This document discusses EEG (electroencephalography), including its importance, challenges, and applications. Some key points:
- EEG measures electrical activity in the brain through electrodes placed on the scalp. It is important for diagnosing brain injuries and illnesses.
- Challenges of EEG include its low amplitude signal that is susceptible to noise, non-stationary electrode-skin interface, and developing portable dry electrode systems.
- Applications include EEG-based brain-computer interfaces (BCIs) which allow communication without muscle movement. However, BCIs face challenges like developing portable, wireless, and comfortable dry electrode systems.
Mobile Phone Handset Radiation Effect on Brainwave Signal using EEG: A Review IJEEE
This paper talks about the growing concern in the people about the effect of mobile phone RF radiation on human brainwave signal using various techniques to capture those effects. Linear, Non- linear and statistical methods (t-tests) employed by various researchers to analyze variation in brainwaves.This paper gives comparison between brainwaves like Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-13 Hz), Beta (13-30 Hz) and Gamma (30 Hz-70Hz) in terms of Frequency Progressive map and amplitude progressive tri-maps. Electroencephalography (EEG) gives a noninvasive way of measuring brainwave activity from sensors placed on the scalp of the human head. So, the aim of this paper is to study the effect of RF exposure from mobile phone on Human brainwave as brainwaves Brain wave can provide information of mental state of the individual.
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.
This paper will review the works on Surface Electromyography (SEMG) signal acquisition and controlling as well as the uses of SEMG signals analysis for Transfemoral amputee's people. In the beginning, this paper will briefly go through the basic theory of myoelectric signal generation. Next, the signal acquisition & filtering techniques applied for SEMG signal will be explained. Then after this EMG signal control or actuate the myoelectric leg who was suffering from Transfemoral amputee using microcontroller. This paper gives the better controlling SEMG signal and also very smooth and easy controlling of the Prosthetic leg motor using Myoelectric Controller.
Mobile Phone Handset Radiation Effect on Brainwave Signal using EEG: A Review IJEEE
This paper talks about the growing concern in the people about the effect of mobile phone RF radiation on human brainwave signal using various techniques to capture those effects. Linear, Non- linear and statistical methods (t-tests) employed by various researchers to analyze variation in brainwaves.This paper gives comparison between brainwaves like Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-13 Hz), Beta (13-30 Hz) and Gamma (30 Hz-70Hz) in terms of Frequency Progressive map and amplitude progressive tri-maps. Electroencephalography (EEG) gives a noninvasive way of measuring brainwave activity from sensors placed on the scalp of the human head. So, the aim of this paper is to study the effect of RF exposure from mobile phone on Human brainwave as brainwaves Brain wave can provide information of mental state of the individual.
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.
This paper will review the works on Surface Electromyography (SEMG) signal acquisition and controlling as well as the uses of SEMG signals analysis for Transfemoral amputee's people. In the beginning, this paper will briefly go through the basic theory of myoelectric signal generation. Next, the signal acquisition & filtering techniques applied for SEMG signal will be explained. Then after this EMG signal control or actuate the myoelectric leg who was suffering from Transfemoral amputee using microcontroller. This paper gives the better controlling SEMG signal and also very smooth and easy controlling of the Prosthetic leg motor using Myoelectric Controller.
Emg driven ipmc based artificial muscle finger Abida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. This can be stimulated by supplying a small voltage of 3V and shows evidence of a large bending behavior. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
A Novel Approach for Measuring Electrical Impedance Tomography for Local Tiss...CSCJournals
This paper proposes a novel approach for measuring Electrical Impedance Tomography (EIT) of a living tissue in a human body. EIT is a non-invasive technique to measure two or three-dimensional impedance for medical diagnosis involving several diseases. To measure the impedance value electrodes are connected to the skin of the patient and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. The determination of local impedance parameters can be carried out using an equivalent circuit model. However, the estimation of inner tissue impedance distribution using impedance measurements on a global tissue from various directions is an inverse problem. Hence it is necessary to solve the inverse problem of calculating mathematical values for current and potential from conducting surfaces. This paper proposes a novel algorithm that can be successfully used for estimating parameters. The proposed novel hybrid model is a combination of an artificial intelligence based gradient free optimization technique and numerical integration. This ameliorates the achievement of spatial resolution of equivalent circuit model to the closest accuracy. We address the issue of initial parameter estimation and spatial resolution accuracy of an electrode structure by using an arrangement called “divided electrode” for measurement of bio-impedance in a cross section of a local tissue.
