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

Brain Sciences

Wilbert A. McClay²  Nancy Yadav²  Yusuf Ozbek²  Andy Haas¹  Hagaii T. Attias³  Srikantan S. Nagarajan⁴ 

[1] Dataura, Sierra Vista, Arizona, AZ 85635, USA; E-Mail:;Northeastern University and Lawrence Livermore National Laboratory, Boston, MA 02115, USA; E-Mails:;Golden Metallic Inc., San Francisco, CA 94147, USA; E-Mail:;Biomagnetic Imaging Laboratory, Department of Radiology, University of California at San Francisco, San Francisco, CA 94122, USA; E-Mail:

关键词: brain-computer interface;    massive data management;    machine learning algorithms;    magnetoencephalographic (MEG);    electroencephalography (EEG);    3D visualization;    Hadoop Ecosystem;   

DOI  :  10.3390/brainsci5040419

来源: mdpi

【 摘 要 】

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 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.