290-2303.

DOI Google Scholar

[10]

Manor R, Geva AB. Convolutional neural network for multi-category rapid serial visual presentation BCI. Front Comput Neurosci 2015, 9: 146.

DOI Google Scholar

[11]

Schirrmeister RT, Springenberg JT, Fiederer LDJ, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 2017, 38(11): 5391-5420.

DOI Google Scholar

[12]

Ahmed S, Mauricio Merino L, Mao ZJ, et al. A Deep Learning method for classification of images RSVP events with EEG data. In 2013 IEEE Global Conference on Signal and Information Processing. Austin, TX, USA, 2013, pp 33-36.

DOI Google Scholar

[13]

Zang B, Lin Y, Liu Z, et al. A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs. J Neural Eng 2021, 18(4): 0460c8.

DOI Google Scholar

[14]

He H, Wu DR. Transfer learning for brain-computer interfaces: a Euclidean space data alignment approach. IEEE Trans Biomed Eng 2020, 67(2): 399-410.

DOI Google Scholar

[15]

Rivet B, Souloumiac A, Attina V, et al. xDAWN algorithm to enhance evoked potentials: application to brain-computer interface. IEEE Trans Biomed Eng 2009, 56(8): 2035-2043.

DOI Google Scholar

[16]

Kobler RJ, Hirayama JI, Hehenberger L, et al. On the interpretation of linear Riemannian tangent space model parameters in M/EEG. In Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Mexico City, Mexico, 2021, pp 5909-5913.

DOI

[17]

Kleinbaum DG, Dietz K, Gail M, et al. Logistic regression. New York: Springer-Verlag, 2002.

[18]

Shi XJ, Chen ZR, 0014 HW, et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015, pp 802-810.

Google Scholar

[19]

Zanini P, Congedo M, Jutten C, et al. Transfer learning: a Riemannian geometry framework with applications to brain-computer interfaces. IEEE Trans Biomed Eng 2018, 65(5): 1107-1116.

DOI Google Scholar

[20]

Pan SJ, Yang Q. IEEE transactions on knowledge and data engineering. IEEE Trans Knowl Data Eng 2004, 16(3): C2.

DOI Google Scholar

[21]

Sun BC, Feng JS, Saenko K. Return of frustratingly easy domain adaptation. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’ 16). AAAI Press, 2016, pp 2058-2065.

DOI Google Scholar

[22]

Wu DR, Lawhern VJ, Hairston WD, et al. Switching EEG headsets made easy: reducing offline calibration effort using active weighted adaptation regularization. IEEE Trans Neural Syst Rehabilitation Eng 2016, 24(11): 1125-1137.

DOI Google Scholar

[23]

Gretton A, Borgwardt K, Rasch M, et al. A kernel method for the two-sample-problem. In Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference. Schölkopf B, Platt J, Hofmann T, Eds. MIT Press, 2007, pp 513-520.

Google Scholar

[24]

Liu BC, Chen XG, Li X, et al. Align and pool for EEG headset domain adaptation (ALPHA) to facilitate dry electrode based SSVEP-BCI. IEEE Trans Biomed Eng 2022, 69(2): 795-806.

DOI Google Scholar

[25]

Li F, Xia Y, Wang F, et al. Transfer learning algorithm of P300-EEG signal based on XDAWN spatial filter and Riemannian geometry classifier. Appl Sci 2020, 10(5): 1804.

DOI Google Scholar

[26]

Lawhern VJ, Solon AJ, Waytowich NR, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng 2018, 15(5): 056013.

DOI Google Scholar

[27]

Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. Lake Tahoe, Nevada, 2012, pp 1097-1105.

Google Scholar

[28]

Parra L, Christoforou C, Gerson A, et al. Spatiotemporal linear decoding of brain state. IEEE Signal Process Mag 2008, 25(1): 107-115.

DOI Google Scholar

[29]

Shamwell J, Lee H, Kwon H, et al. Single-trial EEG RSVP classification using convolutional neural networks. In Proceedings SPIE 9836 Micro- and Nanotechnology Sensors, Systems, and Applications VIII. Baltimore, Maryland, USA, 2016.

DOI Google Scholar

[30]

Marathe AR, Ries AJ, McDowell K. Sliding HDCA: single-trial EEG classification to overcome and quantify temporal variability. IEEE Trans Neural Syst Rehabil Eng 2014, 22(2): 201-211.

DOI Google Scholar

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Acknowledgements

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Publication history

Received: 13 January 2022

Revised: 20 February 2022

Accepted: 04 March 2022

Published: 29 June 2022

Issue date: June 2022

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Acknowledgements

This research was supported by the National Key Research and Development Program of China (Grant No. 2021ZD0201303), the Technology Innovation Project of Hubei Province of China (Grant No. 2019AEA171), and the Hubei Province Funds for Distinguished Young Scholars (Grant No. 2020CFA050).

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