This document describes a brain-controlled robot system using EEG signals. The system uses EEG electrodes placed on the scalp to measure brain wave activity. Different patterns of brain waves can be translated into commands to control a mobile robot in real time. The goal is to develop a robot that can assist disabled people and allow them to move independently without physical movement. The system works by analyzing EEG signals through techniques like fast Fourier transforms to separate different brain wave frequencies associated with different mental states and intentions. This allows the user to think of commands to direct the robot's movement. The system aims to improve quality of life for people with disabilities.
A brain-computer interface sometimes called a direct neural interface or a brain-machine interface is a direct communication pathway between a human or animal brain(or brain cell culture) and an external device. In one BCIs, computers either accept commands from the brain or send signals to it but not both. Two-way BCIs will allow brains and external devices to exchange information in both directions but have yet to be successfully implanted in animals or humans.
2. Partially Invasive BCI: Partially invasive BCI devices are implanted inside the skull but rest outside the brain rather than amidst the grey matter. They produce better resolution signals than non-invasive BCIs where the bone tissue of the cranium deflects and deforms signals and have a lower risk of forming scar tissue in the brain than fully-invasive BCIs.
3. Non-Invasive BCI: Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) have both been used successfully as non-invasive BCIs.
There are three types of BCI
1. Inversive BCI: - Invasive BCI is directly implanted into the grey matter of the brain during neurosurgery. They produce the highest quality signals of BCI devices. Invasive BCIs has targeted repairing damaged sight and providing new functionality to paralysed people.
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.
A brain-computer interface sometimes called a direct neural interface or a brain-machine interface is a direct communication pathway between a human or animal brain(or brain cell culture) and an external device. In one BCIs, computers either accept commands from the brain or send signals to it but not both. Two-way BCIs will allow brains and external devices to exchange information in both directions but have yet to be successfully implanted in animals or humans.
2. Partially Invasive BCI: Partially invasive BCI devices are implanted inside the skull but rest outside the brain rather than amidst the grey matter. They produce better resolution signals than non-invasive BCIs where the bone tissue of the cranium deflects and deforms signals and have a lower risk of forming scar tissue in the brain than fully-invasive BCIs.
3. Non-Invasive BCI: Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) have both been used successfully as non-invasive BCIs.
There are three types of BCI
1. Inversive BCI: - Invasive BCI is directly implanted into the grey matter of the brain during neurosurgery. They produce the highest quality signals of BCI devices. Invasive BCIs has targeted repairing damaged sight and providing new functionality to paralysed people.
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.
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.
A BRIEF INTRODUCTION TO THE BRAIN-COMPUTER INTERFACES
A brain-computer interface (BCI) is a hardware and software system that allows brain activity alone to control computers or external devices. In this short talk, I will review the state-of-the-art of BCIs, looking at the different steps that form a brain-computer interface: signal acquisition, preprocessing or enhancement, feature extraction and classification and the control interface. We will discuss different BCI types their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported so far (including commercially available devices).
At the first part, I will introduce basics of the Brain functioning and will review the neuroimaging modalities used in the signal acquisition, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Then, we will discuss different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Finally, we will overview various BCI applications that control a range of devices and discuss neurofeedback technology for medical, learning and gaming applications.
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.
It consists of all details about BCI which are necessary, I sorted from net and implemented in PPT. For abstract U can mail me koushik.veldanda@gmail.com
(It is not my own talent,it is a collaboration of 4 to 5 PPT's , wiki and other sites.
But simply awesome )
As the power of modern computers grows alongside our understanding of the human brain, we move ever closer to making some pretty spectacular science fiction into reality. Imagine transmitting signals directly to someone's brain that would allow them to see, hear or feel specific sensory inputs. Consider the potential to manipulate computers or machinery with nothing more than a thought. It isn't about convenience, for severely disabled people, development of a brain-computer interface (BCI) could be the most important technological breakthrough in decades.
A Brain-computer interface, sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a brain and an external device. It is the ultimate in development of human-computer interfaces or HCI. BCIs being the recent development in HCI there are many realms to be explored. After experimentation three types of BCIs have been developed namely Invasive BCIs, Partially-invasive BCIs, Non-invasive BCIs.
