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Computational Intelligence and Neuroscience
Volume 2011, Article ID 327953, 7 pages
http://dx.doi.org/10.1155/2011/327953
Research Article

rtMEG: A Real-Time Software Interface for Magnetoencephalography

1Program in Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2Brain Research Unit, Low Temperature Laboratory, Aalto University School of Science, 00076 Espoo, Finland
3Department of Neurology, Froedtert & The Medical College of Wisconsin, Milwaukee, WI 53226, USA
4Departments of Neurology and Biophysics, Froedtert & The Medical College of Wisconsin, Milwaukee, WI 53226, USA
5Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15260, USA

Received 1 October 2010; Revised 18 January 2011; Accepted 28 February 2011

Academic Editor: Robert Oostenveld

Copyright © 2011 Gustavo Sudre 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 [36 citations]

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

  • Foldes, Vinjamuri, Wang, Weber, and Collinger, “Stability of MEG for real-time neurofeedback,” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 5778–5781, 2011. View at Publisher · View at Google Scholar
  • René J. Huster, Zacharais N. Mokom, Stefanie Enriquez-Geppert, and Christoph S. Herrmann, “Brain computer interfaces for EEG neurofeedback: Peculiarities and solutions,” International Journal of Psychophysiology, 2013. View at Publisher · View at Google Scholar
  • Hiroki Ora, Kouji Takano, Toshihiro Kawase, Sunao Iwaki, Lauri Parkkonen, and Kenji Kansaku, “Implementation of a beamforming technique in real-time magnetoencephalography,” Journal of Integrative Neuroscience, pp. 1–11, 2013. View at Publisher · View at Google Scholar
  • Arjen Stolk, Ana Todorovic, Jan-Mathijs Schoffelen, and Robert Oostenveld, “Online and offline tools for head movement compensation in MEG,” NeuroImage, vol. 68, pp. 39–48, 2013. View at Publisher · View at Google Scholar
  • М.Н. Устинин, and M.N. Ustinin, “Integrated Software MEGMRIAn for the Analysis and Modeling of the Magnetic Encephalography Data,” Mathematical Biology and Bioinformatics, vol. 8, no. 2, pp. 691–707, 2013. View at Publisher · View at Google Scholar
  • Minkyu Ahn, Sangtae Ahn, Jun H. Hong, Hohyun Cho, Kiwoong Kim, Bong S. Kim, Jin W. Chang, and Sung C. Jun, “Gamma band activity associated with BCI performance: simultaneous MEG/EEG study,” Frontiers in Human Neuroscience, vol. 7, 2013. View at Publisher · View at Google Scholar
  • Esther Florin, Elizabeth Bock, and Sylvain Baillet, “Targeted Reinforcement of Neural Oscillatory Activity with Real-time Neuroimaging Feedback,” NeuroImage, 2013. View at Publisher · View at Google Scholar
  • Chellamani Harini, Ellen Grant, Yoshio Okada, Christos Papadelis, Banu Ahtam, and Chiran Doshi, “Current and emerging potential for magnetoencephalography in pediatric epilepsy,” Journal of Pediatric Epilepsy, vol. 2, no. 1, pp. 73–85, 2013. View at Publisher · View at Google Scholar
  • Shaun Boe, Alicia Gionfriddo, Sarah Kraeutner, Antoine Tremblay, Graham Little, and Timothy Bardouille, “Laterality of brain activity during motor imagery is modulated by the provision of source level neurofeedback,” NeuroImage, 2014. View at Publisher · View at Google Scholar
  • Lukas Breuer, Jürgen Dammers, Timothy P.L. Roberts, and N. Jon Shah, “Ocular and Cardiac Artifact Rejection for Real-Time Analysis in MEG,” Journal of Neuroscience Methods, 2014. View at Publisher · View at Google Scholar
  • Lynn M. McCane, Eric W. Sellers, Dennis J. McFarland, Joseph N. Mak, C. Steve Carmack, Debra Zeitlin, Jonathan R. Wolpaw, and Theresa M. Vaughan, “Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis,” Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, pp. 1–9, 2014. View at Publisher · View at Google Scholar
  • Lukas Breuer, Juergen Dammers, Timothy P. L. Roberts, and N. Jon Shah, “A Constrained ICA Approach for Real-Time Cardiac Artifact Rejection in Magnetoencephalography,” Ieee Transactions on Biomedical Engineering, vol. 61, no. 2, pp. 405–414, 2014. View at Publisher · View at Google Scholar
  • М.Н. Устинин, and M.N. Ustinin, “Новая методология анализа и представления функциональной структуры мозга человека: MEGMRIAn,” Mathematical Biology and Bioinformatics, vol. 9, no. 2, pp. 464–481, 2014. View at Publisher · View at Google Scholar
  • Stefanie Enriquez-Geppert, René J. Huster, Christian Figge, and Christoph S. Herrmann, “Self-regulation of frontal-midline theta facilitates memory updating and mental set shifting,” Frontiers in Behavioral Neuroscience, vol. 8, 2014. View at Publisher · View at Google Scholar
  • Graham Little, Shaun Boe, and Timothy Bardouille, “Head movement compensation in real-time magnetoencephalographic recordings,” MethodsX, 2014. View at Publisher · View at Google Scholar
  • G. Goodman, R. R. Poznanski, L. Cacha, and D. Bercovich, “The Two-Brains Hypothesis: Towards a guide for brain-brain and brain-machine interfaces,” Journal Of Integrative Neuroscience, vol. 14, no. 3, pp. 281–293, 2015. View at Publisher · View at Google Scholar
