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The topics covered are:-
1- introduction
2.circuit diagram and its explaination
3.working
4. features
5.advantages / disadvantages
6. the top vendors
This document discusses different types of amplifiers:
1. Power/current amplifiers increase the power or current of an input signal, while decreasing voltage.
2. Frequency amplifiers increase the frequency component of an input signal.
3. Voltage amplifiers increase the voltage of an input signal. Voltage amplifiers are further divided into pre-amplifiers, differential amplifiers, and single-ended amplifiers.
Differential amplifiers are important in EEG machines as they can reject common mode signals and accurately amplify small voltage differences in brain waves.
EEG is used to record the electrical activity of the brain. It uses electrodes placed on the scalp that are smaller than those used in ECGs. EEG can be used to diagnose neurological disorders like epilepsy. There are different types of brain waves like delta, theta, alpha, beta, and gamma waves that are defined by their frequency ranges and locations in the brain. Evoked potentials involve stimulating specific sensory pathways and measuring the electrical response in certain brain areas to help diagnose conditions.
This document discusses an EEG-based brain-computer interface project. It provides background on EEG, including how it works, common frequency bands and their clinical significance. It then discusses the project, which involves assembling EEG hardware, developing software for EEG signal processing and interfacing it with a computer. The work done so far includes purchasing components, starting PCB assembly and researching relevant software. Future plans include developing code for EEG signal processing, relating the EEG to the computer, and testing the project.
Isolation amplifiers provide electrical isolation and safety barriers between input and output stages. They use transformer, optical, or capacitive isolation methods and isolated power supplies to break continuity while amplifying low-level signals. Common applications include medical equipment, industrial processes, and data acquisition where electrical isolation is needed to protect patients or eliminate noise.
This document discusses electromyography (EMG), which is the study of electrical activity in muscles. It describes how EMG is recorded using different types of electrodes, including surface electrodes to record signals on the skin surface and needle electrodes that can detect deeper muscle potentials. The EMG recording system involves electrodes to pick up signals, amplification, and output to devices like speakers or tape recorders. EMG has applications in studying neuromuscular functions and diseases. Measurement of conduction velocity in motor nerves can help locate nerve lesions by stimulating nerves and measuring latency between stimulation and muscle action potentials.
The document discusses electroencephalography (EEG), which measures the electrical activity of the brain using electrodes placed on the scalp. It describes brain anatomy, including the cerebrum, cerebellum, and brainstem. It also discusses the 10-20 international system for electrode placement on the scalp and the different types of brain waves that can be measured by EEG, including alpha, beta, theta, delta, and gamma waves. The document provides an overview of how EEG is used to record and analyze brain activity for applications such as epilepsy diagnosis, monitoring anesthesia and brain injury.
An electrogastrogram (EGG) is a graphic produced by an electrogastrograph, which records the electrical signals that travel through the stomach muscles and control the muscles' contractions. An electrogastroenterogram (or gastroenterogram) is a similar procedure, which writes down electric signals not only from the stomach, but also from intestines.
The 10-20 system is an internationally recognized method for standardizing EEG electrode placement on the scalp. It is based on the relationship between electrode positions and the underlying areas of the cerebral cortex. Electrodes are placed at fixed locations based on percentages of the total front-to-back or right-to-left distance of the head. Letters and numbers identify the hemisphere and lobe locations of the electrodes. The system allows for reproducible positioning of scalp electrodes across patients and research studies.
This document discusses different types of amplifiers:
1. Power/current amplifiers increase the power or current of an input signal, while decreasing voltage.
2. Frequency amplifiers increase the frequency component of an input signal.
3. Voltage amplifiers increase the voltage of an input signal. Voltage amplifiers are further divided into pre-amplifiers, differential amplifiers, and single-ended amplifiers.
Differential amplifiers are important in EEG machines as they can reject common mode signals and accurately amplify small voltage differences in brain waves.
EEG is used to record the electrical activity of the brain. It uses electrodes placed on the scalp that are smaller than those used in ECGs. EEG can be used to diagnose neurological disorders like epilepsy. There are different types of brain waves like delta, theta, alpha, beta, and gamma waves that are defined by their frequency ranges and locations in the brain. Evoked potentials involve stimulating specific sensory pathways and measuring the electrical response in certain brain areas to help diagnose conditions.
This document discusses an EEG-based brain-computer interface project. It provides background on EEG, including how it works, common frequency bands and their clinical significance. It then discusses the project, which involves assembling EEG hardware, developing software for EEG signal processing and interfacing it with a computer. The work done so far includes purchasing components, starting PCB assembly and researching relevant software. Future plans include developing code for EEG signal processing, relating the EEG to the computer, and testing the project.