In recent years, unspoken words recognition has
received substantial attention from both the scientific research
communities and the society of multimedia information access
networks. Major advancements and wide range of applications
in aids for the speech handicapped, speech pathology research,
telecom privacy issues, cursor based text to speech, firefighters
wearing pressurized suits with self contained breathing
apparatus (SCBA), astronauts performing operations in
pressurized gear, as a part of communication system operating
in high background noise have propelled words recognition
technology into the spotlight. Though early words recognition
techniques used simple maximum likelihood algorithms only
but the recognition process has now graduated into a science
of mathematical representations and comparison processes.
This survey paper provides an up-to-date review of the existing
approaches and offers some insights into the study of unspoken
words recognition. A number of typical techniques and EMG
based approaches are discussed in this paper. Furthermore, a
discussion outlining the incentives for using recognition
techniques, the applications of this technology, and some of
the difficulties plaguing the current systems with regard to
this topic have also been provided.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
Krammer P. et al.: Electrical impedance tomography Simulator.Hauke Sann
Swisstom Scientific Library; 16th International Conference on Biomedical Applications of Electrical Impedance Tomography, Neuchâtel Switzerland, June 2-5, 2015
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/
In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Emg driven ipmc based artificial muscle finger Abida Zama
The medical, rehabilitation and bio-mimetic technology demands human actuated devices which can support in the daily life activities such as functional assistance or functional substitution of human organs. These devices can be used in the form of prosthetic, skeletal and artificial muscles devices. However, we still have some difficulties in the practical use of these devices. The major challenges to overcome are the acquisition of the user’s intention from his or her bionic signals and to provide with an appropriate control signal for the device. Also, we need to consider the mechanical design issues such as lightweight and small size with flexible behavior etc. For the bionic signals, the electromyography (EMG) signal can be used to control these devices, which reflect the muscles motion, and can be acquired from the body surface. We are familiar with the fact that Ionic polymer metal composite (IPMC) has tremendous potential as an artificial muscle. This can be stimulated by supplying a small voltage of 3V and shows evidence of a large bending behavior. In place of the supply voltage from external source for actuating an IPMC, EMG signal can be used where EMG electrodes show a reliable approach to extract voltage signal from body. Using this voltage signal via EMG sensor, IPMC can illustrate the bio-mimetic behavior through the movement of human muscles. Therefore, an IPMC is used as an artificial muscle finger for the bio-mimetic/micro robot.
A Novel Approach for Measuring Electrical Impedance Tomography for Local Tiss...CSCJournals
This paper proposes a novel approach for measuring Electrical Impedance Tomography (EIT) of a living tissue in a human body. EIT is a non-invasive technique to measure two or three-dimensional impedance for medical diagnosis involving several diseases. To measure the impedance value electrodes are connected to the skin of the patient and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. The determination of local impedance parameters can be carried out using an equivalent circuit model. However, the estimation of inner tissue impedance distribution using impedance measurements on a global tissue from various directions is an inverse problem. Hence it is necessary to solve the inverse problem of calculating mathematical values for current and potential from conducting surfaces. This paper proposes a novel algorithm that can be successfully used for estimating parameters. The proposed novel hybrid model is a combination of an artificial intelligence based gradient free optimization technique and numerical integration. This ameliorates the achievement of spatial resolution of equivalent circuit model to the closest accuracy. We address the issue of initial parameter estimation and spatial resolution accuracy of an electrode structure by using an arrangement called “divided electrode” for measurement of bio-impedance in a cross section of a local tissue.
In recent years, unspoken words recognition has
received substantial attention from both the scientific research
communities and the society of multimedia information access
networks. Major advancements and wide range of applications
in aids for the speech handicapped, speech pathology research,
telecom privacy issues, cursor based text to speech, firefighters
wearing pressurized suits with self contained breathing
apparatus (SCBA), astronauts performing operations in
pressurized gear, as a part of communication system operating
in high background noise have propelled words recognition
technology into the spotlight. Though early words recognition
techniques used simple maximum likelihood algorithms only
but the recognition process has now graduated into a science
of mathematical representations and comparison processes.
