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Computational Intelligence and Neuroscience
Volume 2011, Article ID 327953, 7 pages
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.


To date, the majority of studies using magnetoencephalography (MEG) rely on off-line analysis of the spatiotemporal properties of brain activity. Real-time MEG feedback could potentially benefit multiple areas of basic and clinical research: brain-machine interfaces, neurofeedback rehabilitation of stroke and spinal cord injury, and new adaptive paradigm designs, among others. We have developed a software interface to stream MEG signals in real time from the 306-channel Elekta Neuromag MEG system to an external workstation. The signals can be accessed with a minimal delay (≤45 ms) when data are sampled at 1000 Hz, which is sufficient for most real-time studies. We also show here that real-time source imaging is possible by demonstrating real-time monitoring and feedback of alpha-band power fluctuations over parieto-occipital and frontal areas. The interface is made available to the academic community as an open-source resource.