Isolation amplifiers provide electrical isolation and safety barriers between input and output stages. They use transformer, optical, or capacitive isolation methods and isolated power supplies to break continuity while amplifying low-level signals. Common applications include medical equipment, industrial processes, and data acquisition where electrical isolation is needed to protect patients or eliminate noise.
This document discusses electromyography (EMG), which is the study of electrical activity in muscles. It describes how EMG is recorded using different types of electrodes, including surface electrodes to record signals on the skin surface and needle electrodes that can detect deeper muscle potentials. The EMG recording system involves electrodes to pick up signals, amplification, and output to devices like speakers or tape recorders. EMG has applications in studying neuromuscular functions and diseases. Measurement of conduction velocity in motor nerves can help locate nerve lesions by stimulating nerves and measuring latency between stimulation and muscle action potentials.
The document discusses electroencephalography (EEG), which measures the electrical activity of the brain using electrodes placed on the scalp. It describes brain anatomy, including the cerebrum, cerebellum, and brainstem. It also discusses the 10-20 international system for electrode placement on the scalp and the different types of brain waves that can be measured by EEG, including alpha, beta, theta, delta, and gamma waves. The document provides an overview of how EEG is used to record and analyze brain activity for applications such as epilepsy diagnosis, monitoring anesthesia and brain injury.
An electrogastrogram (EGG) is a graphic produced by an electrogastrograph, which records the electrical signals that travel through the stomach muscles and control the muscles' contractions. An electrogastroenterogram (or gastroenterogram) is a similar procedure, which writes down electric signals not only from the stomach, but also from intestines.
The 10-20 system is an internationally recognized method for standardizing EEG electrode placement on the scalp. It is based on the relationship between electrode positions and the underlying areas of the cerebral cortex. Electrodes are placed at fixed locations based on percentages of the total front-to-back or right-to-left distance of the head. Letters and numbers identify the hemisphere and lobe locations of the electrodes. The system allows for reproducible positioning of scalp electrodes across patients and research studies.
The EEG records electrical activity in the brain from the scalp using electrodes placed according to the 10-20 system. There are different types of brain waves seen on EEG including alpha, beta, theta, and delta waves which vary in frequency and amplitude. Factors like age, consciousness, medications, and stimuli can influence the brain waves observed on EEG. Hans Berger first recorded human EEG waves in 1929, establishing EEG as a tool for examining brain function.
Graphic record heart sound - Phonogram.
Recording the sounds connected with the pumping action of heart.
Sound from heart – phonocardiogram
Instrument to measure this – phonocardiograph
Basic function – to pick up the different heart sound,filter the required and display.
The document discusses an electromagnetic blood flow meter. It operates based on electromagnetic induction principles, inducing an EMF in blood flowing through a vessel perpendicular to a magnetic field. Electrodes placed across the vessel measure this induced EMF, which is proportional to blood velocity. The small EMF signal is amplified for measurement and low pass filtered to determine average blood flow rate. Advantages include a linear dynamic range and no mechanical limitations for measuring high and low blood flows.
The document summarizes key aspects of electroencephalography (EEG). It describes how EEG detects electrical signals produced by the brain using electrodes placed on the scalp. It explains that EEG signals vary in amplitude and frequency depending on behavioral states. It also outlines the main components of an EEG machine, including electrodes, amplifiers, filters, and a writing system to record the brain's electrical activity.
Amplifiers and biopotential amplifiers newM. Raihan
What is Amplifiers?
Classification of Amplifiers
Based on number of stages
Single-stage Amplifier
Based on its output
ased on the input signals
Based on the frequency range
Based on the Coupling method
Based on the Transistor
Configuration
CE amplifier
CC amplifier
Comparaison between CB, CE
and CC Amplifies
Noise in Amplifier
Signal to Noise Ratio (SNR)
Biopotential Amplifiers
Typical bio-amplifier requirements
Voltage and Frequency
Range for Biopotentials
Electrocardiograph Amplifiers
Interference Reduction Techniques
This document discusses various types of EEG artifacts including physiological artifacts generated by the body and extraphysiological artifacts from external equipment or environment. It describes common artifacts like cardiac, electrode, eye blink, muscle activity and their characteristic appearances on EEG. The key is to ensure good preparation, electrode placement and monitoring for artifacts during EEG recording to obtain clean data for accurate interpretation.
The document discusses phonocardiography, which is the study of heart sounds using a phonocardiograph. A phonocardiograph is an instrument that picks up heart sounds, filters them, and displays the phonocardiogram, which is the graphic recording of heart sounds. There are two main types of heart sounds - normal sounds due to valve openings and closings, and abnormal murmurs due to turbulent blood flow. The document outlines the history and development of phonocardiography and the stethoscope, describes heart sound characteristics, and discusses the components of a phonocardiogram recording system.
Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields generated by neuronal brain activity. MEG uses very sensitive magnetometers to record these natural magnetic fields produced by the brain's electrical currents. Though brain signals appear irregular, they may be generated by deterministic nonlinear systems. MEG provides both high temporal resolution and excellent spatial resolution of brain function without exposure to radiation or invasive procedures.
EEG artifacts can arise from various physiological and extraphysiological sources other than brain activity. Physiological artifacts originate from the patient's own generator sources like eye movements, muscle activity, movement, and cardiac activity. Extraphysiological artifacts are externally generated, such as from medical devices, electrical equipment, or the environment. Common EEG artifacts include cardiac artifacts like ECG signals, ballistocardiographic artifacts from head or body movement, pacemaker signals, and pulse artifacts. Electrode artifacts can be transient pops or low frequency rhythms across electrodes from poor contact or movement. External artifacts include 50/60 Hz ambient noise, intravenous drips, and signals from devices like pumps and ventilators. Muscle and ocular artifacts
The document discusses electromyography (EMG), which is used to record and study the electrical activity of muscles. EMG uses surface or needle electrodes to measure bioelectric potentials from muscles. The measured signals are amplified and displayed. EMG can detect nerve lesions, muscle conditions, and reflex responses. However, the EMG signals can be affected by various artefacts like power line interference, electrocardiogram signals, movement, DC offset, and muscle crosstalk.
EEG - Montages, Equipment and Basic PhysicsRahul Kumar
This presentation discusses the 10-20 system of electrode placement, with its modifications. Also discussed are the Equipment Specifications, basic Physics and sources of interference
This document provides information about electroencephalography (EEG), electromyography (EMG), and patient monitoring. It discusses how EEG is used to measure brain activity through electrodes on the scalp. It describes the different frequency bands seen on EEG and how they relate to mental states. The document outlines the components of an EEG recording system and various EEG artifacts. It also discusses EMG and how it is used to measure muscle electrical activity. Finally, it covers patient monitoring systems, including bedside monitors, central monitoring stations, and the parameters that are measured like heart rate, blood pressure, respiration rate.
This document discusses patient monitoring systems and biotelemetry. It describes electrocardiogram (ECG) and blood pressure monitoring in hospitals. Intensive care unit (ICU) monitoring instruments that continuously measure vital signs are discussed. Biotelemetry systems that remotely transmit physiological data via radio frequency are then outlined, including the components of transmitters and receivers. Design considerations for biotelemetry systems using amplitude or frequency modulation are presented. Finally, both single-channel and multichannel biotelemetry systems are described.
The document discusses processing and noise cancellation of electrocardiogram (ECG) signals. It begins by explaining what an ECG is and how it is generated by the electrical activity of the heart. The ECG provides information about heart rate and the strength of the heart muscles. ECG signals are recorded using skin electrodes and contain noise from sources like power lines and electrode contact that must be removed. Common processing techniques include filtering using bandpass and adaptive filters to reduce noise and enhance the ECG waveform. Further analysis of the filtered ECG can detect heart abnormalities and conditions. Adaptive noise cancellation algorithms use a reference noise signal to minimize interference in the primary ECG input signal.
Calibration of an EEG machine involves checking various parameters to ensure accurate measurements. It is important as it allows correct interpretation of recordings and comparison to previous studies. Parameters checked include paper speed, pen alignment, centering and damping, time constant, high frequency filter, sensitivity, amplitude linearity, gain, noise level and more. Verifying these helps identify any issues needing adjustment and confirms the machine is functioning properly.
A Bioamplifier is an electrophysiological device, a variation of the instrumentation amplifier, used to gather and increase the signal integrity of physiologic electrical activity for output to various sources. It may be an independent unit, or integrated into the electrodes.
This document discusses two main applications of EEG waves: diagnosis of sleep disorders and brain-computer interfaces (BCIs). For sleep disorder diagnosis, EEG signals are analyzed to stage sleep and identify abnormalities. Statistical methods classify sleep stages over time. BCIs translate brain signals into commands, using visual evoked potentials from stimuli flickering at different frequencies to identify intended commands. Key issues discussed include reducing electrode numbers and optimizing frequency feature extraction from EEG signals to improve BCI accuracy and usability.
1. Impedance is opposition to alternating current flow and has two components: resistance and reactance. Resistance opposes direct current, while reactance depends on frequency and includes capacitance and inductance. (2) Because EEG contains strong AC signals, impedance rather than just resistance is measured. (3) Electrode impedance is measured by passing a small current between electrodes and is impacted by dead skin cells separating the electrode from living tissue.
This document discusses biopotentials and methods for measuring them. It begins with an introduction to biopotentials and what they are. It then discusses the mechanisms behind biopotentials, focusing on ion concentrations and how they generate electrical potentials. The rest of the document discusses specific measurement methods like ECG, EEG, EMG, EOG, and considerations for biopotential measurement like electronics, electrodes, and practices.