This survey paper provides an up-to-date review of the existing
approaches and offers some insights into the study of unspoken
words recognition. A number of typical techniques and EMG
based approaches are discussed in this paper. Furthermore, a
discussion outlining the incentives for using recognition
techniques, the applications of this technology, and some of
the difficulties plaguing the current systems with regard to
this topic have also been provided.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
Krammer P. et al.: Electrical impedance tomography Simulator.Hauke Sann
Swisstom Scientific Library; 16th International Conference on Biomedical Applications of Electrical Impedance Tomography, Neuchâtel Switzerland, June 2-5, 2015
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/
In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
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.
A robotic arm is a Programmable mechanical arm which copies the functions of the human arm. They
are widely used in industries. Human robot-controlled interfaces mainly focus on providing rehabilitation to
amputees in order to overcome their amputation or disability leading them to live a normal life. The major
objective of this project is to develop a movable robotic arm controlled by EMG signals from the muscles of the
upper limb. In this system, our main aim is on providing a low 2-dimensional input derived from emg to move the
arm. This project involves creating a prosthesis system that allows signals recorded directly from the human body.
The arm is mainly divided into 2 parts, control part and moving part. Movable part contains the servo motor
which is connected to the Arduino Uno board, and it helps in developing a motion in accordance with the EMG
signals acquired from the body. The control part is the part that is controlled by the operation according to the
movement of the amputee. Mainly the initiation of the movement for the threshold fixed in the coding. The major
aim of the project is to provide an affordable and easily operable device that helps even the poor sections of the
amputated society to lead a happier and normal life by mimicking the functions of the human arm in terms of both
the physical, structural as well as functional aspects.
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIERhiij
Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures
voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic
applications generally focus on the spectral content of EEG, which is the type of neural oscillations that
can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious
abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and
motivates the need for advanced signal processing techniques to aid clinicians in their interpretations.
This paper describes the application of Wavelet Transform (WT) for the processing of
Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for
feature selection and dimensionality reduction where the informative and discriminative two-dimension
features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP)
neural network. For five classification problems, the proposed model achieves a high sensitivity,
specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed
methods and those obtained with previous literature methods shows the superiority of our approach for
EEG signals classification and automated diagnosis
A nonlinearities inverse distance weighting spatial interpolation approach ap...IJECEIAES
Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.
Gene's law, Common gate, kernel Principal Component Analysis, ASIC Physical Design Post-Layout Verification, TSMC180nm, 0.13um IBM CMOS technology, Cadence Virtuoso, FPAA, in Spanish, Bruun E,
Lab 2: Cadence Tutorial on Layout and DRC/LVS/PEX
This section describes how to extract a netlist from your layout that includes parasitic resistances and capacitances. You will then be able to re-simulate your design with extracted parasitics in Spectre. PEX requires a clean LVS so that extracted parasitics can be correlated to nets on the schematic. Initiate the PEX interface by clicking on:Calibre > Run PEX
A window asking to load a runset file will now appear. Browse to the file
Step by step process of uploading presentation videos Hoopeer Hoopeer
Deep neural network, compressive sensing, floating gate techniques can be efficiently employed to increase voltage swing and reduce supply voltage requirements of class AB regulated cascode current mirrors, implement extreme low power analog circuits with this process. /also have good references for subthreshold region.
[Extreme Low Power Differential Pair: An Experimental Evaluation, Super-Gain-Boosted Miller Op-Amp based on Nested Regulated Cascode Techniques , Step by Step process of uploading presentation videos, Dennis Ritchie The creator of the C programming language and co-creator of Unix
Influential and powerful professional electrical and electronics engineering ...Hoopeer Hoopeer
powerful professional electrical and electronics engineering books
. Analysis and Design of Analog Integrated Circuits
Analysis and Design of Analog Integrated Circuits
Analog filter design
BJT and MOS, Advanced Circuit Topologies, concept of tracking, mm-Wave frequency beyond 30GHz, Bandgap is a stable, well defined, and constant current source
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Governing Equations for Fundamental Aerodynamics_Anderson2010.pdf
Eeg importance and challenges
1. EEG importance and challenges
Presentation · September 2014 DOI: 10.13140/RG.2.1.2387.4166
https://www.researchgate.net/publication/304216715
Outline
Introduction about EEG.
Importance of EEG.
Challenges of EEG.
