This document summarizes a research paper on controlling a mobile robot using brain waves (EEG) detected by an electrode cap worn by the user. It discusses how EEG signals are analyzed to extract features related to different mental tasks. Machine learning classifiers are then used to translate the EEG features into commands to control the robot in real-time. The goal is to develop a system that can assist disabled people by controlling devices independently using only their brain activity.
Brain computer interface is a technique used to capture the emotions and thoughts of a brain activity using Electroencephalogram(EEG). So it is useful to communicate with humans.In this paper, it deals with a Neuro sky mind wave to detect the signals for physically challenged people. If the brain activity of a signal and already attained signals are matched it displayed on the PC then it converted into an audible signal.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Brain Computer Interface for User Recognition And Smart Home ControlIJTET Journal
This project discussed about a brain controlled biometric based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a biometric technology can be controlled. The intention of the project work is to develop a user recognition machine that can assist the work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
Brain computer interface is a technique used to capture the emotions and thoughts of a brain activity using Electroencephalogram(EEG). So it is useful to communicate with humans.In this paper, it deals with a Neuro sky mind wave to detect the signals for physically challenged people. If the brain activity of a signal and already attained signals are matched it displayed on the PC then it converted into an audible signal.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Brain Computer Interface for User Recognition And Smart Home ControlIJTET Journal
This project discussed about a brain controlled biometric based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a biometric technology can be controlled. The intention of the project work is to develop a user recognition machine that can assist the work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
Brain computer interface based smart keyboard using neurosky mindwave headsetTELKOMNIKA JOURNAL
In the last decade, numerous researches in the field of electro-encephalo-graphy (EEG) and brain-computer-interface (BCI) have been accomplished. BCI has been developed to aid disabled/partially disabled people to efficiently communicate with the community. This paper presents a control tool using the Neurosky Mindwave headset, which detects brainwaves (voluntary blinks and attention) to form a brain-computer interface (BCI) by receiving the system signals from the frontal lobe. This paper proposed an alternative computer input device for those disabled people (who are physically challenged) rather than the conventional one. The work suggested to use two virtual keyboard designs. The conducted experiment revealed a significant result in developing user printing skills on PCs. Encouraging results (1.55-1.8 word per minute (WPM)) were obtained in this research in comparison to other studies.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
BRAIN MACHINE INTERFACE SYSTEM FOR PERSON WITH QUADRIPLEGIA DISEASEEditor IJCATR
Brain Machine Interface (BMI) system is very
helpful technique for the disabled and handicapped
person to express their emotion and feeling to someone
else with the help of EEG Signals coming out of our
brain. As we know that, the human brain is made up of
billions of interconnected neurons about the size of a
pinhead. As neurons interact with each other, patterns
manifest as singular thoughts such as a math calculation.
As a by-product, every interaction between neurons
creates a miniscule electrical discharge, measurable by
EEG (electroencephalogram) machines. This system
enables people with severe motor disabilities to send
command to electronic devices by help of their brain
waves. These signals can be used to control any
electronic devices like mouse cursor of the computer, a
wheel chair, a robotic arm etc. The research in this area of
BCI system (or BMI) uses the sequence of 256 channel
EEG data for the analysis of the EEG signals coming out
of our brain by using tradition gel based multi sensor
system, which is very bulky and not convenient to use in
real time application. So this particular work proposes a
convenient system to analyze the EEG signals, which
uses few dry sensors as compared to the tradition gel
based multi sensor system with wireless transmission
technique for capturing the brain wave patterns and
utilizing them for their application. The goal of this
research is to improve quality of life for those with severe
disabilities.
