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Brain Sci. 2015, 5(4), 419-440; doi:10.3390/brainsci5040419

A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem

1
Northeastern University and Lawrence Livermore National Laboratory, Boston, MA 02115, USA
2
Dataura, Sierra Vista, Arizona, AZ 85635, USA
3
Golden Metallic Inc., San Francisco, CA 94147, USA
4
Biomagnetic Imaging Laboratory, Department of Radiology, University of California at San Francisco, San Francisco, CA 94122, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Zhong-Lin Lu
Received: 26 October 2014 / Accepted: 10 July 2015 / Published: 30 September 2015
View Full-Text   |   Download PDF [3151 KB, uploaded 29 October 2015]   |  

Abstract

Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. View Full-Text
Keywords: brain-computer interface; massive data management; machine learning algorithms; magnetoencephalographic (MEG); electroencephalography (EEG); 3D visualization; Hadoop Ecosystem brain-computer interface; massive data management; machine learning algorithms; magnetoencephalographic (MEG); electroencephalography (EEG); 3D visualization; Hadoop Ecosystem
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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. (CC BY 4.0).

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MDPI and ACS Style

McClay, W.A.; Yadav, N.; Ozbek, Y.; Haas, A.; Attias, H.T.; Nagarajan, S.S. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem. Brain Sci. 2015, 5, 419-440.

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