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

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 datamore » 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.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1419358
Alternate Identifier(s):
OSTI ID: 1629026
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Published Article
Journal Name:
Brain Sciences
Additional Journal Information:
Journal Name: Brain Sciences Journal Volume: 5 Journal Issue: 4; Journal ID: ISSN 2076-3425
Publisher:
MDPI AG
Country of Publication:
Switzerland
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; brain-computer interface; massive data management; machine learning algorithms; magnetoencephalographic (MEG); electroencephalography (EEG); 3D visualization; Hadoop Ecosystem

Citation Formats

McClay, Wilbert, Yadav, Nancy, Ozbek, Yusuf, Haas, Andy, Attias, Hagaii, and Nagarajan, Srikantan. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem. Switzerland: N. p., 2015. Web. doi:10.3390/brainsci5040419.
McClay, Wilbert, Yadav, Nancy, Ozbek, Yusuf, Haas, Andy, Attias, Hagaii, & Nagarajan, Srikantan. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem. Switzerland. doi:https://doi.org/10.3390/brainsci5040419
McClay, Wilbert, Yadav, Nancy, Ozbek, Yusuf, Haas, Andy, Attias, Hagaii, and Nagarajan, Srikantan. Wed . "A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem". Switzerland. doi:https://doi.org/10.3390/brainsci5040419.
@article{osti_1419358,
title = {A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem},
author = {McClay, Wilbert and Yadav, Nancy and Ozbek, Yusuf and Haas, Andy and Attias, Hagaii and Nagarajan, Srikantan},
abstractNote = {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.},
doi = {10.3390/brainsci5040419},
journal = {Brain Sciences},
number = 4,
volume = 5,
place = {Switzerland},
year = {2015},
month = {9}
}

Journal Article:
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Publisher's Version of Record
DOI: https://doi.org/10.3390/brainsci5040419

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    Works referencing / citing this record:

    Less Is Enough: Assessment of the Random Sampling Method for the Analysis of Magnetoencephalography (MEG) Data
    journal, November 2019

    • Campi, Cristina; Pascarella, Annalisa; Pitolli, Francesca
    • Mathematical and Computational Applications, Vol. 24, Issue 4
    • DOI: 10.3390/mca24040098