Review:Wavelet transform based electroencephalogram methodsijtsrd
In this paper, EEG signals are the signatures of neural activities. There have been many algorithms developed so far for processing EEG signals. The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of the scalp. The electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. EEG signals are low voltage signals that are contaminated by various types of noises that are also called as artifacts. Statistical method for removing artifacts from EEG recordings through wavelet transform without considering SNR calculation is proposed Miss. N. R. Patil | Prof. S. N. Patil"Review:Wavelet transform based electroencephalogram methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11542.pdf http://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/11542/reviewwavelet-transform-based-electroencephalogram-methods/miss-n-r-patil
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
Survey of Fungal Diseases of Some Vegetables and Fruits in Aswan, EGYPTIOSR Journals
Fifteen species belonging to 9 terrestrial fungal genera were isolated from diseased fruits and vegetables on PDA media during this investigation. Aspergillus came in high incidence genera and represented by three species namely; A. flavus var colamnaris, A. niger and A. ochraceus. Another four fungal genera were came in the second position after Aspergillus and represented by two identified species these were; Acremonium, Alternaria, Fusarium and Penicillium. The remaining four fungal genera which isolated were representative by only one species were; Botryotrichum sp., Gilmaniela humicola, Mucor hiemalis and Torula sp. Solanum lycopersicum was yielded the highest number of genera and species (7 and 11, respectively). Psidium guava was yield the lowest number of fungal genera and species (1 and 1). All fungal which isolated in this investigation were screened for their ability to cellulose production on CMC agar plates within 3 days, among all tested isolates Aspergillus flavus and Fusarium proliferatum were the highest fungal isolates produced clear zone (3.65 mm) and (3.15 mm) respectively.
Brain computer interface based smart keyboard using neurosky mindwave headsetTELKOMNIKA JOURNAL
In the last decade, numerous researches in the field of electro-encephalo-graphy (EEG) and brain-computer-interface (BCI) have been accomplished. BCI has been developed to aid disabled/partially disabled people to efficiently communicate with the community. This paper presents a control tool using the Neurosky Mindwave headset, which detects brainwaves (voluntary blinks and attention) to form a brain-computer interface (BCI) by receiving the system signals from the frontal lobe. This paper proposed an alternative computer input device for those disabled people (who are physically challenged) rather than the conventional one. The work suggested to use two virtual keyboard designs. The conducted experiment revealed a significant result in developing user printing skills on PCs. Encouraging results (1.55-1.8 word per minute (WPM)) were obtained in this research in comparison to other studies.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
BRAIN MACHINE INTERFACE SYSTEM FOR PERSON WITH QUADRIPLEGIA DISEASEEditor IJCATR
Brain Machine Interface (BMI) system is very
helpful technique for the disabled and handicapped
person to express their emotion and feeling to someone
else with the help of EEG Signals coming out of our
brain. As we know that, the human brain is made up of
billions of interconnected neurons about the size of a
pinhead. As neurons interact with each other, patterns
manifest as singular thoughts such as a math calculation.
As a by-product, every interaction between neurons
creates a miniscule electrical discharge, measurable by
EEG (electroencephalogram) machines. This system
enables people with severe motor disabilities to send
command to electronic devices by help of their brain
waves. These signals can be used to control any
electronic devices like mouse cursor of the computer, a
wheel chair, a robotic arm etc. The research in this area of
BCI system (or BMI) uses the sequence of 256 channel
EEG data for the analysis of the EEG signals coming out
of our brain by using tradition gel based multi sensor
system, which is very bulky and not convenient to use in
real time application. So this particular work proposes a
convenient system to analyze the EEG signals, which
uses few dry sensors as compared to the tradition gel
based multi sensor system with wireless transmission
technique for capturing the brain wave patterns and
utilizing them for their application. The goal of this
research is to improve quality of life for those with severe
disabilities.