EEG detects brain wave activity through electrodes placed on the scalp. Brain waves fall into different frequency bands such as delta, theta, alpha, beta, and gamma, which correlate with cognitive and mental states. EEG signals are generated by excitatory and inhibitory neuron activity. EEG recordings involve placing electrodes on standardized scalp locations, amplifying and processing the signals, then analyzing them in frequency or time domains. Digital EEG offers advantages over analog systems like high precision timing and non-invasiveness. It provides a relatively inexpensive way to check brain functioning across different areas.
The EEG records electrical activity in the brain from the scalp using electrodes placed according to the 10-20 system. There are different types of brain waves seen on EEG including alpha, beta, theta, and delta waves which vary in frequency and amplitude. Factors like age, consciousness, medications, and stimuli can influence the brain waves observed on EEG. Hans Berger first recorded human EEG waves in 1929, establishing EEG as a tool for examining brain function.
Graphic record heart sound - Phonogram.
Recording the sounds connected with the pumping action of heart.
Sound from heart – phonocardiogram
Instrument to measure this – phonocardiograph
Basic function – to pick up the different heart sound,filter the required and display.
The document discusses an electromagnetic blood flow meter. It operates based on electromagnetic induction principles, inducing an EMF in blood flowing through a vessel perpendicular to a magnetic field. Electrodes placed across the vessel measure this induced EMF, which is proportional to blood velocity. The small EMF signal is amplified for measurement and low pass filtered to determine average blood flow rate. Advantages include a linear dynamic range and no mechanical limitations for measuring high and low blood flows.
The document summarizes key aspects of electroencephalography (EEG). It describes how EEG detects electrical signals produced by the brain using electrodes placed on the scalp. It explains that EEG signals vary in amplitude and frequency depending on behavioral states. It also outlines the main components of an EEG machine, including electrodes, amplifiers, filters, and a writing system to record the brain's electrical activity.
Amplifiers and biopotential amplifiers newM. Raihan
What is Amplifiers?
Classification of Amplifiers
Based on number of stages
Single-stage Amplifier
Based on its output
ased on the input signals
Based on the frequency range
Based on the Coupling method
Based on the Transistor
Configuration
CE amplifier
CC amplifier
Comparaison between CB, CE
and CC Amplifies
Noise in Amplifier
Signal to Noise Ratio (SNR)
Biopotential Amplifiers
Typical bio-amplifier requirements
Voltage and Frequency
Range for Biopotentials
Electrocardiograph Amplifiers
Interference Reduction Techniques
This document discusses various types of EEG artifacts including physiological artifacts generated by the body and extraphysiological artifacts from external equipment or environment. It describes common artifacts like cardiac, electrode, eye blink, muscle activity and their characteristic appearances on EEG. The key is to ensure good preparation, electrode placement and monitoring for artifacts during EEG recording to obtain clean data for accurate interpretation.
The document discusses phonocardiography, which is the study of heart sounds using a phonocardiograph. A phonocardiograph is an instrument that picks up heart sounds, filters them, and displays the phonocardiogram, which is the graphic recording of heart sounds. There are two main types of heart sounds - normal sounds due to valve openings and closings, and abnormal murmurs due to turbulent blood flow. The document outlines the history and development of phonocardiography and the stethoscope, describes heart sound characteristics, and discusses the components of a phonocardiogram recording system.
Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields generated by neuronal brain activity. MEG uses very sensitive magnetometers to record these natural magnetic fields produced by the brain's electrical currents. Though brain signals appear irregular, they may be generated by deterministic nonlinear systems. MEG provides both high temporal resolution and excellent spatial resolution of brain function without exposure to radiation or invasive procedures.
EEG artifacts can arise from various physiological and extraphysiological sources other than brain activity. Physiological artifacts originate from the patient's own generator sources like eye movements, muscle activity, movement, and cardiac activity. Extraphysiological artifacts are externally generated, such as from medical devices, electrical equipment, or the environment. Common EEG artifacts include cardiac artifacts like ECG signals, ballistocardiographic artifacts from head or body movement, pacemaker signals, and pulse artifacts. Electrode artifacts can be transient pops or low frequency rhythms across electrodes from poor contact or movement. External artifacts include 50/60 Hz ambient noise, intravenous drips, and signals from devices like pumps and ventilators. Muscle and ocular artifacts
The document discusses electromyography (EMG), which is used to record and study the electrical activity of muscles. EMG uses surface or needle electrodes to measure bioelectric potentials from muscles. The measured signals are amplified and displayed. EMG can detect nerve lesions, muscle conditions, and reflex responses. However, the EMG signals can be affected by various artefacts like power line interference, electrocardiogram signals, movement, DC offset, and muscle crosstalk.