Catalan Tau Collocation for Numerical Solution of 2-Dimentional Nonlinear Par...IJERA Editor
Tau method which is an economized polynomial technique for solving ordinary and partial differential
equations with smooth solutions is modified in this paper for easy computation, accuracy and speed. The
modification is based on the systematic use of „Catalan polynomial‟ in collocation tau method and the
linearizing the nonlinear part by the use of Adomian‟s polynomial to approximate the solution of 2-dimentional
Nonlinear Partial differential equation. The method involves the direct use of Catalan Polynomial in the solution
of linearizedPartial differential Equation without first rewriting them in terms of other known functions as
commonly practiced. The linearization process was done through adopting the Adomian Polynomial technique.
The results obtained are quite comparable with the standard collocation tau methods for nonlinear partial
differential equations.
Reliability Based Optimum Design of a Gear BoxIJERA Editor
The gear box represents an important mechanical sub system. In machine tools, the propose of a gear box is to provide a series of useful output speeds so that the machining operation can be carried out at its most optimum operating conditions high spindle speeds with low feed rate for roughing operations. An important aspect in the design of machine tool transmission is to keep the cost and volume of the gear box to a minimum. The probabilistic approach to design has been considered to be more rational compared to the conventional design approach based on the factor of safety. The existence of uncertainties in either engineering simulations or manufacturing processes calls for a reliability-based design optimization (RBDO) model for robust and cost-effective designs. In the present work a three shaft four speed gear box is designed using reliability principles. For the specified reliability of the system (Gear box), component reliability (Gear pair) is calculated by considering the system as a series system. Design is considered to be safe and adequate if the probability of failure of gear box is less than or equal to a specified quantity in each of the two failure modes. A FORTRAN program has been developed to calculate the mean values of face widths of gears for the minimum mass of gear box. By changing the probability of failure of system variations in the face widths are studied. The reliability based optimum design results are compared with those obtained by deterministic optimum design. The minimum mass of the gear box is increase as the specified values of the reliability is increased.
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.
A BRIEF INTRODUCTION TO THE BRAIN-COMPUTER INTERFACES
A brain-computer interface (BCI) is a hardware and software system that allows brain activity alone to control computers or external devices. In this short talk, I will review the state-of-the-art of BCIs, looking at the different steps that form a brain-computer interface: signal acquisition, preprocessing or enhancement, feature extraction and classification and the control interface. We will discuss different BCI types their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported so far (including commercially available devices).
At the first part, I will introduce basics of the Brain functioning and will review the neuroimaging modalities used in the signal acquisition, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Then, we will discuss different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Finally, we will overview various BCI applications that control a range of devices and discuss neurofeedback technology for medical, learning and gaming applications.
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.
It consists of all details about BCI which are necessary, I sorted from net and implemented in PPT. For abstract U can mail me koushik.veldanda@gmail.com
(It is not my own talent,it is a collaboration of 4 to 5 PPT's , wiki and other sites.
But simply awesome )
As the power of modern computers grows alongside our understanding of the human brain, we move ever closer to making some pretty spectacular science fiction into reality. Imagine transmitting signals directly to someone's brain that would allow them to see, hear or feel specific sensory inputs. Consider the potential to manipulate computers or machinery with nothing more than a thought. It isn't about convenience, for severely disabled people, development of a brain-computer interface (BCI) could be the most important technological breakthrough in decades.
A Brain-computer interface, sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a brain and an external device. It is the ultimate in development of human-computer interfaces or HCI. BCIs being the recent development in HCI there are many realms to be explored. After experimentation three types of BCIs have been developed namely Invasive BCIs, Partially-invasive BCIs, Non-invasive BCIs.
Catalan Tau Collocation for Numerical Solution of 2-Dimentional Nonlinear Par...IJERA Editor
Tau method which is an economized polynomial technique for solving ordinary and partial differential
equations with smooth solutions is modified in this paper for easy computation, accuracy and speed. The
modification is based on the systematic use of „Catalan polynomial‟ in collocation tau method and the
linearizing the nonlinear part by the use of Adomian‟s polynomial to approximate the solution of 2-dimentional
Nonlinear Partial differential equation. The method involves the direct use of Catalan Polynomial in the solution
of linearizedPartial differential Equation without first rewriting them in terms of other known functions as
commonly practiced. The linearization process was done through adopting the Adomian Polynomial technique.