Review:Wavelet transform based electroencephalogram methodsijtsrd
In this paper, EEG signals are the signatures of neural activities. There have been many algorithms developed so far for processing EEG signals. The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of the scalp. The electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. EEG signals are low voltage signals that are contaminated by various types of noises that are also called as artifacts. Statistical method for removing artifacts from EEG recordings through wavelet transform without considering SNR calculation is proposed Miss. N. R. Patil | Prof. S. N. Patil"Review:Wavelet transform based electroencephalogram methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11542.pdf http://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/11542/reviewwavelet-transform-based-electroencephalogram-methods/miss-n-r-patil
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
Survey of Fungal Diseases of Some Vegetables and Fruits in Aswan, EGYPTIOSR Journals
Fifteen species belonging to 9 terrestrial fungal genera were isolated from diseased fruits and vegetables on PDA media during this investigation. Aspergillus came in high incidence genera and represented by three species namely; A. flavus var colamnaris, A. niger and A. ochraceus. Another four fungal genera were came in the second position after Aspergillus and represented by two identified species these were; Acremonium, Alternaria, Fusarium and Penicillium. The remaining four fungal genera which isolated were representative by only one species were; Botryotrichum sp., Gilmaniela humicola, Mucor hiemalis and Torula sp. Solanum lycopersicum was yielded the highest number of genera and species (7 and 11, respectively). Psidium guava was yield the lowest number of fungal genera and species (1 and 1). All fungal which isolated in this investigation were screened for their ability to cellulose production on CMC agar plates within 3 days, among all tested isolates Aspergillus flavus and Fusarium proliferatum were the highest fungal isolates produced clear zone (3.65 mm) and (3.15 mm) respectively.
m - projective curvature tensor on a Lorentzian para – Sasakian manifoldsIOSR Journals
In this paper we studied m-projectively flat, m-projectively conservative, 𝜑-m-projectively flat LP-Sasakian manifold. It has also been proved that quasi m- projectively flat LP-Sasakian manifold is locally isometric to the unit sphere 𝑆𝑛(1) if and only if 𝑀𝑛 is m-projectively flat.
A optimized process for the synthesis of a key starting material for etodolac...IOSR Journals
Abstract An optimized process developed for the synthesis of 7-ethyltryptophol, a key starting material for etodolac, a non steroidal anti- inflammatory drug. Starting from commercially available 2-ethylphenylhydrazine. HCl and dihydro furan with con. H2SO4 as a catalyst in N, N- dimethyl acetamide ( DMAc). H2O (1:1) as a solvent in 75% yield . the method is easy, inexpensive , without purification getting pure solid. The process is very clean, high yielding & high quality and operationally simple.
Keywords: Etodolac, 7-ethyl tryptophol, 2-ethyl phenyl hydrazine hydrochloride, N,N-dimethyl acetamide.
Performance Evaluation of IEEE STD 802.16d TransceiverIOSR Journals
WiMAX ("Worldwide Interoperability for Microwave Access") technology is developed to meet the
growing demand of increased data rate and accessing the internet at high speeds. 802.16 family of standards is
officially called Wireless MAN in IEEE. Orthogonal frequency division multiplexing (OFDM) is multicarrier
modulation technique used in IEEE 802.16d (fixed WiMAX) communication standard. OFDM is used to
increase data rate of wireless medium with higher spectral efficiency. The proposed work is to evaluate
performance of IEEE Std 802.16d transceiver in MATLAB R2009b simulink environment. System performance
evaluated using BER vs SNR for different modulation technique such as 4 QAM, 16 QAM, 64 QAM under
different channel condition
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
The mind-to-movement system that allows a quadriplegic man to control a computer using only his thoughts is a scientific milestone. It was reached, in large part, through the brain gate system. This system has become a boon to the paralyzed. The Brain Gate System is based on Cyber kinetics platform technology to sense, transmit, analyze and apply the language of neurons. The principle of operation behind the Brain Gate System is that with intact brain function, brain signals are generated even though they are not sent to the arms, hands and legs.The signals are interpreted and translated into cursor movements, offering the user an alternate Brain Gate pathway to control a computer with thought,just as individuals who have the ability to move their hands use a mouse. The 'Brain Gate' contains tiny spikes that will extend down about one millimetre into the brain after being implanted beneath the skull,monitoring the activity from a small group of neurons.It will now be possible for a patient with spinal cord injury to produce brain signals that relay the intention of moving the paralyzed limbs,as signals to an implanted sensor,which is then output as electronic impulses. These impulses enable the user to operate mechanical devices with the help of a computer cursor. Matthew Nagle,a 25-year-old Massachusetts man with a severe spinal cord injury,has been paralyzed from the neck down since 2001.After taking part in a clinical trial of this system,he has opened e-mail,switched TV channels,turned on lights
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.