EEG - Montages, Equipment and Basic PhysicsRahul Kumar
This presentation discusses the 10-20 system of electrode placement, with its modifications. Also discussed are the Equipment Specifications, basic Physics and sources of interference
This document provides information about electroencephalography (EEG), electromyography (EMG), and patient monitoring. It discusses how EEG is used to measure brain activity through electrodes on the scalp. It describes the different frequency bands seen on EEG and how they relate to mental states. The document outlines the components of an EEG recording system and various EEG artifacts. It also discusses EMG and how it is used to measure muscle electrical activity. Finally, it covers patient monitoring systems, including bedside monitors, central monitoring stations, and the parameters that are measured like heart rate, blood pressure, respiration rate.
This document discusses patient monitoring systems and biotelemetry. It describes electrocardiogram (ECG) and blood pressure monitoring in hospitals. Intensive care unit (ICU) monitoring instruments that continuously measure vital signs are discussed. Biotelemetry systems that remotely transmit physiological data via radio frequency are then outlined, including the components of transmitters and receivers. Design considerations for biotelemetry systems using amplitude or frequency modulation are presented. Finally, both single-channel and multichannel biotelemetry systems are described.
The document discusses processing and noise cancellation of electrocardiogram (ECG) signals. It begins by explaining what an ECG is and how it is generated by the electrical activity of the heart. The ECG provides information about heart rate and the strength of the heart muscles. ECG signals are recorded using skin electrodes and contain noise from sources like power lines and electrode contact that must be removed. Common processing techniques include filtering using bandpass and adaptive filters to reduce noise and enhance the ECG waveform. Further analysis of the filtered ECG can detect heart abnormalities and conditions. Adaptive noise cancellation algorithms use a reference noise signal to minimize interference in the primary ECG input signal.
Calibration of an EEG machine involves checking various parameters to ensure accurate measurements. It is important as it allows correct interpretation of recordings and comparison to previous studies. Parameters checked include paper speed, pen alignment, centering and damping, time constant, high frequency filter, sensitivity, amplitude linearity, gain, noise level and more. Verifying these helps identify any issues needing adjustment and confirms the machine is functioning properly.
A Bioamplifier is an electrophysiological device, a variation of the instrumentation amplifier, used to gather and increase the signal integrity of physiologic electrical activity for output to various sources. It may be an independent unit, or integrated into the electrodes.
This document discusses two main applications of EEG waves: diagnosis of sleep disorders and brain-computer interfaces (BCIs). For sleep disorder diagnosis, EEG signals are analyzed to stage sleep and identify abnormalities. Statistical methods classify sleep stages over time. BCIs translate brain signals into commands, using visual evoked potentials from stimuli flickering at different frequencies to identify intended commands. Key issues discussed include reducing electrode numbers and optimizing frequency feature extraction from EEG signals to improve BCI accuracy and usability.
1. Impedance is opposition to alternating current flow and has two components: resistance and reactance. Resistance opposes direct current, while reactance depends on frequency and includes capacitance and inductance. (2) Because EEG contains strong AC signals, impedance rather than just resistance is measured. (3) Electrode impedance is measured by passing a small current between electrodes and is impacted by dead skin cells separating the electrode from living tissue.
This document discusses biopotentials and methods for measuring them. It begins with an introduction to biopotentials and what they are. It then discusses the mechanisms behind biopotentials, focusing on ion concentrations and how they generate electrical potentials. The rest of the document discusses specific measurement methods like ECG, EEG, EMG, EOG, and considerations for biopotential measurement like electronics, electrodes, and practices.
EEG detects brain wave activity through electrodes placed on the scalp. Brain waves fall into different frequency bands such as delta, theta, alpha, beta, and gamma, which correlate with cognitive and mental states. EEG signals are generated by excitatory and inhibitory neuron activity. EEG recordings involve placing electrodes on standardized scalp locations, amplifying and processing the signals, then analyzing them in frequency or time domains. Digital EEG offers advantages over analog systems like high precision timing and non-invasiveness. It provides a relatively inexpensive way to check brain functioning across different areas.
The document provides information about EEG basics. It discusses:
1. EEG records spontaneous electrical activity generated by cortical neurons. The EEG machine has multiple channels that simultaneously record signals from electrode pairs.
2. EEG machines have three main components - the sensor layer with electrodes, the acquisition layer consisting of amplifiers, and the connectivity layer. Standard electrode placement uses the 10-20 system.
3. EEG signals are amplified, filtered, digitized and can be analyzed to detect abnormal electrical discharges that may indicate conditions like epilepsy. Features like amplitude, frequency, and symmetry are evaluated.