The results obtained are quite comparable with the standard collocation tau methods for nonlinear partial
differential equations.
Reliability Based Optimum Design of a Gear BoxIJERA Editor
The gear box represents an important mechanical sub system. In machine tools, the propose of a gear box is to provide a series of useful output speeds so that the machining operation can be carried out at its most optimum operating conditions high spindle speeds with low feed rate for roughing operations. An important aspect in the design of machine tool transmission is to keep the cost and volume of the gear box to a minimum. The probabilistic approach to design has been considered to be more rational compared to the conventional design approach based on the factor of safety. The existence of uncertainties in either engineering simulations or manufacturing processes calls for a reliability-based design optimization (RBDO) model for robust and cost-effective designs. In the present work a three shaft four speed gear box is designed using reliability principles. For the specified reliability of the system (Gear box), component reliability (Gear pair) is calculated by considering the system as a series system. Design is considered to be safe and adequate if the probability of failure of gear box is less than or equal to a specified quantity in each of the two failure modes. A FORTRAN program has been developed to calculate the mean values of face widths of gears for the minimum mass of gear box. By changing the probability of failure of system variations in the face widths are studied. The reliability based optimum design results are compared with those obtained by deterministic optimum design. The minimum mass of the gear box is increase as the specified values of the reliability is increased.
A Comparative Study on Compressive and Flexural Strength of Concrete Containi...IJERA Editor
Concrete is the most widely used material in the world today. This paper is about the comparative study of the flexural strength and compressive strength of concrete when different admixtures are used as partial replacement of cement in the concrete mix. The mineral admixtures that are used here are Silica Fume, Rice Husk Ash and Iron slag as partial replacement of cement. All these materials are industrial waste products and are abundantly available nowadays. These materials have high silica content and pozzolanic properties and can be effectively used as a replacement of cement during the formation of High Performance Concrete. Compressive and Flexural strength are the two most important characteristic of concrete and are calculated for the hardened concrete to analyze the load bearing capacity for design purposes. Thus for the effective judgment of type of mineral admixtures to be used a comparative study is very useful.
Seismic Performance Evaluation of Multi-Storeyed R C Framed Structural System...IJERA Editor
Masonry infills are normally considered as non-structural elements and their stiffness contributions are generally ignored in practice. But they affect both the structural and non-structural performance of the RC buildings during earthquakes. RC frame building with open first storey is known as soft storey, which performs poorly during strong earthquake shaking. A similar soft storey effect can also appear at top storey level if a storey used as a service storey. Hence a combination of two structural system components i.e. Rigid frames and RC shear walls leads to a highly efficient system in which shear wall resist the majority of the lateral loads and the frame supports majority of the gravity loads. To study the effect of masonry infill and different soft storey level, 11 models of R C framed building were analyzed with two types of shear wall when subjected to earthquake loading. The results of bare frame and other building models have been compared, it is observed that model with swastika and L shape shear wall with core wall are showing efficient performance and hence reducing the effect of soft storey and also reducing the effect of water pressure in the top soft storey.
Hankel Determinent for Certain Classes of Analytic FunctionsIJERA Editor
Let 1 A denote the class of functions
2
( )
n
n
n f z z a z analytic in the unit disc E {z : |z| <1}.
M denotes the class of functions in 1 A which satisfy the conditions 0
( ). ( )
z
f z f z
and for
0 1, 0
( )
( ( ))
( )
( )
) 1 ( Re
f z
zf z
f z
zf z
. We are interested in determining the sharp upper bound
for the functional
2
2 4 3 a a a for the class M .