This presentation shows the detail knowledge about EEG. It contains slides with animation. You can build your own concept to explain the slide.
Best view in 16:9 ratio.
System Architecture for Brain-Computer Interface based on Machine Learning an...ShahanawajAhamad1
Brain functions are required to be read for curing
neurological illness. Brain-Computer Interface (BCI) connects
the brain to the digital world for brain signals receiving,
recording, processing, and comprehending. With a BrainComputer Interface (BCI), the information from the user’s brain
is fed into actuation devices, which then carry out the actions
programmed into them. The Internet of Things (IoT) has made it
possible to connect a wide range of everyday devices.
Asynchronous BCIs can benefit from an improved system
architecture proposed in this paper. Individuals with severe
motor impairments will particularly get benefit from this feature.
Control commands were translated using a rule-based
translation algorithm in traditional BCI systems, which relied
only on EEG recordings of brain signals. Examining BCI
technology’s various and cross-disciplinary applications, this
argument produces speculative conclusions about how BCI
instruments combined with machine learning algorithms could
affect the forthcoming procedures and practices. Compressive
sensing and neural networks are used to compress and
reconstruct ECoG data presented in this article. The neural
networks are used to combine the classifier outputs adaptively
based on the feedback. A stochastic gradient descent solver is
employed to generate a multi-layer perceptron regressor. An
example network is shown to take a 50% compression ratio and
89% reconstruction accuracy after training with real-world,
medium-sized datasets as shown in this paper
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
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https://arxiv.org/abs/2306.08302
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https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
F1102024349
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 11, Issue 2 Ver.II (Mar. – Apr. 2016), PP 43-49
www.iosrjournals.org
DOI: 10.9790/1676-1102024349 www.iosrjournals.org 43 | Page
EEG-Based Brain-Controlled Mobile Robot
Mrs. Gomathy S.1
, Anistomon Joseph 2
, Sebin D.3
, Vivek Kumar V.4
1
Associate Professor, Department of Electrical and Electronics, Adi Shankara Institute of Engineering and
Technology, Kerala, India
2
Department of Electrical and Electronics, Adi Shankara Institute of Engineering and Technology, Kerala,
India
3
Department of Electrical and Electronics, Adi Shankara Institute of Engineering and Technology, Kerala,
India
4
Department of Electrical and Electronics, Adi Shankara Institute of Engineering and Technology, Kerala,
India
Abstract: This project discusses about a brain controlled robot based on Brain Computer Interfaces (BCI).
BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to
provide direct communication and control between the human brain and physical devices by translating
different patterns of brain activity into commands in real time. With these commands a mobile robot can be
controlled. The intention of the project work is to develop a robot that can assist the disabled people in their
daily life to do some work independent of others. Here, we analyze the brain wave signals. Human brain
consists of millions of interconnected neurons. The pattern of interaction between these neurons is represented
as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn
produce different electrical waves. Muscle contraction also generates unique electrical signals. All these
electrical waves will be sensed by the brain wave sensor and converts the data into packets and transmits
through Bluetooth medium. The brain wave raw data is sent to the computer and it will extract and process the
signal using MATLAB platform. Then the control commands will be transmitted to the robot module to process.
With this entire system, we can move a robot based on the human thoughts and it can be turned by blink muscle
contraction.
Key Words: Brain-computer interface (BCI), brain controlled mobile robot, EEG, human factors, performance
evaluation, shared control.
