EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
It is a mind-to-movement system that allows a quadriplegic man to control a computer using his thoughts.
The system is to help those who have lost control of their limbs, or other bodily functions such as patients with spinal cord injury to operate various gadgets such as TV, Computer, Lights, Fan etc.
It monitors brain activity in the patient and converts the intention of the user into computer commands
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
It is a mind-to-movement system that allows a quadriplegic man to control a computer using his thoughts.
The system is to help those who have lost control of their limbs, or other bodily functions such as patients with spinal cord injury to operate various gadgets such as TV, Computer, Lights, Fan etc.
It monitors brain activity in the patient and converts the intention of the user into computer commands
Hi guys this a PPT for brain gate technology which is a blooming technology in industry.Here i have explained what are req for presentation.Hope u enjoy and if u like my presentation please encourage me by fallowing,commenting and liking.If u need documentation part please comment below.
A LOW COST EEG BASED BCI PROSTHETIC USING MOTOR IMAGERY ijitcs
Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain
ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to
control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv
Cognitive Suite (i.e. the 1st framework) combined with an embedded software system (i.e. an open source
Arduino board) is able to control the hand through character input associated with the taught actions of
the suite. This system provides evidence of the feasibility of brain signals being a viable approach to
controlling the chosen prosthetic. Results are then presented in the second framework. This latter one
allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the
system, clear visual representations of the performance and accuracy are presented in the results using a
confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with
various acquisition datasets were carried out and with a critical evaluation of the results given. Finally
depending on the classification of the brain signal a Python script outputs the driving command to the
Arduino to control the prosthetic. The proposed architecture performs overall good results for the design
and implementation of economically convenient BCI and prosthesis.
This power point presentation is about connecting the brain with an external device through which the parts lost by any injuries can be restored partially.
Applying Brain Computer Interface Technology for Playing GamesDr. Amarjeet Singh
Brain Computer Interfaces are specialized systems that allows users to control computer applications using their brain waves. Initially, BCI were mostly used in medical field. But after some research and thanks to consumer-grade electroencephalography (EEG) devices, many applications and research opportunities were opened outside of the medical field. One particular area that is gaining more evidence due to the arrival consumer-grade devices is that of computer games, as it allows more user-friendly applications of BCI technology for the general public. In this report, we are going to talk about one of those games, Maze game. It will be a 2D maze, path known to the user. Using the EEG device named Neurosky Brain Wave Kit user will be able to move the avatar in order to reach the goal from the starting position.
A SMART BRAIN CONTROLLED WHEELCHAIR BASED MICROCONTROLLER SYSTEMijaia
The main objective of this paper is to build Smart Brain Controlled wheelchair (SBCW) intended for patient of Amyotrophic Lateral Sclerosis (ALS). Brain control interface (BCI) gave solutions for a patients having a low rate of data exchange, alsoby using the BCIthe user should have the ability to meditate and tension to let the signal get received. Using the BCI continuously is very much exhausted for the patients, Theproposed system is trying to give all handicapped people and ALS patients the simplest way to let them have a life at least near to the normal life. The system will mainly depend on the Electroencephalogram (EEG) signalsand also on the Electromyography (EMG) signals to put the system in command and out of command. The system will interface with user through a tablet and it will be secured by sensors and tracking system to avoid any obstacle. The proposed system is safe and easily built with lower cost compared with other similar systems.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Hi guys this a PPT for brain gate technology which is a blooming technology in industry.Here i have explained what are req for presentation.Hope u enjoy and if u like my presentation please encourage me by fallowing,commenting and liking.If u need documentation part please comment below.
A LOW COST EEG BASED BCI PROSTHETIC USING MOTOR IMAGERY ijitcs
Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain
ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to
control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv
Cognitive Suite (i.e. the 1st framework) combined with an embedded software system (i.e. an open source
Arduino board) is able to control the hand through character input associated with the taught actions of
the suite. This system provides evidence of the feasibility of brain signals being a viable approach to
controlling the chosen prosthetic. Results are then presented in the second framework. This latter one
allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the
system, clear visual representations of the performance and accuracy are presented in the results using a
confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with
various acquisition datasets were carried out and with a critical evaluation of the results given. Finally
depending on the classification of the brain signal a Python script outputs the driving command to the
Arduino to control the prosthetic. The proposed architecture performs overall good results for the design
and implementation of economically convenient BCI and prosthesis.
This power point presentation is about connecting the brain with an external device through which the parts lost by any injuries can be restored partially.
