Improving longitudinal P300-BCI performance for people with ALS using a data augmentation and jitter correction approach

References

• Gómez-Vilda P, Londral ARM, Rodellar-Biarge V, et al. Monitoring amyotrophic lateral sclerosis by biomechanical modeling of speech production. Neurocomputing. 2015;151:130–138.
   Web of Science ®Google Scholar
• Birbaumer N, Piccione F, Silvoni S, et al. Ideomotor silence: the case of complete paralysis and brain–computer interfaces (BCI). Psychol Res. 2012;76(2):183–191.
   PubMed Web of Science ®Google Scholar
• Kübler A, Birbaumer N. Brain–computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin Neurophysiol. 2008;119(11):2658–2666.
   PubMed Web of Science ®Google Scholar
• Zisk AH, Borgheai SB, McLinden J, et al. P300 Latency jitter and its correlates in people with amyotrophic lateral sclerosis. Clin Neurophysiol. 2021;132(2):632–642.
   PubMed Web of Science ®Google Scholar
• McCane LM, Sellers EW, Mcfarland DJ, et al. Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis. Amyotrophic Lat Scler Frontotemporal Degener. 2014;15(3–4):207–215.
   PubMed Web of Science ®Google Scholar
• Geronimo AM, Simmons Z. The P300 ‘face’ speller is resistant to cognitive decline in ALS. Brain-Comput Interfaces. 2017;4(4):225–235.
   Web of Science ®Google Scholar
• Sellers EW, Vaughan TM, Wolpaw JR. A brain-computer interface for long-term independent home use. Amyotrophic Lat Scler. 2010;11(5):449–455.
   PubMed Web of Science ®Google Scholar
• Shahriari Y, Sellers EW, McCane LM, et al., “Directional brain functional interaction analysis in patients with amyotrophic lateral sclerosis,” in 2015 7th International IEEE/EMBS Conference on Neural Eng. (NER) Montpellier, France, 2015, pp. 972–975. https://doi.org/10.1109/NER.2015.7146788.
   Google Scholar
• Shahriari Y, Vaughan TM, McCane L, et al. An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data. J Neural Eng. 2019;16(5):056031.
   PubMed Web of Science ®Google Scholar
• Shahriari Y, Erfanian A. Improving the performance of P300-based brain–computer interface through subspace-based filtering. Neurocomputing. 2013;121:434–441.
   Web of Science ®Google Scholar
• Wang J, Gu Z, Yu Z, et al. An online semi-supervised P300 speller based on extreme learning machine. Neurocomputing. 2017;269:148–151.
   Web of Science ®Google Scholar
• Alvarado-Gonzalez M, Fuentes-Pineda G, Cervantes-Ojeda J. A few filters are enough: convolutional neural network for P300 detection. Neurocomputing. 2021;425:37–52.
   Web of Science ®Google Scholar
• Bayliss JD, Ballard DH. Single trial P3 epoch recognition in a virtual environment. Neurocomputing. 2000;32:637–642.
   Web of Science ®Google Scholar
• Allison BZ, Kübler A, Jin J. 30+ years of P300 brain–computer interfaces. Psychophysiology. 2020;57(7):e13569.
   PubMed Web of Science ®Google Scholar
• Speier W, Chandravadia N, Roberts D, et al. Online BCI typing using language model classifiers by ALS patients in their homes. Brain-Comput Interfaces. 2017;4(1–2):114–121.
   Web of Science ®Google Scholar
• Polich J. Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol. 2007;118(10):2128–2148.
   PubMed Web of Science ®Google Scholar
• Ramadan RA, Vasilakos AV. Brain computer interface: control signals review. Neurocomputing. 2017;223:26–44.
   Web of Science ®Google Scholar
• Mak JN, McFarland DJ, Vaughan TM, et al. EEG correlates of P300-based brain–computer interface (BCI) performance in people with amyotrophic lateral sclerosis. J Neural Eng. 2012;9(2):026014.