I. Introduction
The main aim of this project is to control a device based on electrical signals of brain. Brain Computer
Interface (BCI) is a communication system, which enables the user to control special computer applications by
using only his or her thoughts. Almost all of the research work on BCI are based on electroencephalography
(EEG) recorded from the scalp. EEG is measured and sampled as the user imagines different things (for
example, moving an arm). Different preprocessing and feature extraction methods are applied to the EEG
sample of certain length depending on the type of BCI. The task-specific EEG signals or patterns are then
detected from the EEG samples. BCI research was first started in 1960’s, but it was in 1990’s when it got
serious. So far, over 20 BCI research groups have taken various approaches to the subject. Some of the BCI
research groups have built an online BCI, which can give feedback to the subject. Despite the recent
technological developments numerous problems still exist in building efficient BCIs. The biggest challenges are
related to accuracy, speed and usability. However, BCI could provide a new communication tool for people
suffering from so called locked-in syndrome where they are completely paralyzed physically and unable to
speak, but cognitively intact and alert.
II. Brain-Computer Interfaces (BCIS)
A brain-computer interface can be defined as a communication system that does not depend on the
brains normal output pathways of peripheral nerves and muscles i.e., a BCI should be able to detect the user’s
wishes and commands while the user is silent and immobilized. The brain activity must be monitored for this
using various techniques.
EEG recordings from these techniques give continuous and instantaneous recordings of the brain
activity (time resolution about 1 ms), which is required for real-time BCI.
Once the EEG of a user has been recorded, the BCI must then detect the user’s commands from the
EEG. Two main approaches are followed in achieving this. In the first approach the subject concentrates on a
few mental tasks (for example, imagining an arm movement). Concentrations on these mental tasks produce
different EEG patterns. The BCI can then be trained to classify these patterns. In the second approach called the
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DOI: 10.9790/1676-1102024349 www.iosrjournals.org 44 | Page
operant conditioning approach the user has to learn to self-regulate his or her EEG response (for example
change the rhythm amplitude). Unlike in the pattern recognition approach, the BCI itself is not trained but it
looks for particular changes (such as higher amplitude of a certain frequency) in the EEG signal. This requires
usually a long training period, because the entire training load is on the user. According to Allison [1] there are
at least five components necessary for effective BCI system: 1) Knowing what to look for; 2) Knowing the
relevant physiological signals; 3) Gathering the data from the user; 4) Extracting useful information from the
raw signal; 5) Interface design. The BCI can classify two mental tasks and provides feedback in the form of
cursor control. It has also ―reject‖ option, if the probability of the classification does not exceed some predefined
level. The purpose of this chapter is to explain the concept of the BCI. First, the other part of the interface, the
human brain, is examined. Then, the basic principles of electroencephalography (EEG) are explained. BCIs are
divided into two above mentioned approaches. Then, the EEG measurement and the components of BCI system
are defined. Feedback, human training issues and BCI performance measurement are explained after that.
2.1 Electroencephalography (EEG)
This is a method used in measuring the electrical activity of the brain. Brain electrical activity is
generated by billions of neurons (nerve cells). Each of these neurons is connected to thousands of other neurons.
All the signals from other neurons sum up in the receiving neuron and when this sum exceeds a certain potential
level, the neuron fires nerve impulse. EEG can measure the combined electrical activity of millions of neurons.
An EEG is characterized by its amplitude and frequency. The amplitudes of the EEG signals typically vary
between 10 and 100 V (10 and 50 V in adults). The electrical activity goes on continuously in every living
human’s brain without rest. The brain remains active even when one is unconscious. Allison [1] lists four
prerequisites, which must be met for the activity of any network of neurons to be visible in EEG signal: 1) The
neurons must generate most of their electrical signals along a specific axis oriented perpendicular to the scalp; 2)
The neuronal dendrites must be aligned in parallel so that their field potentials summate to create a signal which
is detectable at a distance; 3) The neurons should fire in near synchrony; 4) The electrical activity produced by
each neuron needs to have the same electrical sign. Various properties in EEG can be used as a basis for a BCI:
