A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases
<p>Phase II, MongoDB MEG Brain Computer Interface Database(s).</p> "> Figure 2
<p>Phase II, magnetoencephalography brain-computer interface(s) (MEG BCI) with Apple iOS Mobile Applications stored in MongoDB and Cassandra.</p> "> Figure 3
<p>Yongwook Chae, “EYE-BRAIN INTERFACE (ERI) SYSTEM AND METHOD FOR CONTROLLING SAME”, US2018/0196511.</p> "> Figure 4
<p>University of San Francisco in California (UCSF) MEG Scanner with Superconducting Quantum Interference Device (SQUID) detectors.</p> "> Figure 5
<p>Phase I, “A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive three-dimensional 3D-Visualization and the Hadoop Ecosystem”, Journal of Brain Sciences, 2015.</p> "> Figure 6
<p>Phase I, “A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D-Visualization and the Hadoop Ecosystem”, flowchart process of BCI analytics in the Hadoop Ecosystem.</p> "> Figure 7
<p>Phase I, “A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D-Visualization and the Hadoop Ecosystem”, Pig analysis for MEG Subject performance on Warfighter.</p> "> Figure 8
<p>(<b>a</b>) Phase II, MongoDB Magnetoencephalography Brain-Computer Interface Database. (<b>b</b>) Phase II, Variational Bayesian Factor Analysis (VBFA) Machine Learning Algorithm. (<b>c</b>) Phase II, MEG Subject Brain Wave Data and VBFAgeneratorCTF training matrices in MongoDBdatabase(s). (<b>d</b>) Phase II, C code testVBFA function on MEG Subject Brainwave Data.</p> "> Figure 8 Cont.
<p>(<b>a</b>) Phase II, MongoDB Magnetoencephalography Brain-Computer Interface Database. (<b>b</b>) Phase II, Variational Bayesian Factor Analysis (VBFA) Machine Learning Algorithm. (<b>c</b>) Phase II, MEG Subject Brain Wave Data and VBFAgeneratorCTF training matrices in MongoDBdatabase(s). (<b>d</b>) Phase II, C code testVBFA function on MEG Subject Brainwave Data.</p> "> Figure 9
<p>Phase II, MongoDB Magnetoencephalography Brain-Computer Interface Database storage of MEG Subject Variational Bayesian Factor Analysis training matrices and MEG Subject Performance and Metadata.</p> "> Figure 10
<p>MEG Brainwave data acquisition in MongoDB with 12-byte BSON timestamp representing ObjectID for Epoch Trial performance for MEG Subject.</p> "> Figure 11
<p>(<b>a</b>) MEG Brainwave data acquisition in MongoDB with 12-byte BSON timestamp representing ObjectID representing Subject’s Training Matrices acquired during VBFA Machine learning algorithm training on MEG brainwaves. (<b>b</b>) MEG Brainwave data acquisition in MongoDB with 12-byte BSON timestamp representing ObjectID representing with Subject Brainwaves controlling flight of Warfighter simulation. (<b>c</b>) Nazzy Ironman Subject MEG Brain Computer Interface to Warfighter Flight Simulator iOS Mobile Applications yielding over 90% performance on MEG Subject brain signal data. (<b>d</b>) Nazzy Ironman Subject MEG Brain Computer Interface to Warfighter Flight Simulator iOS Mobile Applications stored in MongoDB databases yielding over 90% performance on Subject Data, demonstrated in <a href="#diseases-06-00089-f009" class="html-fig">Figure 9</a>, <a href="#diseases-06-00089-f010" class="html-fig">Figure 10</a> and <a href="#diseases-06-00089-f011" class="html-fig">Figure 11</a>.</p> "> Figure 11 Cont.
