AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Brain Science Advances Article
PDF (1.8 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

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

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

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.

Keywords

brain-computer interfaceselectroencephalogramrapid serial visual presentation (RSVP)data imbalancetraining-free

References

[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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
[6]
Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proc IEEE 2001, 89(7): 1123-1134.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
[10]
Manor R, Geva AB. Convolutional neural network for multi-category rapid serial visual presentation BCI. Front Comput Neurosci 2015, 9: 146.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
[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.
Crossref Google Scholar
[20]
Pan SJ, Yang Q. IEEE transactions on knowledge and data engineering. IEEE Trans Knowl Data Eng 2004, 16(3): C2.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref 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.
Crossref Google Scholar
Brain Science Advances
Volume 8 Issue 2,
June 2022
Pages 82-98
DOI: 10.26599/BSA.2022.9050001
Cite this article:
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

1380

Views

247

Downloads

4

Crossref

Altmetrics
Received: 13 January 2022
Revised: 20 February 2022
Accepted: 04 March 2022
Published: 29 June 2022
© The authors 2022.

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).
Return