1. Rhythmic brain activity
2. Event-related potentials (ERPs)
3. Event-related desynchronization (ERD) and event-related synchronization (ERS).
Band Frequency [Hz]
Delta (_) < 3.5
Theta (_) 4-7.5
Alpha (_) 8-13
Beta (_) >13
Rhythmic brain activity:
EEG rhythms are associated with various physiological and mental processes. The alpha rhythm is the
principal resting rhythm of the brain, and is common in wakeful, resting adults. Auditory and mental arithmetic
tasks with the eyes closed lead to strong alpha waves, which are suppressed when the eyes are opened (by a
visual stimulus).
The alpha wave is replaced by slower rhythms at various stages of sleep. Theta waves appear at the
beginning stages of sleep. Delta waves appear at deep-sleep stages. High-frequency beta waves appear as
background activity in tense and anxious subjects. The absence of the normal (expected) rhythm in a certain
state of the subject could indicate abnormality. The presence of delta or theta (slow) waves in a wakeful adult
would be considered to be abnormal. Focal brain injury and tumors lead to abnormal slow waves in the
corresponding regions. Unilateral depression (left - right asymmetry) of a rhythm could indicate disturbances in
cortical pathways. Spikes and sharp waves could indicate the presence of epileptogenic regions in the
corresponding parts of the brain [2].
Event-related potentials (ERPs)
ERP is a common title for the potential changes in the EEG that occur in response to a particular
―event‖ or a stimulus. These changes are so small that in order to reveal them, EEG samples have to be averaged
over many repetitions. The ―random‖ fluctuations of the EEG, which are not stimulus-locked, get removed as a
result of this. The most commonly studied ERP is P300. Positive deflection in the EEG occurs about 300 ms
after the stimulus onset. P300 is commonly recorded during an ―odd-ball paradigm‖. In it the subject has been
told to respond to a rare stimulus, which occurs randomly and infrequently among the other, frequent stimuli.
Evoked potentials (EPs) is a subset of the ERPs, that rise in response to a certain physical (visual, auditory,
somatosensory etc.) stimulus. A typical evoked potential is the Visual evoked potential (VEP) that reflects the
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DOI: 10.9790/1676-1102024349 www.iosrjournals.org 45 | Page
output features of the entire visual pathway. The EEG over the visual cortex varies at the same frequency as the
stimulating light.
Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS)
ERD and ERS can be defined as follows:
1. ERD is an amplitude attenuation of a certain EEG rhythm.
2. ERS is an amplitude enhancement of a certain EEG rhythm.
In order to measure an ERD or an ERS, the power of a certain frequency band (for example, 8-12 Hz)
is calculated before and after certain ―event‖ over a number of EEG trials. The event can be externally-paced
(such as light stimulus) or internally paced (such as voluntary finger movement). The power (averaged over a
number of trials) is then measured in percentage relative to the power of the reference interval. The reference
interval is defined, for example, as 1 second interval between 4.5 and 3.5 seconds before the event (i.e. during
the rest). The ERS is the power increase (in percents) and the ERD is the power decrease relative to the
reference interval (which is defined as 100 %). To keep the power at the reference interval at the resting level,
the interval between two consecutive events should be random and not shorter than a few seconds.
2.2 BCI approaches
An ideal BCI could detect the user’s wishes and commands directly. However, this is not possible with
today’s technology. Therefore, BCI researches have used the knowledge they have had of the human brain and
the EEG in order to design a BCI. There are basically two different approaches that have been used. The first
one called a pattern recognition approach which is based on cognitive mental tasks. The second one is called an
operant conditioning approach and is based on the self-regulation of the EEG response.
2.3 EEG Measurement
The recording of the EEG signals is performed by fixing an electrode on the subject scalp (Fig-2.)
using the standardized electrode placement scheme.