<p>(<b>a</b>) MEG Brainwave data acquisition in MongoDB with 12-byte BSON timestamp representing ObjectID representing Subject’s Training Matrices acquired during VBFA Machine learning algorithm training on MEG brainwaves. (<b>b</b>) MEG Brainwave data acquisition in MongoDB with 12-byte BSON timestamp representing ObjectID representing with Subject Brainwaves controlling flight of Warfighter simulation. (<b>c</b>) Nazzy Ironman Subject MEG Brain Computer Interface to Warfighter Flight Simulator iOS Mobile Applications yielding over 90% performance on MEG Subject brain signal data. (<b>d</b>) Nazzy Ironman Subject MEG Brain Computer Interface to Warfighter Flight Simulator iOS Mobile Applications stored in MongoDB databases yielding over 90% performance on Subject Data, demonstrated in <a href="#diseases-06-00089-f009" class="html-fig">Figure 9</a>, <a href="#diseases-06-00089-f010" class="html-fig">Figure 10</a> and <a href="#diseases-06-00089-f011" class="html-fig">Figure 11</a>.</p> "> Figure 12
<p>(<b>a</b>) NAZZY IronMan with Frozen Videogame & iOS Warfighter Mobile Game for Brain Computer Interface Project with Emotiv/OpenVibe Wireless electroencephalography (EEG) brain signal(s) data while using machine learning algorithms to classify brain signals in iOS videogame applications utilizing EEG brain signal data storage in NoSQL database MongoDB. (<b>b</b>) NAZZY IronMan with Frozen Project with Emotiv Wireless EEG brain signal(s) data using machine learning algorithms to classify brain signals in iOS Frozen videogame utilizing EEG brain signal data storage in NoSQL database MongoDB.</p> "> Figure 13
<p>(<b>a</b>) Emotiv EPOC Headset, Features, and Brain Computer Interface applications. (<b>b</b>) Utilization of Matlab FIR (Finite Impulse Response) & IIR (Infinite Impulse Response) Bandpass and Lowpass Filters on Wireless EEG Signals.</p> "> Figure 14
<p>Nazzy IronMan Brain Computer Interface Cloud Provider Facility with Cassandra NoSQL database(s).</p> "> Figure 15
<p>Nazzy IronMan Brain Computer Interface Cassandra Cloud Security Architecture Strategy.</p> "> Figure 16
<p>Emotiv and OpenVibe EEG Sensor Array stored in Cassandra NoSQL database.</p> "> Figure 17
<p>OpenVibe EEG Sensor Array stored in Cassandra NoSQL KEYSPACE (database) with Simple_Strategy and Replication Factor = 1.</p> "> Figure 18
<p>OpenVibe EEG Sensor Array stored in Cassandra NoSQL KEYSPACE (database) with Simple_Strategy and Replication Factor = 1 displaying primary key and all attributes for keyspace, eeg_motor_imagery_openvibe and table, eeg_1_signal Cassandra statistics.</p> "> Figure 19
<p>OpenVibe EEG Sensor Array stored in Cassandra NoSQL KEYSPACE (database) with Simple_Strategy, table, eeg_1_signal importing 317,825 rows of EEG brain signal data.</p> "> Figure 20
<p>OpenVibe EEG Sensor Array stored in Cassandra NoSQL KEYSPACE (database) with Simple_Strategy, Stimulation table, eeg_signal_1_stimulation_table importing eeg brain signal data (<span class="html-italic">e.g., time, identifier, duration</span>).</p> "> Figure 21
<p>MongoDB Brain Computer Interface Cloud Security Restraints.</p> "> Figure 22
<p>Java Tokenization of OpenVibe EEG Sensor Array inputted into MongoDB Collection utilizing db.openVibeSignal.find() queries.</p> "> Figure 23
<p>Usage of NoSQL database MongoDB for Wireless EEG Signal Storage and Retrieval with MongoDB BSON Timestamp with EEG Signal Electrode Array.</p> "> Figure 24
<p>Java Program for Emotiv and OpenVibe EEG Sensor Array Channel inserting a document into MongoDB Collection using Java class <b><span class="html-italic">BasicDBObject</span></b>.</p> "> Figure 25
<p>OpenVibe EEG Sensor Array Java Program for Brainwave Signal Stimulation Codes for time, stimulation code, and duration.</p> "> Figure 26
<p>Wireless EEG Java Stimulation Code Dictionary to input EEG signal patterns in MongoDB.</p> "> Figure 27
<p>Stimulation Codes have to match the acquired EEG signal patterns in MongoDB.</p> "> Figure 28
<p>MapReduce in MongoDB for Signal Processing and EEG data analytics.</p> "> Figure 29
<p>(<b>a</b>) iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 (Khronos Group, Beaverton, Oregon if USA, country, <a href="https://www.khronos.org/about/" target="_blank">https://www.khronos.org/about/</a>) and GLKit with the UITapGestureRecognizer class to fire a projectile. (<b>b</b>) iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit with aerial targets using the addTarget Method. (<b>c</b>) Display of iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit with aerial targets using the addTarget Method (close-up).</p> "> Figure 29 Cont.