Physico-Chemical Characteristics Of Water Of River Mandakini In Chitrakoot Re...IJERA Editor
The river flows in Madhya Pradesh for about 25km, then makes a border of district Satna (Madhya Pradesh) and district Chitrakoot (Uttar Pradesh) for the next 25km and again enters in Madhya Pradesh just downstream of Sati Anusuiya. After flowing through about 15km more in M.P., it crosses into Uttar Pradesh near Ramghat in Chitrakoot area and later flows only in Uttar Pradesh finally it joins river Yamuna near Rajapur. The present research works identify Physico - Chemical Characteristics of water quality of River Mandakini in Chitrakoot Region. The water samples were analysed some parameter like pH, TDS, TSS, TH, Alkalinity, DO, BOD, COD, Nitrate, and Sulphate. The pH value was found between 7.49 to 8.5, TDS 290 to 470mg/l, TSS 140 to 192mg/l, TH 250 to 288mg/l, Alkalinity 175 to 198mg/l, DO 3.19 to 6.5mg/l, BOD 2.5 to 12 mg/l, COD 10 to 38mg/l, Nitrate 3 to 9mg/l, Sulphate 3 to 8mg/. Most of the sample BOD and COD are higher than the permissible limit prescribed by WHO (1994) as 6mg/l and 10mg/l respectively.TDS, TSS, TH, Alkalinity, Nitrate and Sulphate of all the results below the WHO recommended values as 500mg/l, 200mg/l, 300mg/l, 200mg/l, 45mg/l, 250mg/l.
A Modified C-Dump Converter for BLDC Machine Used in a Flywheel Energy Storag...IJERA Editor
This paper presents a modified C-dump converter for brushless DC (BLDC) machine used in the flywheel energy storage system. The converter can realize the energy bidirectional flowing and has the capability to recover the energy extracted from the turnoff phase of the BLDC machine. The principle of operation, modeling, and control strategy of the system has been investigated in the paper. Simulation and experimental results of the proposed system are also presented and discussed.
Defluoridization Using a Natural Adsorbent, Strychnos PotatorumIJERA Editor
The study assessed the suitability of low-cost natural adsorbent to effectively remediate fluoride contaminated water. The removal of fluoride from aqueous solution by using Strychnos Potatorum was studied in batch technique. Influence of pH, adsorbent dose, contact time, co ions, speed and initial concentration on the adsorption were investigated. The maximum removal of fluoride ion was obtained at pH 7. The removal of fluoride was expressed with Langmuir and Freundlich isotherm. It was found that the sufficient time for adsorption equilibrium of fluoride ion was 1 hour. The removal of fluoride ions was maximum for the adsorbent dosage of SP is 50mg/50ml. The fluoride adsorption was maximum at 60minutes. The adsorption of F- ion was maximum in the shaking speed of 120 rpm. The presence of interfering ions such as nitrate and carbonate showed positive effect while sulphate and chloride showed little negative effect and phosphate showed high negative effect for the adsorbent. The optimum initial fluoride concentration for SP adsorbent was 1mg/50ml.
Stochastic Model to Find the Gallbladder Motility in Acromegaly Using Exponen...IJERA Editor
The purpose of the study was octreotide therapy in acromegaly is associated with an increased prevalence of gall stones, which may be the result of inhibition of gall bladder motility. Gall stone prevalence in untreated acromegalic patients relative to the general population is unknown, however and the presence of gall stones and gall bladder motility in these patients and in acromegalic patients receiving octreotide was therefore examined. Gall bladder emptying in untreated acromegalic subjects is impaired. Octreotide further increases post prandial residual gall bladder volume and this may be a factor in the increased gall stone prevalence seen in these patients.
Identification of Closest and Phantom Nodes in Mobile Ad Hoc NetworksIJERA Editor
There are several services that build on the availability of closest node location information like geographic routing in spontaneous networks, data gathering in sensor networks, movement coordination among autonomous robotic nodes, location specific services for hand held devices and danger warning or traffic monitoring in vehicular networks. Ad hoc networking protocols and location-aware services require that mobile nodes identify the location of their closest nodes. Such a process can be easily misuses or stop by opposed nodes. In absence of a priori trusted nodes, the spotting and identifying of closest node position presents challenges that have been scarcely investigated in the literature. Node can also send message from one to many nodes in a broadcasting manner here.
A Systematic Study on Composition of Low Viscosity Automotive Lube Oils with ...IJERA Editor
In this work the performance of three commercial lube oils and two base oils of different viscosities, composition have been studied for their tribo performance using four ball tribo tester as per ASTM D 4172D and IP 239 and at the end of the test run the steel ball surfaces have been examined under scanning electron microscope (SEM) to assess the wear deformities. The tribo-performance results have been co-related with the physico-chemical properties along with quantity and type of the molecules present in these oils by proton nuclear magnetic resonance (NMR) studies. It has been observed that the lubricant with higher viscosity, higher aromatic molecules containing CH₂ chains shows lower friction and wear behavior whereas the increasing napthenic type molecules have reverse effect which is evident from the results.