  • Stephen T. Foldes, Douglas J. Weber, and Jennifer L. Collinger, “MEG-based neurofeedback for hand rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 12, no. 1, 2015. View at Publisher · View at Google Scholar
  • Christos Papadelis, Hisako Fujiwara, William Gaetz, Ronald S. Gordon, J. Christopher Edgar, Timothy P. L. Roberts, Douglas F. Rose, and Erin S. Schwartz, “Magnetoencephalography for clinical pediatrics: Recent advances in hardware, methods, and clinical applications,” Journal of Pediatric Epilepsy, vol. 4, no. 4, pp. 139–155, 2015. View at Publisher · View at Google Scholar
  • Bin He, Bryan Baxter, Bradley J. Edelman, Christopher C. Cline, and Wenjing W. Ye, “Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms,” Proceedings Of The Ieee, vol. 103, no. 6, pp. 907–925, 2015. View at Publisher · View at Google Scholar
  • Shivayogi V. Hiremath, Weidong Chen, Wei Wang, Stephen Foldes, Ying Yang, Elizabeth C. Tyler-Kabara, Jennifer L. Collinger, and Michael L. Boninger, “Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays,” Frontiers in Integrative Neuroscience, vol. 9, 2015. View at Publisher · View at Google Scholar
  • Christoph Dinh, Daniel Strohmeier, Martin Luessi, Daniel Güllmar, Daniel Baumgarten, Jens Haueisen, and Matti S. Hämäläinen, “Real-Time MEG Source Localization Using Regional Clustering,” Brain Topography, 2015. View at Publisher · View at Google Scholar
  • Lauri Parkkonenpp. 315–330, 2015. View at Publisher · View at Google Scholar
  • Lorraine Perronnet, Anatole Lécuyer, Fabien Lotte, Maureen Clerc, and Christian Barillot, “Brain Training with Neurofeedback,” Brain-Computer Interfaces 1, pp. 271–292, 2016. View at Publisher · View at Google Scholar
  • F. Pitolli, and C. Pocci, “Neuroelectric source localization by random spatial sampling,” Journal of Computational and Applied Mathematics, vol. 296, pp. 237–246, 2016. View at Publisher · View at Google Scholar
  • Hanna-Leena Halme, and Lauri Parkkonen, “Comparing Features for Classification of MEG Responses to Motor Imagery,” Plos One, vol. 11, no. 12, pp. e0168766, 2016. View at Publisher · View at Google Scholar
  • Bercovich, Goodman, Cacha, and Poznanskipp. 279–294, 2016. View at Publisher · View at Google Scholar
  • Eszter Szekely, Wendy Sharp, Ellen Leibenluft, Gustavo P. Sudre, and Philip Shaw, “Defining the neural substrate of the adult outcome of childhood ADHD: A multimodal neuroimaging study of response inhibition,” American Journal of Psychiatry, vol. 174, no. 9, pp. 867–876, 2017. View at Publisher · View at Google Scholar
  • Christoph Dinh, Lorenz Esch, Johannes Rühle, Steffen Bollmann, Daniel Güllmar, Daniel Baumgarten, Matti S. Hämäläinen, and Jens Haueisen, “Real-Time Clustered Multiple Signal Classification (RTC-MUSIC),” Brain Topography, 2017. View at Publisher · View at Google Scholar
  • Lorraine Perronnet, Anatole Lécuyer, Marsel Mano, Elise Bannier, Fabien Lotte, Maureen Clerc, and Christian Barillot, “Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task,” Frontiers in Human Neuroscience, vol. 11, 2017. View at Publisher · View at Google Scholar
  • Tao Chen, Sergey Suslov, Michael Schiek, N. Jon Shah, Stefan Van Waasen, and Jürgen Dammers, “Model-Driven Development Methodology Applied to Real-Time MEG Signal Preprocessing System Design,” Proceedings - UKSim-AMSS 11th European Modelling Symposium on Computer Modelling and Simulation, EMS 2017, pp. 28–33, 2017. View at Publisher · View at Google Scholar
  • Ivan Zubarev, and Lauri Parkkonen, “Evidence for a general performance-monitoring system in the human brain,” Human Brain Mapping, 2018. View at Publisher · View at Google Scholar
  • Halme Hanna-Leena, and Parkkonen Lauri, “Across-subject offline decoding of motor imagery from MEG and EEG,” Scientific Reports, vol. 8, no. 1, 2018. View at Publisher · View at Google Scholar
  • Giuseppe Placidi, Matteo Polsinelli, Matteo Spezialetti, Luigi Cinque, Paolo Di Giamberardino, and Daniela Iacoviello, “Self-induced emotions as alternative paradigm for driving brain–computer interfaces,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1–8, 2018. View at Publisher · View at Google Scholar
  • Nina Merkel, Michael Wibral, Gareth Bland, and Wolf Singer, “Endogenously generated gamma-band oscillations in early visual cortex: A neurofeedback study,” Human Brain Mapping, 2018. View at Publisher · View at Google Scholar
  • Yasaman Bagherzadeh, Daniel Baldauf, Dimitrios Pantazis, and Robert Desimone, “Alpha Synchrony and the Neurofeedback Control of Spatial Attention,” Neuron, 2019. View at Publisher · View at Google Scholar
  • Cristina Campi, Annalisa Pascarella, and Francesca Pitolli, “Less Is Enough: Assessment of the Random Sampling Method for the Analysis of Magnetoencephalography (MEG) Data,” Mathematical and Computational Applications, vol. 24, no. 4, pp. 98, 2019. View at Publisher · View at Google Scholar