<p>(<b>a</b>) iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 (Khronos Group, Beaverton, Oregon if USA, country, <a href="https://www.khronos.org/about/" target="_blank">https://www.khronos.org/about/</a>) and GLKit with the UITapGestureRecognizer class to fire a projectile. (<b>b</b>) iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit with aerial targets using the addTarget Method. (<b>c</b>) Display of iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit with aerial targets using the addTarget Method (close-up).</p> "> Figure 30
<p>iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit to evade or chase aerial targets.</p> "> Figure 31
<p>(<b>a</b>) iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit to evade or chase aerial targets. (<b>b</b>) iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit to evade or chase aerial targets can be interfaced to MEG Subject Brain Signal Data with over 90% classification performance. (<b>c</b>) Nazzy IronMan with Apple iOS Frozen Videogram Application can be interfaced to with MEG Subject Brain Signal Data with over 90% classification performance.</p> "> Figure 31 Cont.
<p>(<b>a</b>) iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit to evade or chase aerial targets. (<b>b</b>) iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit to evade or chase aerial targets can be interfaced to MEG Subject Brain Signal Data with over 90% classification performance. (<b>c</b>) Nazzy IronMan with Apple iOS Frozen Videogram Application can be interfaced to with MEG Subject Brain Signal Data with over 90% classification performance.</p> "> Figure 32
<p>iOS Mobile Application of Warfighter Videogame using OpenGL ES 2.0 and GLKit for online user’s game analytics and dynamic biometrics.</p> "> Figure 33
<p>Nazzy Ironman MEG/EEG (Virtual LAN) VLAN Base Unit for Security Authentication.</p> "> Figure 34
<p>MEG/EEG Cryptographic Key Authentication utilizing MEG/EEG brainwaves with Cassandra and MongoDB NoSQL databases.</p> ">
Abstract
:1. Introduction
2. UCSF MEG System
3. Phase II: Wireless EEG MongoDB & Cassandra Brain Computer Interface Databases and iOS Applications
3.1. EEG Data Acquisition and Signal Processing
Brain-Machine Interfaces
- Pilots and flight control
- Vigilance monitoring for air force, navy, or ground troop vehicles
- Clinical settings: Monitoring patient mental states and providing feedback
- Education: Improving vigilance, attention, learning, and memory
- Monitoring mental processes (“reading the mind”)
- Detecting deception (FBI, CIA, other law enforcement agencies)
- Predicting behavior
- Detecting brain-based predispositions to certain mental tendencies (the brain version of Myers-Briggs)
- Likelihood of improving with one type of training versus another
- Likelihood of performing better under specific circumstances
3.2. EEG Cassandra NoSQL Databases
3.3. Cassandra EEG Databases
Cassandra EEG Databases: KeySpaces and Column-Families
3.4. EEG MongoDB NoSQL Databases
3.5. EEG and MEG BCI Objective and iPhone Integration
3.6. MEG Subject Data BCI iOS Mobile Applications Integration
4. Conclusions
Funding
Acknowledgments
Conflicts of Interest
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McClay, W. A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases. Diseases 2018, 6, 89. https://doi.org/10.3390/diseases6040089
McClay W. A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases. Diseases. 2018; 6(4):89. https://doi.org/10.3390/diseases6040089
Chicago/Turabian StyleMcClay, Wilbert. 2018. "A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases" Diseases 6, no. 4: 89. https://doi.org/10.3390/diseases6040089