A aluna do Mestrado Profissional em Memória Social e Bens Culturais do Unilasalle – Canoas/RS, Lenise Di Domenico Colpo, realizou a pesquisa sobre o uso das ferramentas da web 2.0 pelas bibliotecas universitárias que compõem o Sistema de Bibliotecas da UFRGS - SBUFRGS.
Como produto do mestrado profissional, a pesquisa propôs a criação de um infográfico, que apresenta os resultados do estudo, entre eles, os serviços oferecidos pelas bibliotecas através das ferramentas da web 2.0, as mudanças provocadas no trabalho do bibliotecário e as competências exigidas diante do uso das ferramentas.
Este produto é pioneiro entre as bibliotecas universitárias do Brasil.
One-day system authentication could be widely achieved through brainwaves. One doesn’t need to remember that 8 or more character long strange password. Simply thinking of certain things, such as a person face, or a rotating displayed cube, or line of song would be enough to unlock a device. Electro-encephalography (EEC) sensors are behind the technique. That is where electrical activity in certain parts of the brain is recorded. These sensors are used to generate the graphical lines on charts created from wired electrodes placed on the scalp, as seen in hospitals and TV shows. They are used in hospital to diagnose epilepsy, among other things. In this case, though, one wouldn’t need to be fitted with wired electrodes —or even a headset, which is used already in some current non-muscular EEC computer controls. An ear bud will collect the signals (mental gesture) and perform secure authentication. This research could provide hands-free and wireless interaction, authentication, and user experience, all in the form-factor of a typical ear bud.
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.
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairIJTET Journal
Free versatility is center to having the capacity to perform exercises of day by day living without anyone else's input. In this proposed framework introduce an imparted control construction modeling that couples the knowledge and cravings of the client with the exactness of a controlled wheelchair. Outspread Basis Function system was utilized to characterize the predefined developments, for example, rest, forward, regressive, left and right of the wheelchair. This EEG-based cerebrum controlled wheelchair has been produced for utilization by totally incapacitated patients. The proposed outline incorporates a novel methodology for selecting ideal terminal positions, a progression of sign transforming and an interface to a controlled wheelchair.The Brain Controlled Wheelchair (BCW) is a basic automated framework intended for individuals, for example, bolted in individuals, who are not ready to utilize physical interfaces like joysticks or catches. The objective is to add to a framework usable in healing centers and homes with insignificant base alterations, which can help these individuals recover some portability. Also, it is explored whether execution in the STOP interface would be influenced amid movement, and discovered no modification with respect to the static performance.Finally, the general procedure was assessed and contrasted with other cerebrum controlled wheelchair ventures. Notwithstanding the overhead needed to choose the destination on the interface, the wheelchair is quicker than others .It permits to explore in a commonplace indoor environment inside a sensible time. Accentuation was put on client's security and comfort,the movement direction procedure guarantees smooth, protected and unsurprising route, while mental exertion and exhaustion are minimized by lessening control to destination determination.
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.
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
The locomotive disabled people and elderly people cannot control the wheelchair manually. The key
objective of this paper is to help the locomotive disabled and old people to easily manoeuvre without any social
aid through a brainwave-controlled wheelchair. There are various types of wheelchair available in the market
such as Voice controlled wheelchair, Joystick control wheelchair, Smart phone controlled wheelchair, Eye
controlled wheelchair, Mechanical wheelchair. These wheelchairs hold certain limitations for e.g. if the user is
dumb; user cannot access voice controlled wheelchair, etc. Brain-computer interface (BCI) is a new method used
to interface between the human mind and a digital signal processor. An Electroencephalogram (EEG) based BCI
is connected with an artificial reality system to control the movement and direction of a wheelchair. This paper
proposes brainwave controlled wheelchair, which uses the captured EEG signals from the brain. This EEG
signals are then passed to Arduino. It converts into control signals which will in turn help to move the wheelchair
in different direction.
Review Paper on Brain-Computer Interface and Recent TrendsEditor IJMTER
Although the development in computer hardware and software has been enormous in
recent decades, but the development in Human-computer interface (HCI) has been very slow but
discontinuous. From punch cards, text console, mouse to recently introduced gesture and voice
controls the development is enormous, the recent addition to this is Brain computer interface (BCI).
