726
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Closed loop BCI system for Cybathlon 2020

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, & ORCID Icon
Pages 114-128 | Received 13 May 2022, Accepted 29 Aug 2023, Published online: 14 Sep 2023

References

  • Riener R, Seward LJ. Cybathlon 2016. In 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 2014. pp. 2792–2794. doi: 10.1109/SMC.2014.6974351.
  • Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–791. doi: 10.1016/S1388-2457(02)00057-3
  • Perdikis S, Tonin L, Saeedi S, et al. The cybathlon BCI race: successful longitudinal mutual learning with two tetraplegic users. PLoS Biol. 2018;16(5):28. doi: 10.1371/journal.pbio.2003787
  • Tortora S, Beraldo G, Bettella F, et al. Neural correlates of user learning during long-term BCI training for the cybathlon competition. J Neuroeng Rehabil. 2022;19(1). doi: 10.1186/s12984-022-01047-x
  • Statthaler K, Schwarz A, Steyrl D, et al. Cybathlon experiences of the graz BCI racing team mirage91 in the brain-computer interface discipline. J Neuroeng Rehabil. 2017;14(1):16. doi:10.1186/s12984-017-0344-9
  • Hehenberger L, Kobler RJ, Lopes-Dias C, et al. Long-term mutual training for the CYBATHLON BCI race with a tetraplegic pilot: a case study on inter-session transfer and intra-session adaptation. Front Hum Neurosci. 2021;15(635777):15. doi: 10.3389/fnhum.2021.635777
  • Ang KK, Chin ZY, Wang C, et al. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci. 2012;6(39). doi: 10.3389/fnins.2012.00039
  • 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):17. doi:10.1088/1741-2552/aace8c
  • Riyad M, Khalil M, Adib A. MI-EEGNET: a novel convolutional neural network for motor imagery classification. J Neurosci Methods. 2021;353(109037):109037. doi: 10.1016/j.jneumeth.2020.109037
  • Zhang D, Yao L, Zhang X, et al. EEG-based intention recognition from spatio- temporal representations via cascade and parallel convolutional recurrent neural networks. arXiv: 170806578 [Cs, Q-Bio]. 2017.
  • Fadel W, Wahdow M, Kollod C, et al. Chessboard EEG images classification for BCI systems using deep neural network. In: Chen Y, Nakano T, Lin L, Mahfuz MU Guo W, editors. Bio-inspired information and communication technologies. ser Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 329. Cham: Springer International Publishing, 2020;pp. 97–104. doi:10.1007/978-3-030-57115-3_8
  • Fadel W, Kollod C, Wahdow M, et al. Multi-class classification of motor imagery EEG signals using image-based deep recurrent convolutional neural network. In 2020 8th International Winter Conference on Brain-Computer Interface (BCI); 2020. pp. 1–4. doi: 10.1109/BCI48061.2020.9061622.
  • Roots K, Muhammad Y, Muhammad N. Fusion convolutional neural network for cross-subject EEG motor imagery classification. Computers. 2020;9(3):72. doi: 10.3390/computers9030072
  • Gedik N. Classification of right and left-hand movement using multi-resolution analysis method. International Journal Of Biomedical And Biological Engineering. 2021;15(1):6–9.
  • Varsehi H, Firoozabadi SMP. An EEG channel selection method for motor imagery based brain–computer interface and neurofeedback using granger causality. Neural Networks. 2021;133:193–206. doi: 10.1016/j.neunet.2020.11.002
  • Gwon D, Ahn M. Alpha and high gamma phase amplitude coupling during motor imagery and weighted cross-frequency coupling to extract discriminative cross-frequency patterns. Neuroimage. 2021;240:118 403. doi: 10.1016/j.neuroimage.2021.118403
  • Huang Y, Jin J, Xu R, et al. Multi-view optimization of time-frequency common spatial patterns for brain-computer interfaces. J Neurosci Methods. 2022;365:109 378. jneumeth. 2021. 109378.
  • Jin J, Miao Y, Daly I, et al. Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Networks. 2019;118:262–270. doi: 10.1016/j.neunet.2019.07.008
  • Li D, Xu J, Wang J, et al. A multi-scale fusion convolutional neural network based on attention mechanism for the visualization analysis of EEG signals decoding. In IEEE Transactions on Neural Systems and Rehabilitation Engineering; 2020. pp. 1–1, doi: 10.1109/tnsre.2020.3037326.
  • Blankertz B, Muller K-R, Curio G, et al. The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans Biomed Eng. 2004;51(6):1044–1051. doi:10.1109/TBME.2004.826692
  • Blankertz B, Muller K-R, Krusienski D, et al. The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng. 2006;14(2):153–159. doi:10.1109/TNSRE.2006.875642
  • Sajda P, Gerson A, Muller K-R, et al. A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2003;11(2):184–185. doi: 10.1109/TNSRE.2003.814453