Introduction about EEG
EEG (Electro-encephalo-gram) belongs to biopotential signals which become
important field in diagnosing human health since 1929 [1].
EEG is the registration and the interpretation of the electrical waves generated
from the neuron activity of the brain, which is related to the nervous system [2,
3, 4, 5-6, 7].
EEG signal is a low amplitude voltage ranged from 10μV to 100μV and it
occupies low frequency bands from 0.1Hz to 100Hz [8-1, 15]. Table 1 compares
different biopotential signals in their amplitude and frequency ranges [8-1, 9-13,
14, 15-16].
Table 1 The amplitude voltage, the frequency, and the segment of the signal
for some biopotential signals.
2. There are five classifications of the frequency waves of the EEG signal
mentioned in table 2, ordered from lowest to highest frequencies.
Table 2 The amplitude voltage, the frequency band, the location, the
occurrence, and morphology of EEG’s waves [1, 17, 18, 19].
There are five classifications of the frequency waves of the EEG signal
mentioned in table 2, ordered from lowest to highest frequencies.
The brain rhythm is used to diagnose the brain seizures and injuries [8].
EEG device can be connected on the scalp of the patient through electrodes. A
common used system to place electrode is 10-20 system standard [1-20, 18-21].
3. Importance of EEG
•EEG test
EEG test is an indicator to check brain waves normality and to diagnose brain
illness and injury.
It is used in hospitals for medical purposes and in research laboratories for
scientific researches and device developments.
EEG based Brain Control Interface
EEG works as a brain monitoring method employed in BCI.
BCI is a communication/control system which allows interface between human
and computer without the use of muscles or peripheral nerves [22].
The system diagram of BCI is shown in figure 2.
4.
5. Challenges in EEG
The challenges face designing EEG system can be categorized as:
1.EEG characteristics.
2.Application used EEG acquisition system.
3.Building blocks of EEG acquisition system.
6. 1.EEG characteristics.
Measuring EEG signal is difficult due to its characteristics.
The surface voltage which reaches to the scalp passes through different
nonhomogeneous tissues. These tissues are listed in the table 3 with their
corresponding resistivity [1, 15].
Measuring EEG signal is difficult due to its characteristics.
The surface voltage which reaches to the scalp passes through different
nonhomogeneous tissues. These tissues are listed in the table 3 with their
corresponding resistivity.
Its low frequency band is easily affected by interference and noise.
7. 2.Application used EEG acquisition system.
EEG based BCI challenges.
1.Portability and Wearability
Compact/Miniaturized.
Lightweight.
Handheld.
Simple to use.
► Portable EEG system with Laptop/computer/phone
9. 2. Dry/Noncontact electrodes.
Free-gel media (direct skin contact).
Comfortable and easy to prepare.
Long term monitoring (electrode longevity).
Flexible electrodes to push away hair from scalp (through hair) [31].
10. ► Wearable EEG system with direct skin contact.
₪Suffered from motion artifacts [31].
2. Dry/Noncontact electrodes.
Free-gel media (special case of dry electrodes [31]).
Inserted on insulation layer (clothes [31, 41]).
Long term monitoring.
11. ► Wearable EEG system with through clothes electrodes.
₪Sensitive to motion artifacts [31].
3.Building blocks of EEG acquisition system.
Analog readout EEG circuit.
The analog readout EEG circuit is shown in figure 11.
EEG Readout circuit.
1.1/f noise appears in the band of EEG frequency.
2.AC power line interference (mains interference) can easily interfere with EEG
signal ( can reach amplitude of 1mV) [44].
12. 3.Skin-electrode interface generates differential DC electrode offset voltage (can
reach amplitude 50mV) [45].
► Readout circuit should have HPF characteristics to remove low frequency
noise (1/f noise and DC offset), and LPF to eliminate unwanted high frequency.
₪Readout circuit designed for dry electrode must have high input impedance
(>>1GΩ) [45].
Biopotential Amplifier
1.High CMRR to neglect mains interference.
2.HPF features to filter DC electrodes offset voltage.
3.Low noise to have high quality signal.
4.Low power dissipation to increase supply voltage life [45].
5.Configurable gain and filter features that suit with the needs of different
biopotential signals and different applications.
Table 2 and table 3 show different designs of instrumentation amplifier and
differential amplifier using for biopotential applications, respectively.
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Thank you