End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities

References

• F L, Nam CS, Nijholt A, Ed. Brain-computer interfaces handbook. Boca Raton: CRC Press; 2018.
   Google Scholar
• Huggins JE, Guger C, Allison B, et al. Workshops of the fifth international brain-computer interface meeting: defining the future. Brain-Comput Interfaces. 2014;1(1):27–49.
   PubMedGoogle Scholar
• Brunner C, Birbaumer N, Blankertz B, et al. BNCI Horizon 2020: towards a roadmap for the BCI community. Brain-Comput Interfaces. 2015;2(1):1–10.
   Web of Science ®Google Scholar
• Congedo M, Barachant A, Bhatia R. Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Comput Interfaces. 2017;4(3):1–20.
   Web of Science ®Google Scholar
• Rashid M, Sulaiman N, Majeed AP, et al. Current status, challenges and possible solutions of EEG based brain-computer interface: a comprehensive review. Front Neurorobot. 2020;14:25.
   PubMed Web of Science ®Google Scholar
• 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.
   PubMedGoogle Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Barachant A, Congedo M. A plug&play P300 BCI using information geometry, arXiv preprint arXiv:1409.0107, 2014.
   Google Scholar
• Goncharenko V, Grigoryan R Samokhina A. Raccoons vs Demons: multiclass labeled P300 dataset, arXiv preprint arXiv:2005.02251, 2020.
   Google Scholar
• Yuan P, Gao X, Allison B, et al. A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces. J Neural Eng. 2013;10(2):026014.
   PubMed Web of Science ®Google Scholar
• Lotte L, Bougrain L, Cichocki A, et al. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng. 2018;15(3):031005.
   PubMed Web of Science ®Google Scholar
• Tomioka R, Aihara K, Müller K-R. Logistic regression for single trial EEG classification. NIPS. 2007;19:1377–1384.
   Google Scholar
• Reuderink B, Poel M. Robustness of the common spatial patterns algorithm in the BCI-pipeline. Univ Twente Tech Rep. 2008.
   Google Scholar
• Grosse-Wentrup M, Liefhold C, Grasmann K, et al. Beamforming in noninvasive brain-computer interfaces. IEEE Trans Biomed Eng. 2009;56(4):1209–1219.
   PubMed Web of Science ®Google Scholar
• Barachant A, Bonnet S, Congedo M, et al. Multiclass brain–computer interface classification by Riemannian geometry. IEEE Trans Biomed Eng. 2012;59(4):920–928.
   PubMed Web of Science ®Google Scholar
• Korczowski L, Congedo M, Jutten C. Single-trial classification of multi-user P300-based brain-computer interface using riemannian geometry. Embc. 2015;2015:1769–1772.
   Google Scholar
• Wolpaw J, Birbaumer N, McFarland DJ, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–791.
   PubMed Web of Science ®Google Scholar
• Chavarriaga R, D. R. Millan J. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2010;18(4):381–388.
   PubMed Web of Science ®Google Scholar
• Luck SJ. An introduction to the event-related potential technique. Cambridge, Massachusetts: MIT Press; 2014.
   Google Scholar
• pyRiemann, [cited 2022 Nov 01]. https://pyriemann.readthedocs.io/.
   Google Scholar
• Jin J, Allison BZ, Kaufmann T, et al. The changing face of P300 BCIs: a comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement. PloS One. 2012;7(11):e49688.
   PubMed Web of Science ®Google Scholar
• Congedo M. EEG source analysis, Grenoble University, Tech. Rep. HDR, 2013.
   Google Scholar
• Yger F, Berar M, Lotte F. Riemannian approaches in brain-computer interfaces: a review. IEEE Trans Neural Syst Rehabil Eng. 2017;25(10):1753–1762.
   PubMed Web of Science ®Google Scholar
• Moakher M. A differential geometric approach to the geometric mean of symmetric positive-definite matrices. SIAM J Matrix Anal Appl. 2005;26(3):735–747.
   Web of Science ®Google Scholar
• Fletcher PT, Lu C, Pizer SM, et al. Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE Trans Med Imaging. 2004;23(8):995–1005.
