Table of Contents Author Guidelines Submit a Manuscript
Advances in Human-Computer Interaction
Volume 2012, Article ID 578295, 13 pages
http://dx.doi.org/10.1155/2012/578295
Research Article

Objective and Subjective Evaluation of Online Error Correction during P300-Based Spelling

1Lyon Neuroscience Research Center (CRNL), INSERM, CNRS, Dycog team, 95 Boulevard Pinel, 69500 Bron, France
2Université Claude Bernard Lyon 1, 69000 Lyon, France
3CERMEP, 95 Boulevard Pinel, 69500 Bron, France

Received 6 July 2012; Revised 15 October 2012; Accepted 24 October 2012

Academic Editor: Surjo R. Soekadar

Copyright © 2012 Perrin Margaux et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [21 citations]

The following is the list of published articles that have cited the current article.

  • Jacopo Tessadori, Lucia Schiatti, Giacinto Barresi, and Leonardo S. Mattos, “Does tactile feedback enhance single-trial detection of error-related eeg potentials?,” 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1417–1422, . View at Publisher · View at Google Scholar
  • Mohammad Faizuddin Mohd Noor, Simon Rogers, John Williamson, Mohammad Faizuddin Mohd Noor, Simon Rogers, and John Williamson, “Detecting Swipe Errors on Touchscreens using Grip Modulation,” Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems - CHI '16, pp. 1909–1920, . View at Publisher · View at Google Scholar
  • Thomas, Carpentier, Munos, Clerc, Daucea, and Devlaminck, “Optimizing P300-speller sequences by RIP-ping groups apart,” International IEEE/EMBS Conference on Neural Engineering, NER, pp. 1062–1065, 2013. View at Publisher · View at Google Scholar
  • Ricardo Chavarriaga, Aleksander Sobolewski, and José del R. Millán, “Errare machinale est: the use of error-related potentials in brain-machine interfaces,” Frontiers in Neuroscience, vol. 8, 2014. View at Publisher · View at Google Scholar
  • Emmanuel Maby, Aline Bompas, Jérémie Mattout, Olivier Bertr, Gaëtan Sanchez, and Jean Daunizeau, “Toward a new application of real-time electrophysiology: Online optimization of cognitive neurosciences hypothesis testing,” Brain Sciences, vol. 4, no. 1, pp. 49–72, 2014. View at Publisher · View at Google Scholar
  • Boyla O. Mainsah, Kenneth D. Morton, Leslie M. Collins, Eric W. Sellers, and Chandra S. Throckmorton, “Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers,” Ieee Transactions On Neural Systems And Rehabilitation Engineering, vol. 23, no. 5, pp. 737–743, 2015. View at Publisher · View at Google Scholar
  • Jeong-Hwan Lim, Jun-Hak Lee, Han-Jeong Hwang, Dong Hwan Kim, and Chang-Hwan Im, “Development of a hybrid mental spelling system combining SSVEP-based brain–computer interface and webcam-based eye tracking,” Biomedical Signal Processing and Control, vol. 21, pp. 99–104, 2015. View at Publisher · View at Google Scholar
  • Eoin Thomas, Matthew Dyson, Laurence Casini, and Boris Burle, “Online extraction and single trial analysis of regions contributing to erroneous feedback detection,” NeuroImage, vol. 121, pp. 146–158, 2015. View at Publisher · View at Google Scholar
  • Nima Bigdely-Shamlo, Tim Mullen, Christian Kothe, Kyung-Min Su, and Kay A Robbins, “The PREP pipeline: Standardized preprocessing for large-scale EEG analysis,” Frontiers in Neuroinformatics, vol. 9, no. JUNE, pp. 1–19, 2015. View at Publisher · View at Google Scholar
  • Felix Putze, Christoph Amma, and Tanja Schultz, “Design and evaluation of a self-correcting gesture interface based on error potentials from EEG,” Conference on Human Factors in Computing Systems - Proceedings, vol. 2015-, pp. 3375–3384, 2015. View at Publisher · View at Google Scholar
  • Jérémie Mattout, Margaux Perrin, Olivier Bertrand, and Emmanuel Maby, “Improving BCI performance through co-adaptation: Applications to the P300-speller,” Annals of Physical and Rehabilitation Medicine, 2015. View at Publisher · View at Google Scholar
  • Amar R. Marathe, Vernon J. Lawhern, Dongrui Wu, David Slayback, and Brent J. Lance, “Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 3, pp. 333–343, 2016. View at Publisher · View at Google Scholar
  • Jijun Tong, Qinguang Lin, Ran Xiao, and Lei Ding, “Combining multiple features for error detection and its application in brain–computer interface,” BioMedical Engineering OnLine, vol. 15, no. 1, 2016. View at Publisher · View at Google Scholar
  • Timothy Zeyl, Erwei Yin, Michelle Keightley, and Tom Chau, “Partially supervised P300 speller adaptation for eventual stimulus timing optimization: target confidence is superior to error-related potential score as an uncertain label,” Journal of Neural Engineering, vol. 13, no. 2, pp. 026008, 2016. View at Publisher · View at Google Scholar
  • Ke Lin, Andrea Cinetto, Yijun Wang, Xiaogang Chen, Shangkai Gao, and Xiaorong Gao, “An online hybrid BCI system based on SSVEP and EMG,” Journal of Neural Engineering, vol. 13, no. 2, pp. 026020, 2016. View at Publisher · View at Google Scholar
  • Timothy Zeyl, Erwei Yin, Michelle Keightley, and Tom Chau, “Adding Real-Time Bayesian Ranks to Error-Related Potential Scores Improves Error Detection and Auto-Correction in a P300 Speller,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 1, pp. 46–56, 2016. View at Publisher · View at Google Scholar
  • Saugat Bhattacharyya, Amit Konar, D. N. Tibarewala, and Mitsuhiro Hayashibe, “A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection,” Frontiers in Neuroscience, vol. 11, 2017. View at Publisher · View at Google Scholar
  • Mohammad Abu-Alqumsan, Christoph Kapeller, Christoph Hintermüller, Christoph Guger, and Angelika Peer, “Invariance and variability in interaction error-related potentials and their consequences for classification,” Journal of Neural Engineering, vol. 14, no. 6, pp. 066015, 2017. View at Publisher · View at Google Scholar
  • Vernon J Lawhern, Amelia J Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, and Brent J Lance, “EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces,” Journal of Neural Engineering, vol. 15, no. 5, pp. 056013, 2018. View at Publisher · View at Google Scholar
  • F Lotte, L Bougrain, A Cichocki, M Clerc, M Congedo, A Rakotomamonjy, and F Yger, “A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update,” Journal of Neural Engineering, vol. 15, no. 3, pp. 031005, 2018. View at Publisher · View at Google Scholar
  • Aniana Cruz, Gabriel Pires, and Urbano J. Nunes, “Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 1, pp. 26–36, 2018. View at Publisher · View at Google Scholar