BCI makes uses of changes in brain and Electrical activity of brain to certain actions/thought
processes and uses algorithms to interpret the intention of the user, and reports the same to computer.
This paper focuses to review this area of HCI and demystify the techniques and concepts used and
also give a short report on recent development and research on the same .This technique of BCI not
only would be helpful for disabled to gain new strength but also would change the way we interact
with machine...FOREVER
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.
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.
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When stars align: studies in data quality, knowledge graphs, and machine lear...
Ab044195198
1. Lavanya Thunuguntla et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 4( Version 1), April 2014, pp.195-198
www.ijera.com 195 | P a g e
EEG Based Brain Controlled Robot
Lavanya Thunuguntla1
, R Naveen Venkatesh Mohan2
, P Mounika3
1,2,3
Hyderabad Institute Of Technology And Management, Hyderabad, AP(India)
Abstract
This brain controlled robot is 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 on
others.
I. INTRODUCTION
The brain controlled robot basically works
on the principle of capturing the brain wave signals
utilizing it for the movement of robot. This when
equipped with the wheel chairs of disabled persons
who can’t speak or move their hands will be useful
for their movement independently. Here the brain
wave analysis is being performed, the brain thoughts
1
is not being captured instead the brain concentration
level is being measured.
This robot can be utilized for multiple
purposes. Here the User Interface can be developed
in java & the robot can be serially controlled from
PC. This can be done by wirelessly controlled using
Bluetooth module, for increasing the range GSM
module also can be used. If the API is developed in
android then it also can be controlled using an
android platform based embedded device. However,
BCI development is no longer constrained to just
patients or for treatment, there is a shift of focus
towards people with ordinary health. Especially
gamers are becoming a target group that would likely
to be adaptive to use EEG as a new modality; giving
them advantages or new experiences in gameplay. It
is not just treatment in mind, but entertainment also.
This shift could benefit patients, because when EEG
technology becomes more available, and the
powerful gaming industry gets involved, they can
become the same driver for improvements as they are
for all silicon-based technology: needing, and thus
getting, faster processors and graphic engines so they
can create better games. By taking BCI to the level of
entertainment, the motivation for making more user
friendly, faster, cheaper and public available systems
will be totally different and become of a much higher
priority. The targeted groups of users are not forced
toutilize BCI systems, and thus needs better reasons
for wanting to, other than it is cool to be able to
control your computer with the mind. Current
systems do not meet such standards. The motivating
thought is that approaching this issue from an
entertaining point of view could help getting BCIs to
such standards faster.
Fig 1: Block Diagram
II. EEG
The term ‘bio signal’ is defined as any
signal measured and monitored from a biological
being, although it is commonly used to refer to an
electrical bio signal. Electrical bio signals2
(bio-
electrical signals) are the electrical currents generated
by electrical potential differences across a tissue,
organ or cell system like the nervous system.
Neuro means brain; therefore, ‘neuro-signal’
refers to a signal related to the brain. A common
approach to obtaining neuro-signal information is an
Electroencephalograph (EEG), which is a method of
measuring and recording neuro-signals using
electrodes placed on the scalp.
An electroencephalograph (EEG) is the
recorded electrical activity generated by the brain. In
general, EEG is obtained using electrodes3
placed on
the scalp with a conductive gel. In the brain, there are
millions of neurons, each of which generates small
electric voltage fields. The aggregate of these electric
voltage fields create an electrical reading which
RESEARCH ARTICLE OPEN ACCESS
2. Lavanya Thunuguntla et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 4( Version 1), April 2014, pp.195-198
www.ijera.com 196 | P a g e
electrodes on the scalp are able detect and record.
Therefore, EEG is the superposition of many simpler
signals. The amplitude of an EEG signal typically
ranges from about 1 µV to 100 µV in a normal adult,
and it is approximately 10 to 20 mV when measured
with subdural electrodes such as needle electrodes.
2.1 EEG analysis
The FFT (Fast Fourier Transform) is a
mathematical process which is used in EEG analysis
to investigate the composition of an EEG signal.