   PubMed Web of Science ®Google Scholar
• Roy Y, Banville H, Albuquerque I, et al. Deep learning-based electroencephalography analysis: a systematic review. J Neural Eng. 2019;16(5):051001.
   PubMed Web of Science ®Google Scholar
• Said S, Bombrun L, Berthoumieu Y, et al. Riemannian Gaussian distributions on the space of symmetric positive definite matrices. IEEE Trans Inf Theory. 2017;63(4):2153–2170.
   Web of Science ®Google Scholar
• Kaufmann T, Völker S, Gunesch L, et al. Spelling is just a click away – a user-centered brain-computer interface including auto-calibration and predictive text entry. Front Neurosci. 2012;6. DOI:10.3389/fnins.2012.00072.
   PubMed Web of Science ®Google Scholar
• Schreuder M, Höhne J, Blankertz B, et al. Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. J Neural Eng. 2013;10(3):036025.
   PubMed Web of Science ®Google Scholar
• Kindermans P-J, Tangermann M, Müller K-R, et al. Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller. J Neural Eng. 2014;11(3):035005.
   PubMed Web of Science ®Google Scholar
• Daucé E, Thomas E. Evidence build-up facilitates on-line adaptivity in dynamic environments: example of the BCI P300-speller. ESANN. 2014;14:377–382.
   Google Scholar
• Ng AY, Jordan MI. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. NIPS. 2002;841–848.
   Google Scholar
• Riccio A, Simione L, Schettini F, et al. Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis. Front Hum Neurosci. 2013;7:732.
   PubMed Web of Science ®Google Scholar
• Aloise F, Aricò P, Schettini F, et al. A covert attention P300-based brain-computer interface: geospell. Ergonomics. 2012;55(5):538–551.
   PubMed Web of Science ®Google Scholar
• Krusienski DJ, Sellers EW, Cabestaing F, et al. A comparison of classification techniques for the P300 speller. J Neural Eng. 2006;3(4):299.
   PubMed Web of Science ®Google Scholar
• Blankertz B, Lemm S, Treder M, et al. Single-trial analysis and classification of ERP components - A tutorial. NeuroImage. 2011;56(2):814–825.
   PubMed Web of Science ®Google Scholar
• Clisson P, Bertrand-Lalo R, Congedo M, et al. Timeflux: an open-source framework for the acquisition and near real-time processing of signal streams. GBCIC. 2019.
   Google Scholar
• Jayaram V, Barachant A. MOABB: trustworthy algorithm benchmarking for BCIs. J Neural Eng. 2018;15(6):066011.
   PubMed Web of Science ®Google Scholar
• Gramfort A, Luessi M, Larson E, et al. MNE software for processing MEG and EEG data. NeuroImage. 2014;86:446–460.
   PubMed Web of Science ®Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–2830.
   Web of Science ®Google Scholar
• Kalunga EK, Chevallier S, Barthélemy Q, et al. Online SSVEP-based BCI using Riemannian geometry. Neurocomputing. 2016;191:55–68.
   Web of Science ®Google Scholar
• Barachant A, Bonnet S, Congedo M, et al. Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing. 2013;112:172–178.
   Web of Science ®Google Scholar
• Arns M, Batail JM, Bioulac S, et al. Neurofeedback: one of today’s techniques in psychiatry? Encephale. 2017;43(2):135–145.
   PubMed Web of Science ®Google Scholar
• Sen S, Dutta A, Dey N. Audio processing and speech recognition: concepts, techniques and research overviews. Singapore: Springer; 2019.
   Google Scholar
• Wang Y, Ostermann J, Zhang YQ. Video Processing and Communications. NJ: Prentice hall Upper Saddle River,; 2002.
   Google Scholar
• Kalunga EK, Chevallier S, Barthélemy Q. Transfer learning for SSVEP-based BCI using Riemannian similarities between users. EUSIPCO. 2018;1685–1689.
   Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Rodrigues PLC, Jutten C, Congedo M. Riemannian procrustes analysis: transfer learning for brain-computer. IEEE Trans Biomed Eng. 2019;66(8):2390–2401.
   PubMed Web of Science ®Google Scholar