The subject is asked to wear an Electrode Cap through which the signals are acquired. Several channels
of the EEG are recorded simultaneously from various locations on the scalp for comparative analysis of
activities in different regions of the brain. The International Federation of Societies for Electroencephalography
and Clinical Neurophysiology has recommended the 10 — 20 system of electrode placement for clinical EEG
recording, which is schematically illustrated in Fig- 1. The name 10 — 20 indicate the fact that the electrodes
along the midline are placed at 10, 20, 20, 20, 20 and 10% of the total nasion - inion distance; the other series of
electrodes are also placed at similar fractional distances of the corresponding reference distances. The inter-
electrode distances are equal along any antero-posterior or transverse line, and electrode positioning is
symmetrical. EEG signals may be used to study the nervous system, monitoring of sleep stages, biofeedback and
control, and diagnosis of diseases such as epilepsy [2].
Fig- 1: The 10 — 20 system of electrode placement
Notes regarding channel labels: pg - naso-pharyngeal, a - auricular (ear lobes), fp - prefrontal , f -
frontal, p – parietal, c - central, o - occipital, t – temporal, cb – cerebellar, z – midline, odd numbers on the left,
even numbers on the right of the subject [2].
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Fig- 2: A Subject wearing an electrode cap
Reference and bipolar recordings
The EEG recordings can be divided into two major categories: Reference recordings and scalp-to-scalp
bipolar linkages. In the reference recording each electrode is referred to either distant reference electrode, one
common electrode on each side of the head or to combined activity of two or more electrodes. The reference
electrode(s) must be placed on the parts of the body where potential remains fairly constant. Usually reference
electrodes are placed on the ear lobes or on the mastoid bones behind the ear. In addition to one single reference
electrode two reference electrodes shorted together can be used. In bipolar recordings differential measurements
are made between successive pairs of electrodes.
2.4 BCI Components
A typical BCI device consists of several components. These include electrode cap, EEG amplifiers,
computer and subject’s screen. A critical issue is how the user’s commands, i.e., the changes in the EEG, are
converted to actions on the feedback screen or the application. This process can be divided into five stages:
1) Measurement of EEG
This is done by using the electrodes. Many BCIs use a special electrode cap, in which the electrodes
are already in the right places, typically according to the international 10-20 system [2] (see section 2.3.). It
saves time because the electrodes do not have to be attached one by one. Typically, less than 10 electrodes are
used in online BCIs with sampling rates of 100-400 Hz.
2) Preprocessing
This includes amplification, initial filtering of EEG signal and possible artifact removal. Also A/D
conversion is made, i.e. the analog EEG signal is digitized.
3) Feature extraction
In this stage, certain features are extracted from the preprocessed and digitized EEG signal. In the
simplest form a certain frequency range is selected and the amplitude relative to some reference level measured.
Typically the features are certain frequency bands of a power spectrum. The power spectrum (which describes
the frequency content of the EEG signal) can be calculated using, for example, Fast Fourier Transform (FFT),
the transfer function of an autoregressive (AR) model or wavelet transform [5]. No matter what features are
used, the goal is to form distinct set of features for each mental task. If the feature sets representing mental tasks
overlap each other too much, it is very difficult to classify mental tasks, no matter how good a classifier is used.
On the other hand, if the feature sets are distinct enough, any classifier can classify them.
4) Classification
A variety of classifiers have been used to translate these extracted features from EEG signals into an
output command, from simple classifiers such as nearest neighbor, linear discriminant analysis (LDA), to
nonlinear neural networks (NN), support vector machines (SVM), and statistical classifiers [3].
LDA is a widely used linear classifier. Compared with the other methods, the main advantages of LDA
include the following: 1) It is simple to use and 2) it has low computational complexity. Thus, numerous brain-
controlled mobile robots used LDA to develop the classifiers of BCI systems. Artificial neural network (ANN)
is a widely used nonlinear modeling method for regression analysis and classification, which is based on
biological neural networks. The main advantage of ANN as a classification method is its ability to approximate
arbitrary nonlinear decision functions by minimizing the error in classifying training data. Unlike ANN, SVM
does not need to set up many configurations and parameters [3]. Another advantage of SVM is that it has good
generalization characteristics and is especially suitable for the cases, where a small amount of training data is
gained. In addition, the two kinds of classifiers were widely applied into brain-controlled mobile robots [3].