Since the FFT transforms a signal from the time
domain into the frequency domain, frequency
distributions of the EEG can be observed. EEG
frequency distribution is very sensitive to mental and
emotional states as well as to the location of the
electrode(s).Two types of EEG montages are used:
monopolar and bipolar. The monopolar montage
collects signals at the active site and compares them
with a common reference electrode. The common
electrode should be in a location so that it would not
be affected by cerebral activity. The main advantage
of the monopolar montage is that the common
reference allows valid comparisons of the signals in
many different electrode pairings. Disadvantages of
the monopolar montage include that there is no ideal
reference site, although the earlobes are commonly
used. In addition, EMG and ECG artifacts4
may occur
in the monopolar montage. Bipolar montage
compares signals between two active scalp sites.Any
activity in common with these sites is subtracted so
that only difference in activity is recorded.Therefore
some information is lost with this montage.
The 10-20 international system is used as
the standard naming and positioning scheme for EEG
measurements. The original 10-20 system included
only 19 electrodes. Later on, extensions were made
so that 70 electrodes could be placed in standard
positions. Generally one of the electrodes is used as
the reference position, often at the earlobe or mastoid
location.
The brain have always fascinated humans.
New methods for exploring it have been found and
we can categorize them into two main groups.
Invasive and non-invasive. An invasive approach
requires physical implants of electrodes in humans or
animals, making it possible to measure single
neurons or very local field potentials. A non-invasive
approach makes use of, for instance, magnetic
resonance imaging (MRI) and EEG technology to
make measurements. Both gives different
perspectives and enables us to look inside the brain
and to observe what happens.
Fig 2: Original 10-20 System
EEG is generally described in terms of its
frequency band. The amplitude of the EEG shows a
great deal of variability depending on external
stimulation as well as internal mental states. Delta,
theta ,alpha, beta and gamma are the names of the
different EEG frequency bands.
NeuroSky has developed a dry sensor
system for consumer applications of EEG
technology. NeuroSky5
system consists of dry
electrodes and a specially designed electronic circuit
for the dry electrodes.
NeuroSky has been conducting benchmark
tests of the dry EEG by comparing EEG signals
measured by the dry sensor system with signals from
the Biopac system, a well known wet electrode EEG
system widely used in medical and research
applications.
EEG was simultaneously recorded by the
NeuroSky system and the Biopac system. Electrodes
for the two systems were placed at the same location,
as close together as possible without interfering with
one another. Gold-plated dry electrodes were used for
NeuroSky system, while silver-silver-chloride
disposable electrodes with gel were used for Biopac
system.
Brain
Wave
type
Frequency
range
Mental states &
Conditions
Delta 0.1Hz-3 Hz Deep, dreamless
sleep, non-REM
sleep, unconscious
Theta 4 Hz-7 Hz Intuitive, creative,
recall, fantasy,
imaginary, dream
Alpha 8 Hz-12 Hz Relaxed, but not
drowsy, tranquil,
conscious
3. Lavanya Thunuguntla et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 4( Version 1), April 2014, pp.195-198
www.ijera.com 197 | P a g e
Low beta 12 Hz-15 Hz Formerly SMR,
relaxed yet focused,
integrated
Mid
range
beta
16 Hz-20 Hz Thinking, aware of
self & surroundings
High beta 21 Hz-30 Hz Alertness, agitation
Gamma 30 Hz-100
Hz
Motor Functions,
higher mental activity
Fig 3 : Different Brain states at different
frequecies
2.2 EEG Artifacts
Since EEG signals are very weak (ranging
from 1 to 100 µV), they can easily be contaminated
by other sources. An EEG signal that does not
originate from the brain is called an artifact. Artifacts
can be divided into two categories: physiologic and
non-physiologic. Any source in the body which has
an electrical dipole or generates an electrical field is
capable of producing physiologic artifacts. These
include the heart, eyes, muscle, and tongue. Sweating
can also alter the impedance at the electrodescalp
interface and produce an artifact. Non-physiologic
artifacts include 60 Hz interference from electric
equipment, kinesiologic artifacts caused by body or
electrode movements, and mechanical artifacts
caused by body movement.
III. Results and Discussion
Brain-computer interface is a method of
communication based on neural activity generated by
the brain and is independent of its normal output
pathways of peripheral nerves and muscles. The goal
of BCI is not to determine a person’s intent by
eavesdropping on brain activity,
but rather to provide a new channel of output for the
brain that requires voluntary adaptive control by the
user.