This document summarizes the design of an EEG circuit and data acquisition system. It includes block diagrams of the EEG amplifier board and analog-to-digital converter board. The EEG amplifier uses a two-stage design with gains of 50 and 390. The proposed analog-to-digital converter is a Keithley KPCI-1307 card capable of 100k samples/second. Software options for the card include VHDL implementation on an Altera board or using DriverLINX APIs. Testing showed the system could successfully record eyebrow raises and eye blinks.
This document summarizes key concepts about EEG circuit design and analysis. It discusses electrode circuits, instrumentation amplifiers, chopper-stabilized low-noise amplifiers, two-stage op-amps, equivalent circuit models, EEG recordings from different conditions, hardware block diagrams, the Nyquist theorem, bipolar vs monopolar recordings, artifacts from EMG, eye blinks, EKG, line noise, reviewing EEG based on voltage, frequency, location, and transient events, normal and abnormal distributions of EEG data, constructing life span normative databases, and related BCI research goals and challenges.
An EEG is a test that detects electrical activity in the brain using sensors placed on the scalp. It is used to diagnose brain conditions like epilepsy and Alzheimer's. During an EEG, electrodes detect brain waves which are amplified and recorded. Abnormal brain wave patterns can indicate conditions affecting the electrical activity in the brain. A neurologist interprets the EEG results to look for normal and abnormal patterns to make a diagnosis.
This document discusses electroencephalography (EEG) and evoked potentials. It begins by describing EEG as a method for recording electrical brain activity using electrodes placed on the scalp. It then discusses how EEG is used to diagnose neurological conditions like epilepsy and brain tumors. The document outlines different brain wave patterns observed in EEG like alpha, beta, theta, and delta waves. It also discusses how EEG is performed and interpreted, potential artifacts, and different electrode montages. Finally, it describes evoked potentials as the electrical response of the brain to sensory stimulation, and summarizes different types of evoked potentials including visual, somatosensory, and auditory potentials.
The document provides information on electroencephalography (EEG) and magnetoencephalography (MEG). It discusses the history of EEG, how the signals are recorded, various montages used, neural basis of the signals, analysis methods for EEG including evoked potentials and artifacts. MEG is described as detecting the magnetic fields generated by electrical activity in the brain using SQUIDs, and its increased sensitivity to activity in sulcal walls compared to EEG. Key differences between the two methods are the orientation of measured fields relative to current flow in neurons.
1. An EEG requires electrodes to detect brain wave activity, amplifiers to magnify the small signals, filters to remove unwanted waves, and recording units to document the waves.
2. Proper electrode placement and preparation is important for EEG, including cleaning the scalp, using conductive gel, and ensuring good contact.
3. The 10-20 system standardizes electrode placement locations as a percentage of the skull size, and different areas are labeled based on lobe and laterality (odd numbers left, even right).
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document discusses techniques for recording electroencephalograms (EEGs). It begins by describing the basics of neuronal cell structure and electrical activity in the brain. It then discusses the international 10-20 system for positioning EEG electrodes on the scalp. The document outlines the key components of an EEG recording system, including biopotential electrodes, signal conditioning equipment to amplify and filter signals, and systems for recording and analyzing the EEG data. It also provides circuit diagrams to illustrate differential amplifier designs used in EEG machines to reduce interference from common-mode signals.
EEG records electrical activity of the brain through electrodes placed on the scalp. The 10-20 system is commonly used for electrode placement. EEG signals are amplified and filtered before being displayed. Activation procedures like photic stimulation and hyperventilation are used to provoke seizures. Sleep deprivation and induction can also increase the diagnostic yield of EEG by altering brain activity. The visual analysis of EEG considers factors like waveforms, distribution, and reactivity to diagnose abnormalities.
EEG Signal Classification using Deep Neural NetworkIRJET Journal
This document discusses using deep neural networks to classify EEG signals. It proposes using a convolutional neural network to analyze recurrence plots generated from EEG data in order to distinguish between focal and non-focal EEG signals. The recurrence plots are generated from EEG data collected from epilepsy patients. The convolutional neural network is then used to classify the recurrence plots to identify patterns associated with focal versus non-focal EEG signals. This classification of EEG signals could help doctors locate epileptic foci to inform treatment decisions.
Wavelet Based Feature Extraction Scheme Of Eeg Waveformshan pri
This document presents a project on wavelet based feature extraction of electroencephalography (EEG) signals. It discusses using wavelet transforms as an alternative to discrete Fourier transforms for feature extraction from EEG data. The objectives are to improve quality of life for those with disabilities through neuroprosthetics applications of brain-computer interfaces. Wavelet transforms provide advantages over short-time Fourier transforms like multi-resolution analysis and the ability to analyze non-stationary signals. The document outlines the methodology, which includes EEG signal acquisition, wavelet decomposition, coefficient computation, and signal reconstruction in MATLAB.