Statistical classifiers classify one new instance into a particular class by selecting the highest one from
the estimated posterior probabilities of all classes based on observed features of the new instance and prior
knowledge. The main advantage of statistical classifiers is that it can represent the uncertainty of EEG signals. It
has been applied into brain-controlled mobile robots [6], [7]. However, the robustness of all existing BCI
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systems is not satisfactory due to the non stationary nature of noninvasive EEG signals. Considering that the
natural change of brain signals over time and the change of brain activity patterns since the users develop new
capabilities as subjects gain experience, Mill´an proposed that a possible research direction to improve the
robustness is the online adaptation of the classifier during its use to drifts in the brain signals, and preliminary
results have shown the feasibility and advantage of this method [8]. In addition, there are a few software tools
that are widely used to process the EEG data such as EEGLAB [9] and BCI 2000 [10], which can help
researchers develop brain-controlled mobile robots. More details of classifiers, applications, and issues
regarding BCI systems can be seen in several reviews of BCI [1], [11], [12], [13], [14]–[18], [19].
5) Device control
The classifier’s output is the input for the device control. The device control simply transforms the
classification to a particular action. The action can be, e.g., an up or down movement of a cursor on the feedback
screen or a selection of a letter in a writing application. However, if the classification was ―nothing‖ or ―reject‖,
no action is performed, although the user may be informed about the rejection.
Fig- 3: Schematic of the main BCI components [3]
III. Actual System
Actual system can be mainly divided into 3 stages. In the first stage, a brain sensor with the help of
EEG, acquires the raw data (signals) from the brain and transmits it in the form of packets via Bluetooth to the
next stage the processing unit. The signals are processed here and then sent to the robotic module through serial
data transmission using a ZIGBEE module. Based on the data and the commands received through the ZIGBEE
module, the robot performs actions accordingly. MpLab software was used to program the PIC microcontroller
used to communicate with the robot.
Fig- 4: Block Diagram-1
Fig-4 shows the first stage of the system (Data acquisition) and Fig- 5 shows the next two stages of the
system i.e., Data processing and Robotic module.
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Fig- 5: Block Diagram-2
IV. Simulation And Results
Simulation showing the working of the robot was developed using KIEL and Proteus softwares. The
on-off switch represents 5 eye blinks which activates the system. Once the system has been activated it works
based on the input given. The FRONT, BACK switch is provided such that the motors (representing the
wheelchair) move forward when it is high and backwards when it is low. The SIDE MOTION switch is
provided to activate right and left motions. Once this switch is high, then based on the position of the RIGHT,
LEFT switch, the side motions take place. Right side motion occurs when the switch is high and left side
motion occurs when the switch is low. The variable resistance represents the ultrasonic sensor (for obstacle
detection). When it is high, it indicates that an obstacle has been detected and the motors either stop or change
their directions. The lcd displays the different actions performed by the motor based on the input commands
received.
Fig- 6: Simulation Diagram
The following table shows the different comments the lcd displays based on the actions performed by
the motor obtained depending on the input commands received in the simulation.
Table-1: Simulation Results
Switch
Position
FRONT,
BACK
SIDE
MOTION
RIGHT,
LEFT
VARIABLE
RESISTANCE
HIGH FRONT Activated
RIGHT if
high.
LEFT if
low
Obstacle Detected
LOW BACK
Not
Activated
Not
Activated
No Obstacle
Detection
V. Conclusions
Brain controlled mobile robots have been receiving a lot of attention due to its immense scope in the
medical areas. The signals received from the brain can be used conveniently to control a robot and make it
perform the actions we desire. These robots may also find applications in defense as well.