Applying Brain Computer Interface Technology for Playing GamesDr. Amarjeet Singh
Brain Computer Interfaces are specialized systems that allows users to control computer applications using their brain waves. Initially, BCI were mostly used in medical field. But after some research and thanks to consumer-grade electroencephalography (EEG) devices, many applications and research opportunities were opened outside of the medical field. One particular area that is gaining more evidence due to the arrival consumer-grade devices is that of computer games, as it allows more user-friendly applications of BCI technology for the general public. In this report, we are going to talk about one of those games, Maze game. It will be a 2D maze, path known to the user. Using the EEG device named Neurosky Brain Wave Kit user will be able to move the avatar in order to reach the goal from the starting position.
A SMART BRAIN CONTROLLED WHEELCHAIR BASED MICROCONTROLLER SYSTEMijaia
The main objective of this paper is to build Smart Brain Controlled wheelchair (SBCW) intended for patient of Amyotrophic Lateral Sclerosis (ALS). Brain control interface (BCI) gave solutions for a patients having a low rate of data exchange, alsoby using the BCIthe user should have the ability to meditate and tension to let the signal get received. Using the BCI continuously is very much exhausted for the patients, Theproposed system is trying to give all handicapped people and ALS patients the simplest way to let them have a life at least near to the normal life. The system will mainly depend on the Electroencephalogram (EEG) signalsand also on the Electromyography (EMG) signals to put the system in command and out of command. The system will interface with user through a tablet and it will be secured by sensors and tracking system to avoid any obstacle. The proposed system is safe and easily built with lower cost compared with other similar systems.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
Embedded system projects for final year BangaloreAidell2583
Final Year Engineering Projects For ECE in Bangalore. Embedded Innovation Lab is the right place for best real time final year engineering projects. EIL is not a training institute its a embedded company.You can learn new ideas and insdustrial working experience. BE/B.Tech student projects in bangalore,mechanical final year project in bangalore
Controlling Home Appliances adopting Chatbot using Machine Learning ApproachMinhazul Arefin
In the last decades, home automation becomes popular and rapidly increased artificial intelligence-based controlling systems. So, many researchers have been interested in the Internet of things so that every appliance should be autonomous. Smart home technology is one of them. It involves certain electrical and electronic systems in a building with some degree of computerized or automated control. It can control elements of our home environments (e.g. light, fans, electrical devices, and safety systems). We propose an approach that fully controlled the home appliances by chatbot technology. In our research, the system can extract the device name such as light, fan, etc using synonyms. In the device name extraction part, we use Jaro-Winkler string matching algorithms. We have also used the Naive Bayes algorithm to take command for action. Finally, a Firebase-based system connects the users and controls hardware. Our model can control the home appliances from a long distance because we used the wireless fidelity system.
Interactive mixed reality virtual assistant rendered on displays in public places to drive fan engagement
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Get the best of your documents and notes!
docX helps you learn and quickly access content of documents through an Alexa skill.
- Ask questions to Alexa about document content - Get natural text answers to any questions related to data in documents (Text/PPTs/PDFs/Photos of notes)
- Get summary of documents
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The system includes the following components:
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- Alexa skill for accessing all data - Ask Alexa for summary of documents, ask Alexa to find data in documents, ask Alexa to quiz you
The GCP application uses GCP NLP for extracting raw data from text generated by OCR. We run a simple algorithm for generating questions from text. All data is stored on Firebase and accessed by Node based API
Connect-i Artisan discovery platform for government of RajasthanJay Lohokare
Proposal for e-commerce, skills discovery, tourism development platform for Government of Rajasthan.
Based on Rajasthan stack e-mitra and Bhamashah systems
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
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Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
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A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
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In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
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Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
2. Jay Lohokare
Rohan Karhadkar
Akshay Satam
Dr. Xiaojun Bi
State University of New York at Stony Brook
Spring 2018
Human Computer Interactions course
3. Motivation
• Non invasive interface to allow users access interface with devices
through new ‘modes’ - Multi Modal system
• Physically disabled users - Access with physical motion
• End to end system allowing seamless control
• Not limiting the number of activities allowed - Control any
smartphone, any application
• Authentication along with control to ensure privacy and security
5. Existing works
EEG
• “Electroencephalography (EEG) is an electrophysiological monitoring method to
record electrical activity of the brain”. Any action, thought and emotions
generate electric signals in brains which are captured by electrodes resulting to
EEG
• Existing works focus on enabling brain-computer access for specific applications
– No universal interface developed.
• Brain waves can be used to classify as many as 10 actions with accuracy above
90% [1]
• “NeuroPhone” – iPhone interface that uses flashing images that user needs to
focus on [2]
• Samsung demoed Tablet control using flashing images based BCI [4]
• There are many systems developed that use BCI to perform various actions like
controlling robot motion [3], controlling drones [5], Typing applications [6][7].