The Fourier Transformation and extraction
of band powers is by far the most applied method for
signal processing and analysis The algorithm is based
on discrete Fourier transform (DFT) and by applying
that to the EEG signal it makes it possible to separate
the EEG rhythms.
Definition:
N-1
Xk=∑ xne-(i2kπn)/N
k=0,….N-1.
n=0
The performance of the DTF is O(N2
), but
there is a more efficient algorithm called fast Fourier
Transform (FFT), that can compute the same result in
only O(NlogN). This is a great improvement and one
of the reasons why FFT is the favorable method of
analyzing EEG signals, and other waves like sound.
The Problems faced with BCI design are
Noise-They have poor signal-to-noise ratio, The EEG
signals vary rapidly (Non-Stationary).
The process flow BCI can be shown in the
below block diagram
Fig 4 : Flow of BCI
BCI robot can be used in Bioengineering
applications: Devices with assisting purposes for
disabledpeople , Human subject monitoring:
Research and detection of sleep disorders,
neurological diseases, attention monitoring, and/or
overall ”mental state” ,. Neuroscience research: real-
time methods for correlating observable behavior
with recorded neural signals, Human-Machine
Interaction: Interface devices between humans,
computers or machines.
IV. CONCLUSION
Here a single robot is used for multiple
purposes thereby reducing cost for designing multiple
robots. It gives a optimized as well as customized
solution of general robots which we require.
REFERENCES
[1] Lopes da Silva, F., Functional localization of
brain sources using EEG and/or MEG data:
volume conductor and source models. Magn
Reson Imaging, 2004. 22(10): p. 1533-8.
[2] Delorme, A. and S. Makeig, EEGLAB: an
open source toolbox for analysis of single-trial
EEG dynamics including independent
component analysis. J Neurosci Methods,
2004. 134(1): p. 9-21.
[3] Benjamini, Y. and Y. Hochberg, Controlling
the false discovery rate: a practical and
powerful approach to multiple testing. Journal
of the Royal Statistical Society, Series B
(Methodological), 1995. 57(1): p. 289-300.
[4] McKinnon, K.I.M., Convergence of the
Nelder-Mead simplex method to a non-
stationary point. SIAM J Optimization, 1999.
9: p. 148-158.
[5] Andrew T Campbell, Tanzeem Choudhury,
Shaohan Hu, Hong Lu, Mashfiqui Rabbi,
Rajeev D S Raizada, M. K. M. . (2010).
NeuroPhone:Brain-MobilePhone Interface
using a Wireless EEG Headset. MobiHeld 2,
3–8.
4. Lavanya Thunuguntla et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 4( Version 1), April 2014, pp.195-198
www.ijera.com 198 | P a g e
Author (s)
Author 1: Ms Lavanya Thunuguntla
has 8 years of Teaching experience and presently
working as an Associate Professor in the Department
of ECE in Hyderabad Institute of Technology and
Management (HITAM), Hyderabad, AP(India). She
received her B.Sc degree in Computer Science from
Acharya Nagarjuna University in 2002, M.Sc degree
in Physics from University of Hyderabad, Hyderabad
in 2004 and M.Tech from Indian Institute of
Technology (IIT) Kharagpur in 2007. She has
Professional Membership in IEEE,WIE. She has
various National & International journal publications.
She has guided several M.Tech and B.Tech projects.
She has strong motivation towards research in the
fields of Nano Technology, Microwave and Optical
& Analog Communications ,VLSI system design etc.
She can be reached at lavanya_iitkgp@yahoo.co.in
Author 2
G Naveen Venkatesh Mohan is
pursuing Fourth Year B.Tech in Electronics &
Communication Engineering branch in Hyderabad
Institute of Technology and Management (HITAM),
Hyderabad, AP(India). He gave presentations in
various National level contests.His areas of interests
are Communications,VLSI system Design etc.
Author 3
P Mounika is pursuing Fourth Year
B.Tech in Electronics & Communication
Engineering branch in Hyderabad Institute of
Technology and Management (HITAM), Hyderabad,
AP(India). She gave presentations in various National
level contests.Her areas of interests are
Nanotechnology ,VLSI system Design etc.