An EEG records electrical activity in the brain using electrodes placed on the scalp. It is used to diagnose epilepsy and other neurological conditions. During an EEG, electrodes detect voltage fluctuations corresponding to different brain wave patterns such as delta, theta, alpha, and beta waves. Abnormal wave patterns can indicate conditions like epilepsy. EEGs provide high temporal resolution to study brain activity but low spatial resolution. They are a non-invasive way to monitor brain function.
Denoising Techniques for EEG Signals: A ReviewIRJET Journal
The document reviews techniques for denoising EEG signals contaminated with artifacts. It discusses regression, blind source separation (BSS) including principal component analysis (PCA) and independent component analysis (ICA), wavelet transform, and empirical mode decomposition (EMD). Each method has benefits and limitations. Combining multiple current approaches can address individual constraints and provide superior outcomes than single algorithms alone by overcoming each other's limitations.
EEG records electric potentials from the brain using electrodes on the scalp. It has high temporal resolution but low spatial resolution. MEG records magnetic fields generated by neural activity using SQUID magnetometers and has similar properties as EEG. ERPs are extracted from EEG/MEG data by averaging time-locked responses to events and are characterized by polarity and latency. EEG/MEG/ERPs are useful for studying cognitive processes and clinical applications when high temporal resolution is required.
Clinical teaching on electroencephelographyAquiflal KM
The document discusses a clinical teaching session on electroencephalography (EEG) for 4th year nursing students. The session objectives were to define EEG, describe its indications, mechanism, procedure, and waveforms. EEG measures electrical activity in the brain using electrodes attached to the scalp. It is used to detect problems associated with brain disorders like seizures, tumors, or injuries. During an EEG, technicians attach electrodes to the scalp to record brain wave patterns over 30-60 minutes.
The document discusses compressive wideband power spectrum analysis for EEG signals using FastICA and neural networks. It first provides background on EEG signals and how they are measured. It then describes using FastICA to extract independent components from EEG signals related to detecting epileptic seizures. The independent components are then used to train a backpropagation neural network for effective detection of epileptic seizures. The proposed method involves preprocessing EEG signals, performing spectral estimation using FastICA, and classifying brain activity patterns using the neural network.
Denoising of EEG Signals for Analysis of Brain Disorders: A ReviewIRJET Journal
This document provides a review of techniques for denoising electroencephalogram (EEG) signals to remove noise and artifacts for improved analysis of brain disorders. It discusses how EEG signals are contaminated by various noise sources that can obscure important information. Several denoising techniques are examined, including independent component analysis (ICA), principal component analysis (PCA), wavelet-based denoising, and wavelet packet-based denoising. Wavelet transforms are highlighted as providing effective solutions for denoising non-stationary signals like EEG due to their ability to perform time-frequency analysis. The document concludes that wavelet methods, especially using wavelet packets, are useful for removing noise from EEG signals.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Anti-Universe And Emergent Gravity and the Dark UniverseSérgio Sacani
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDSSérgio Sacani
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...Sérgio Sacani
We present the JWST discovery of SN 2023adsy, a transient object located in a host galaxy JADES-GS
+
53.13485
−
27.82088
with a host spectroscopic redshift of
2.903
±
0.007
. The transient was identified in deep James Webb Space Telescope (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic followup with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red (
�
(
�
−
�
)
∼
0.9
) despite a host galaxy with low-extinction and has a high Ca II velocity (
19
,
000
±
2
,
000
km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low-
�
Ca-rich population. Although such an object is too red for any low-
�
cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
≲
1
�
) with
Λ
CDM. Therefore unlike low-
�
Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
�
truly diverge from their low-
�
counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
4. Electroencephalogram
An EEG machine is a device that records the electrical activity
of the brain. It contain electrodes that can detect brain activity
when placed on a subject’s scalp. The electrodes record the
brain wave patterns and the EEG machine sends the data to a
computer or cloud server.
7. ELECTRODES
Electrodes are used to record EEG
signals.
They are made of a conducting material
commonly metals with some underlying
conductive paste or gel to improve
contact with the skin.
An ideal electrode should transduce the
voltage underneath it without altering it in
any way.
However, real electrodes have limits on
their performance, including the
frequency range over which they are
accurate and the buildup of charge on
the electrode.
Gold or gold-plated electrodes have
commonly been used for scalp EEG
Recording.
8. (A) International 10-20 system for electrode
placement.
(B) 10-10 or 10% system for electrode placement. Ele
ctrodes in black have different names from the
corresponding electrodes of the International 10-20
system
(T7 = T3; T8 = T4; P7 = T5; P8 = T6).
9.
10. JACKBOX
Jackbox is the
electrode board
where each individual
pin of the electrodes is
plugged to pre-amplify
and convert the
analogue signal to one
that is digital.