However, all of these systems used visual stimulation (Like P300) which limits
its use to develop generic BCI interface.
• There are studies working on directly using EEG for action classification. For
example [8] uses raw EEG data with visual stimulation to allow users to play
rock-paper-scissors game.
• Many projects achieved higher classification accuracy – Needed invasive
surgeries.
6. • System keeps flashing the numbers
• Visual stimuli activates some brain waves
• These reading are recorded and mapped to the
image flashed at that moment to determine the
exact action user intends.
• This system can’t be used as a part of existing
systems (Apps, games, etc) – News interfaced
need to be designed for compatibility
• Our system provides a solution
Existing works
EEG
8. Existing works
EEG
MIT Researchers playing game using BCI
“NeuroPhone” – Smartphone BCI
using flashing images
Samsung researchers controlling Tablet
Commercial BCI systems
9. Existing works
EMG
• Electromyography (EMG) is an electrodiagnostic medicine technique for
evaluating and recording the electrical activity produced by skeletal
muscles.
• EMG provides direct signals compared to EEG for actions classification.
• EMG detects electric potential generated by muscles when a person tries
to move or preform some actions.
• “AlterEgo” achieves 92% median accuracy for a silent conversational
interface [9]
• Microsoft’s muCIs (Muscle computer interface) is another project using
EMGs to enable human computer interactions.[10]
• Another study uses EMG for controlling mouse cursor, achieving 70%
accuracy [11].
10. Our contribution
• All existing work focus on creating alternative interfaces to applications
• These interfaces provide with limited functionalities of underlying
platform
• Existing systems with conventional displays and interfaces should be
able to interface with EEG/EMG based applications
• Need for Plug and play system that can make all applications compatible
with BCI interfaces/Muscle control interfaces
• We present a system a system that can be integrated with any existing
display to interface it with brain or muscles based control.
• No need to re-develop applications to interface them with the brain
11. Approach to the
solution
• Overlay numbers of usual display interfaces
• No special application screens with limited feature
• System contains 3 parts
1. A background service that detects all possible interactions on the system,
and renders numbers on those locations – In our prototype, we used
Android’s Accessibility service that can access UI elements of the
smartphone screen in real time. The service allows us to access coordinates
of all ‘interaction elements’ on the screen (Elements that allow interactions
like Touch, long touch, swipe).
2. We then used these coordinates to render numbers on an empty android
activity, and kept updating this overlay screen.
3. We implemented a Bluetooth (BLE) service that keeps listening to
messages containing numbers (With a defined encoding). When this service
gets a number, it forwards it to the accessibility service (which maps
numbers with coordinates of elements on the screen). The service then
performs touch/swipe interaction on the corresponding coordinate.
13. Approach to the
solution
1. Accessibility service
recognize all possible
interactions
2. Accessibility service
overlays numbers on
screen
Action performed
14. Implementation
• 6 pins ( 2 ground)
• Power with 3.3V to 12V DC battery
• Current Draw: 14mA when idle, 15mA
connected and streaming data
• Simblee BLE Radio module (Arduino
Compatible)
• MCP3912 Analog Front End
• LIS2DH 3 axis Accelerometer
• MicroSD Card Slot
• Board Dimensions 2.41” x 2.41” (octagon has
1” edges)
OpenBCI Ganglion Board
15. Data capture
• We ran various experiments to find out optimal data capture
configurations for EEG
• 0.3 to 50 Hertz low pass filter – Remove noise related to breathing
and background activities
• Existing EEG data sets have high background noise as the person is
not in calm state when the experiment was conducted.
• Our initial approach involved building CNN classifier – We
observed that the signals differ from person to person. Hence, same
classifier can not be used for different people. Retraining the model
becomes imperative. Existing works support this claim.
16. Deep learning
implemented
• Literature review provides strong support to using CNNs for
classification
• We used 3 layer CNN (Inspired from MIT AlterEgo architecture)
for building the classifier
• We generated the data using Ganglion kit for training, and validated
the model using dataset available on Kaggle
• Implemented architecture – CNN classifier using Tensorflow.
Every model has to be trained for every user and then imported into
the application. Drawback is that CNNs can not be trained on
smartphones due to the low computing capabilities.
• Proposed enhancement – CNN feature extractor that will be
trained once, and will work for any user. The feature extractor will
extract features for any users and then pass on the data to a SVM
classifier which can be easily trained on any devices (Including
smartphones).
18. Train the classifier model
Training stage
Classification stage
Pre-trained classifier
Number (0-9)
Data Processing
4 Channel
(Implemented)
19. Train the feature extractor
Training stage
Classification stage
Pre-trained feature extractor
Features extracted
SVM
Number (0-9)
Data Processing
16 Channel
(Proposed
enhancement)
20. Results
• Our primary achievement – Creating a system that allows plug
and play interface to control any smartphone
• EEG Signals classification model – Weak model due to only 4
input channels available
• Still achieved significant accuracies in classification.