   PubMed Web of Science ®Google Scholar
• Geronimo A, Simmons Z, Schiff SJ. Performance predictors of brain–computer interfaces in patients with amyotrophic lateral sclerosis. J Neural Eng. 2016;13(2):026002.
   PubMed Web of Science ®Google Scholar
• Thompson DE, Warschausky S, Huggins JE. Classifier-based latency estimation: a novel way to estimate and predict BCI accuracy. J Neural Eng. 2012;10(1):016006.
   PubMed Web of Science ®Google Scholar
• Mowla MR, Gonzalez-Morales JD, Rico-Martinez J, et al. A comparison of classification techniques to predict brain-computer interfaces accuracy using classifier-based latency estimation. Brain Sci. 2020;10(10):734.
   Web of Science ®Google Scholar
• Aricò P, Aloise F, Schettini F, et al. Influence of P300 latency jitter on event related potential-based brain–computer interface performance. J Neural Eng. 2014;11(3):035008.
   PubMed Web of Science ®Google Scholar
• Vucic S. P300 jitter latency, brain-computer interface and amyotrophic lateral sclerosis. Clin Neurophysiol. 2021;132(2):614–615.
   PubMed Web of Science ®Google Scholar
• Mowla MR, Huggins JE, Thompson DE. Enhancing P300-BCI performance using latency estimation. Brain-Comput Interfaces. 2017;4(3):137–145.
   Web of Science ®Google Scholar
• Togashi R, Washizawa Y. Feature extraction of P300 signal using Bayesian delay time estimation. In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Kaohsiung, Taiwan, 2013, pp. 1–5. https://doi.org/10.1109/APSIPA.2013.6694191.
   Google Scholar
• Iturrate I, Chavarriaga R, Montesano L, et al. Latency correction of event-related potentials between different experimental protocols. J Neural Eng. 2014;11(3):036005.
   PubMed Web of Science ®Google Scholar
• Lashgari E, Liang D, Maoz U. Data augmentation for deep-learning-based electroencephalography. J Neurosci Methods. 2020;346:108885.
   PubMed Web of Science ®Google Scholar
• Abdelfattah SM, Abdelrahman GM, Wang M. Augmenting the size of EEG datasets using generative adversarial networks. In 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, 2018, pp. 1–6. https://doi.org/10.1109/IJCNN.2018.8489727.
   Google Scholar
• Riyad M, Khalil M, Abdellah A. MI-EEGNET: a novel Convolutional Neural Network for motor imagery classification. J Neurosci Methods. 2021;353:109037.
   PubMed Web of Science ®Google Scholar
• Krell MM, Kim SK. Rotational data augmentation for electroencephalographic data. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Jeju, Korea (South), 2017, pp. 471–474. https://doi.org/10.1109/EMBC.2017.8036864.
   Google Scholar
• Krell MM, Seeland A, Kim SK Data augmentation for brain-computer interfaces: analysis on event-related potentials data . Jan . 2018 https://arxiv.org/abs/1801.02730. arXiv Preprint arXiv:1801.02730.
   Google Scholar
• Yin Z, Zhang J. Cross-subject recognition of operator functional states via EEG and switching deep belief networks with adaptive weights. Neurocomputing. 2017;260:349–366.
   Web of Science ®Google Scholar
• Wan Z, Yang R, Huang M, et al. A review on transfer learning in EEG signal analysis. Neurocomputing. 2021;421:1–14.
   Web of Science ®Google Scholar
• Lotte F, 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
• Sakai A, Minoda Y, Morikawa K. Data augmentation methods for machine-learning-based classification of bio-signals. In 2017 10th Biomedical Engineering International Conference (BMEiCON) Hokkaido, Japan, pp. 1–4. https://doi.org/10.1109/BMEiCON.2017.8229109.
   Google Scholar
• Kim SK, Kirchner EA, Stefes A, et al. Intrinsic interactive reinforcement learning–Using error-related potentials for real world human-robot interaction. Sci Rep. 2017;7(1):1–16.