11. ELECTRODE MONOTAGE SELECTOR
Montage means the placement of the electrodes. The EEG can be
monitored with either a bipolar montage or a referential one.
Bipolar means that you have two electrodes per one channel, so
you have a reference electrode for each channel.
12. EEG AMPLIFIERS
5.
The amplification factor is referred to
as gain and may be expressed as
Vout/ Vin
1.
Electrical signals produced by the brain
are in the order of microvolts.
4.
An amplifier multiplies an input voltage
By a constant usually lying in the range
of up to 1,000,000.
2.
They have to be magnified so that the
voltage changes can be given sufficient
power to be graphically displayed either
on paper or on a computer screen
3.
When measured directly at the cortical
surface, these voltages are on the order
of 10 mV.
13. FILTERS
The fact that the potential differences fluctuate as a function of time implies that the
recorded signals have a certain bandwidth. For the majority of EEG investigations the
recorded signal lies between 1 Hz and 70 Hz.
Information will be lost if the frequency response of the recording channel is narrower
than the frequency range of the EEG signal.
If the frequency range of the recording channel is wider than the bandwidth of the EEG
signal, noise in the recorded data will contain additional irrelevant information.
EEG recording channels are equipped with adjustable high pass and low pass filters by
which the frequency response can be restricted to the frequency band of interest.
For standard recordings, the low frequency filter should not be higher than 1 Hz with
the corresponding time constant of 0.16 s
14. USES OF FILTERS
Low-pass filter to filter out
frequencies above 40 or 50 Hz.
For standard recordings, the low
frequency filter should not be
higher than 1 Hz with the
corresponding time constant of
0.16 s
EEG signal processing is to
apply a high-pass filter to
filter out slow frequencies
less than 0.1 Hz or often
even 1 Hz.Distortion of higher
frequency components is also
possible when the high
frequency filter is set lower
than 70 Hz.
a notch filter is used to reject
the 60 Hz or 50 Hz power line
noise. The notch is a very
selective filter with a very high
rejection just for a tiny frequency b
and around the selected
frequency.
HIGHPASSFILTER NOTCHFILTERLOWPASSFILTER
15.
16. WRITE OUT
The final link between the patient and a
legible EEG tracing is the writer. In
conventional EEG machines, a
pen-ink-paper system is employed
The speed of the paper mechanism
should include 30 mm/s with at least the
additional speeds of 15 mm and 60 mm/s
selectable during operation.
The writing points of the different
channels should be aligned on a line
perpendicular to the direction of paper
travel without the use of special tools
and without the need for bending the
writer arms.
17. A sample EEG recording showing a focal spiketypical of a seizure
18. OUTPUT
The number of channels that an EEG machine
has is related to the number of electrodes used.
The more channels, the more detailed the brain
wave picture.This means that the output from
the machine is actually the difference in
electrical activity picked up by the two electrodes
19. Abnormal results from an electroencephalogram can indicate:
Migraines
Bleeding (haemorrhage)
Head injury
Tissue damage
Seizures
Swelling (edema)
Substance abuse
Sleep disorders
Tumours
21. Applications of EEG:
1. EEG is mainly used in studying the properties of cerebral and neural networks in
neurosciences.
2. It is used to monitor the neurodevelopment and sleep patterns of infants in ICU and e
nable the physician to use this information to enhance daily medical care.
3. In epilepsy, EEG is used to map brain areas and to receive localization information pri
or to a surgery.
4. The EEG neuro-feedback or EEG bio-feedback or EEG bio-feedback has many applic
ations such as treating for physiological disorders and neurological disorders such as epi
lepsy.
5. Many disorders as chronic anxiety, depression etc can be found out using as EEG pat
tern.
23. Advantages of EEG:
1.Theyare functionallyfast, relativelycheap and safe wayof checkingthe functioningof
different areasof brain.
2. High precision time measurements
3.Today'sEEGtechnologycan accuratelydetect brainactivityat a resolution of a single
millisecond.
4.EEGelectrodes are simplystuckonto the scalp. It is therefore a non-invasive procedure.
5. EEG is simple to operate.
25. Disadvantages of EEG:
1.The main disadvantage of EEG recording is poor spatial resolution.
2.The EEG signal is not useful for pin-pointing the exact source of activity. In other
words they are not very exact.
3.EEG waveform does not researchers to distinguish between activities originating in different
but closely adjacent locations.
27. Features of EEG:
• Hardware costs are lower when comparedwithother imagingtechniquessuchas
MRIscanning.
• EEGsensors can be deployed intoa wide varietyof environments.
• EEGallows higher temporalresolution on theorder of milliseconds.
• EEGis relativelytolerable to subject movementsas comparedto MRI.
• The silent nature of EEGallowsfor better study of the responses.
• In EEGsome voltage componentscan be detected even when the subject is not
respondingto stimuli.