  • Tangermann M, Müller K-R, Aertsen A, et al. Review of the BCI competition IV. Front Neurosci. 2012;6(55):31. doi:10.3389/fnins.2012.00055
  • Fan C-C, Yang H, Hou Z-G, et al. Bilinear neural network with 3-d attention for brain decoding of motor imagery movements from the human EEG. Cogn Neurodyn. 2020;15(1):181–189. doi: 10.1007/s11571-020-09649-8
  • Goldberger Ary L, Amaral Luis AN, Glass L, et al. Eugene, PhysioBank, PhysioToolkit, and PhysioNet. Circulation. 2000;101(23):215–220. doi: 10.1161/01.CIR.101.23.e215 23.e215.
  • Bria A, Marrocco C, Tortorella F. Sinc-based convolutional neural networks for EEG-BCI-based motor imagery classification. arXiv:210110846 [eess]. 2021.
  • Schalk G, McFarland DJ, Hinterberger T, et al. BCI2000: a general- purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng. 2004;51(6):1034–1043. doi: 10.1109/TBME.2004.827072 827072.
  • Gramfort A, Luessi M, Larson E, et al. MEG and EEG data analysis with MNE-python. Front Neurosci. 2013;7: doi: 10.3389/fnins.2013.00267
  • Abadi M, Agarwal A, Barham P, et al. TensorFlow: large- scale machine learning on heterogeneous distributed systems. arXiv: 160304467[cs]. 2016.
  • Nolan H, Whelan R, Reilly RB. FASTER: fully automated statistical thresholding for EEG artifact rejection. J Neurosci Methods. 2010;192(1):152–162. doi:10.1016/j.jneumeth.2010.07.015
  • Vliet MV. Wmvanvliet/mne-faster: first official release, version 1.0. 2021. doi: 10.5281/zenodo.5112399
  • Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12(85):2825–2830.
  • Thomas KP, Guan C, Lau CT, et al. A new discriminative common spatial pattern method for motor imagery brain–computer interfaces. IEEE Trans Biomed Eng. 2009;56(11):2730–2733. doi:10.1109/tbme.2009.2026181
  • Xu G, Shen X, Chen S, et al. A deep transfer convolutional neural network framework for EEG signal classification. IEEE Access. 2019;7:112 767–112 776. doi:10.1109/access.2019.2930958
  • Riyad M, Khalil M, Adib A. A novel multi- scale convolutional neural network for motor imagery classification. Biomedical Signal Processing And Control. 2021;68(102747):102747. doi: 10.1016/j.bspc.2021.102747
  • Benaroch C, Sadatnejad K, Roc A, et al. Long-term BCI training of a tetraplegic user: adaptive riemannian classifiers and user training. Front Hum Neurosci. 2021;15(635653):22. doi:10.3389/fnhum.2021.635653
  • Robinson N, Chouhan T, Mihelj E, et al. Design considerations for long term non-invasive brain computer interface training with tetraplegic CYBATHLON pilot. Front Hum Neurosci. 2021;15(648275):16. doi:10.3389/fnhum.2021.648275
  • Korik A, McCreadie K, McShane N, et al. Competing at the cybathlon championship for people with disabilities: long-term motor imagery brain–computer interface training of a cybathlete who has tetraplegia. J Neuroeng Rehabil. 2022;19(1):22. doi:10.1186/s12984-022-01073-9
  • Blanco-Mora DA, Aldridge A, Jorge C, et al. Finding the optimal time window for increased classification accuracy during motor imagery. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies; 2021. pp. 144–151. doi: 10.5220/0010316101440151.
  • Cooley JW, Tukey JW. An algorithm for the machine calculation of complex fourier series. Math Comp. 1965;19(90):297–301. doi:10.1090/S0025-5718-1965-0178586-1
  • Raju VNG, Lakshmi KP, Jain VM, Kalidindi A, and Padma V. Study the influence of normalization/transformation process on the accuracy of supervised classification. In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT); 2020. pp. 729–735. doi: 10.1109/icssit48917.2020.9214160.
  • Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers, In Proceedings of the fifth annual workshop on Computational learning theory, ser. COLT ’92, New York, NY, USA: Association for Computing Machinery, 1992, pp. 144–152. doi: 10.1145/130385.130401.
  • Zhang Y, Liu J, Liu J, et al. EEG recognition of motor imagery based on SVM ensemble. In 2018 5th International Conference on Systems and Informatics (ICSAI); 2018. pp. 866–870. doi: 10.1109/ICSAI.2018.8599464.
  • Stenner T, Boulay C, Grivich M, et al. Clisson, and phfix. Sccn/Liblsl: V1151 - ASIO Updated Version,v. 2021;1(15.1): doi: 10.5281/zenodo.5415959
  • Müller-Putz GR, Coyle D, Lotte F, et al. Editorial: long term user training and preparation to succeed in a closed-loop BCI competition. Front Human Neurosci. 2022;16: doi: 10.3389/fnhum.2022.869700
  • Novak D, Sigrist R, Gerig NJ, et al. Benchmarking brain-computer interfaces outside the laboratory: the cybathlon 2016. Front Neurosci. 2018;11(756):14. doi: 10.3389/fnins.2017.00756
  • Turi F, Clerc M, Papadopoulo T. Long multi- stage training for a motor-impaired user in a BCI competition. Front Hum Neurosci. 2021;15(647908). doi: 10.3389/fnhum.2021.647908