• However, the hardware we had can not support building end to
end working system
• The classification accuracies achieved have been reported on
next slide. With a 16 channel hardware, we could potentially
build a system that achieves a high (90% +) accuracy for 10
digits classification (As already proven in previous research)
• We also validated an authentication system using EEG as
proposed by [12] – Ensures privacy and end to end security in
the systems.
• We validated that EEG data captured from right side of the brain
gives better accuracy with predicting what number a person is
thinking.
23. Results –
Classification
accuracy
We extracted data for each digit in two batches, of 30 seconds each, at a sampling rate
of 200Hz. During this time interval, the person tried to stay in a passive state and only
thought about a particular digit.
2 Classes = 0, 1
3 Classes = 0, 1, 2
5 Classes = 0, 1, 2, 3, 4
Number of classes Data from default
position
Data from Right
hemisphere
2 92.2% 94.7%
3 76.4% 84.2%
5 41.6% 51.4%
24. Ubiquitous
computing & IoT
• Instead of existing approach of creating new custom interface
for BCI, our system will allow universal control of any devices
and any existing applications
• Users with EEG headsets can connect to any devices through
BLE and control them in a wireless manner.
• A communication standard can be set to enable control of any
appliance using codes. For example –
1 = On
2 = Off
• The benefit of such a standard is that an user can control IoT
enabled devices as well as complex systems with GUIs using the
Brain/EMG interfaces
25. Future work
• Developing the plug and play interface was primary goal of this project
• Future work can involve working on better data processing and Deep
Learning model to achieve better accuracies
• Evaluating performance of EEG classification under various human
activity conditions (Stress, Physical activities, thought, etc)
• We aim to build end to end system by using 16 channel EEG devices
(Instead of current 4 channel) to evaluate the actual integration of plug
and play interface with a real Deep learning model
• Validating “BrainSense” with EMG integration
27. References
1. https://www.independent.co.uk/news/science/read-your-mind-brain-waves-thoughts-locked-in-syndrome-toyohashi-
japan-a7687471.html
2. Andrew Campbell, Tanzeem Choudhury, Shaohan Hu, Hong Lu, Matthew K. Mukerjee, Mashfiqui Rabbi, and
Rajeev D.S. Raizada. 2010. NeuroPhone: brain-mobile phone interface using a wireless EEG headset. In
<em>Proceedings of the second ACM SIGCOMM workshop on Networking, systems, and applications on mobile
handhelds</em> (MobiHeld '10). ACM, New York, NY, USA, 3-8. DOI=http://dx.doi.org/10.1145/1851322.1851326
3. D. Wijayasekara and M. Manic, "Human machine interaction via brain activity monitoring," 2013 6th International
Conference on Human System Interactions (HSI), Sopot, 2013, pp. 103-109.
doi: 10.1109/HSI.2013.6577809
keywords: {brain-computer interfaces;electroencephalography;human computer interaction;medical robotics;mobile
robots;patient monitoring;BCI device;EEG measuring device;Emotiv EPOC headset;brain activity monitoring;brain
computer interface;differential wheeled robot;electroencephalograph;human computer interaction;human machine
interaction;mass market;mobile robot;pattern identification;sensor;Brain;Electroencephalography;Headphones;Mobile
robots;Sensors;Training;Brain Computer Interface;Differential Wheel Robot;EEG;Emotiv EPOC},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6577809&isnumber=6577789
4. https://www.technologyreview.com/s/513861/samsung-demos-a-tablet-controlled-by-your-brain/
5. http://www.cs.zju.edu.cn/~gpan/publication/2012-ubicomp-flyingbuddy2.pdf
6. https://www.sciencedirect.com/science/article/pii/S0010482516303092
7. Q. T. Obeidat, T. A. Campbell and J. Kong, "Spelling With a Small Mobile Brain-Computer Interface in a Moving
Wheelchair," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 11, pp. 2169-2179,
Nov. 2017.
doi: 10.1109/TNSRE.2017.2700025
8. https://www.seeker.com/mind-reading-computer-knows-what-youre-about-to-say-1770704180.html
9. Arnav Kapur, Shreyas Kapur, and Pattie Maes. 2018. AlterEgo: A Personalized Wearable Silent Speech Interface.
In 23rd International Conference on Intelligent User Interfaces (IUI '18). ACM, New York, NY, USA, 43-53. DOI:
https://doi.org/10.1145/3172944.3172977
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