Abstract
In Phase I, we collected data on five subjects yielding over 90% positive performance in Magnetoencephalographic (MEG) mid-and post-movement activity. In addition, a driver was developed that substituted the actions of the Brain Computer Interface (BCI) as mouse button presses for real-time use in visual simulations. The process was interfaced to a flight visualization demonstration utilizing left or right brainwave thought movement, the user experiences, the aircraft turning in the chosen direction, or on iOS Mobile Warfighter Videogame application. The BCI’s data analytics of a subject’s MEG brain waves and flight visualization performance videogame analytics were stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. In Phase II portion of the project involves the Emotiv Encephalographic (EEG) Wireless Brain–Computer interfaces (BCIs) allow for people to establish a novel communication channel between the human brain and a machine, in this case, an iOS Mobile Application(s). The EEG BCI utilizes advanced and novel machine learning algorithms, as well as the Spark Directed Acyclic Graph (DAG), Cassandra NoSQL database environment, and also the competitor NoSQL MongoDB database for housing BCI analytics of subject’s response and users’ intent illustrated for both MEG/EEG brainwave signal acquisition. The wireless EEG signals that were acquired from the OpenVibe and the Emotiv EPOC headset can be connected via Bluetooth to an iPhone utilizing a thin Client architecture. The use of NoSQL databases were chosen because of its schema-less architecture and Map Reduce computational paradigm algorithm for housing a user’s brain signals from each referencing sensor. Thus, in the near future, if multiple users are playing on an online network connection and an MEG/EEG sensor fails, or if the connection is lost from the smartphone and the webserver due to low battery power or failed data transmission, it will not nullify the NoSQL document-oriented (MongoDB) or column-oriented Cassandra databases. Additionally, NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology. In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile Applications with Objective-C and C++, however on EEG signals utilized on three subjects with the Emotiv wireless headsets and (n < 10) subjects from the OpenVibe EEG database the Variational Bayesian Factor Analysis Algorithm (VBFA) yielded below 60% performance and we are currently pursuing extending the VBFA algorithm to work in the time-frequency domain referred to as VBFA-TF to enhance EEG performance in the near future. The novel usage of NoSQL databases, Cassandra and MongoDB, were the primary main enhancements of the BCI Phase II MEG/EEG brain signal data acquisition, queries, and rapid analytics, with MapReduce and Spark DAG demonstrating future implications for next generation biometric MEG/EEG NoSQL databases.
Highlights
In PhasePhase I,I,“A “AReal-TimeReal-TimeMagnetoencephalographyMagnetoencephalography Brain-Computer Interface Using Interactive In3D3D-Visualization and the Ecosystem”, Journal of
NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology
In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile
Summary
[1], was developed with an an Visualization and the Hadoop Ecosystem”, Journal of Brain Sciences, 2015 [1], was developed with overall above above 92%. The previous tomographic MEG/EEG subject brainwave data analysis locally stored acquisitioned brainwave signals into directories in a UNIX based file system or utilizing a relational database system, such as PostgreSQL or MySQL. This proved unfruitful for real-time data acquisition for subject’s brain signals and was focused on a RAID (Redundant Array of Disks) architecture. What follows because, is a brief ifdiscussion a sensor fails, the database is not compromised, and of the history and importance of BCIs in the noninvasive procedures of MEG and EEG as uses and applications, Sharding” forranging distributed data is separated into blocks they“Replication relate to recent fromcomputing interactivewhere video the game technology to robotics and ormobile chunks and spread across distributed machines. The videogame analytics and processing were stored in the Hadoop Ecosystem demonstrated
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