   PubMedGoogle Scholar
• Wang X, Wang X, Liu W, et al. One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG. Neurocomputing. 2021;459:212–222.
   Web of Science ®Google Scholar
• Cedarbaum JM, Stambler N, Malta E, et al. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS study group (Phase III). J Neurol Sci. 1999;169(1–2):13–21.
   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
• Schalk G, Mellinger J. A practical guide to brain–computer interfacing with bci2000: general-purpose software for brain-computer interface research, data acquisition, stimulus presentation, and brain monitoring. New York: Springer Science & Business Media; 2010.
   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
• Krusienski DJ, Sellers EW, McFarland DJ, et al. Toward enhanced P300 speller performance. J Neurosci Methods. 2008;167(1):15–21.
   PubMed Web of Science ®Google Scholar
• Nasiri S, Zahedi G, Kuntz S, et al. Knowledge representation and management based on an ontological CBR system for dementia caregiving. Neurocomputing. 2019;350:181–194.
   Web of Science ®Google Scholar
• Michiels M, Larrañaga P, Bielza C. BayeSuites: an open web framework for massive Bayesian networks focused on neuroscience. Neurocomputing. 2021;428:166–181.
   Web of Science ®Google Scholar
• Dal Seno B, Matteucci M, Mainardi LT. The utility metric: a novel method to assess the overall performance of discrete brain–computer interfaces. IEEE Transactions on Neural Systems Rehabilitation Engineering. 2010;18(1):20–28.
   PubMed Web of Science ®Google Scholar
• Mowla MR, Huggins JE, Thompson DE. Evaluation and performance assessment of the brain–computer interface system Nam, CS, Nijholt, A, and Lotte, F. In: Brain–Computer Interfaces Handbook. New York: CRC Press; 2018. 635–650. https://www.taylorfrancis.com/chapters/edit/https://doi.org/10.1201/9781351231954-33/
   Google Scholar
• Team RC. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2021;4.0.5.
   Google Scholar
• Wilcoxon F. Individual comparisons by ranking methods.Biom Bull. 1945;1(6):80–83.
   Google Scholar
• Bakdash JZ, Marusich LR. Repeated measures correlation. Front Psychol. 2017;8:456.
   PubMed Web of Science ®Google Scholar
• Metropolis N, Ulam S. The monte carlo method. J Am Stat Assoc. 1949;44(247):335–341.
   PubMed Web of Science ®Google Scholar
• Kroese DP, Chan JC. Monte carlo sampling. Statistical Modeling Computation 2014, Springer.;195–226. DOI:https://doi.org/10.1007/978-1-4614-8775-3_7.
   Google Scholar
• Huang Y, Chen X, Zhang J, et al. Single-trial ERPs denoising via collaborative filtering on ERPs images. Neurocomputing. 2015;149:914–923.
   Web of Science ®Google Scholar
• Murguialday AR, Hill J, Bensch M, et al. Transition from the locked in to the completely locked-in state: a physiological analysis. Clin Neurophysiol. 2011;122(5):925–933.
   PubMed Web of Science ®Google Scholar
• Holz EM, Botrel L, Kaufmann T, et al. Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: a case study. Arch Phys Med Rehabil. 2015;96(3):S16–S26.
   PubMed Web of Science ®Google Scholar
• Wolpaw JR, Bedlack RS, Reda DJ, et al. Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis. Neurology. 2018;91(3):e258–e267.
   PubMed Web of Science ®Google Scholar
• Borgheai SB, McLinden J, Zisk AH, et al. Enhancing communication for people in late-stage ALS using an fNIRS-based BCI system. IEEE Trans Neural Syst Rehabil Eng. 2020;28(5):1198–1207 doi:https://doi.org/10.1109/TNSRE.2020.2980772 .
   PubMed Web of Science ®Google Scholar
• Thompson DE, Mowla MR, Huggins JE. Evidence of latency variation in the P3 speller brain computer interface. In Soc. Neurosci., October 23, 2019. https://www.abstractsonline.com/pp8/#!/7883/presentation/69619
   Google Scholar