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Intro to EEG - Electroencephalography

Intro to EEG - Electroencephalography



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Are there any introductory level text, researches or video for "How to learn EEG"?

Those material should include dictionary of terms, what waves mean, how to connect some activity in waves to brain activity… etc

There are lot of books which covered this subject, I am looking for something very basic and easy to comprehend.


Practical approach to electroencephalography by Mark H. Libenson.
ISBN: 978-0-7506-7478-2

This should address your issues:

  • dictionary of terms
  • what waves mean
  • how to connect some activity in waves to brain activity… etc

Why consult encyclopedic references when you only need the essentials? Practical Approach to Electroencephalography, by Mark H. Libenson, MD, equips you with just the right amount of guidance you need for obtaining optimal EEG results! It presents a thorough but readable guide to EEGs, explaining what to do, what not to do, what to look for, and how to interpret the results. It also goes beyond the technical aspects of performing EEGs by providing case studies of the neurologic disorders and conditions in which EEGs are used, making this an excellent learning tool. Abundant EEG examples throughout help you to recognize normal and abnormal EEGs in all situations.

Presents enough detail and answers to questions and problems encountered by the beginner and the non-expert. Uses abundant EEG examples to help you recognize normal and abnormal EEGs in all situations. Provides expert pearls from Dr. Libenson that guide you in best practices in EEG testing. Features a user-friendly writing style from a single author that makes learning easy. Examines the performance of EEGs-along with the disorders for which they're performed-for a resource that considers the patient and not just the technical aspects of EEGs. Includes discussions of various disease entities, like epilepsy, in which EEGs are used, as well as other special issues, to equip you to handle more cases.

Another review here

Practical Approach to Electroencephalography differs from other EEG texts of its level in that it takes the time to explain itself, resulting in a far more readable albeit less portable text than other condensed introductory texts.

Book Preview

This link will provide you with access to review the book yourself, before purchasing.


All You Need to Know About Electroencephalography (EEG)

An EEG (electroencephalogram) is a special test that is used to record brain waves in order to diagnose seizures or epilepsy.

The Electrical Activity of the Brain

The brain consists of billions of cells, half of which are neurons, half of which help and facilitate the activity of neurons. These neurons are densely interconnected via synapses, which act as gateways of inhibitory or excitatory activity.

Any synaptic activity generates a subtle electrical impulse referred to as a postsynaptic potential. Of course, the burst of a single neuron is difficult to reliably detect without direct contact with it. However, whenever thousands of neurons fire in sync, they generate an electrical field which is strong enough to spread through tissue, bone, and skull. Eventually, it can be measured on the head surface.

Use of an EEG Machine

Electroencephalography (EEG) is a technique with over a hundred years of history, and while it was originally used more strictly in the fields of psychology, medicine, and neuroscience, it is widely used today in gaming, human-computer-interaction, neuromarketing, simulations, and beyond.

Due to this increased use and demand for high-quality EEG devices, there are now numerous companies that are able to cater to the specific needs of EEG users. Each offers something unique for the consumer – whether it’s the number of channels, a stationary or portable device, the predefined metrics offered, or of course the price.

How is an EEG done?

The EEG test is done by applying various electrodes to the scalp. These electrodes are applied with glue and will remain on the scalp during your entire stay at the hospital. Once the electrodes are applied they will be plugged into a box and placed in a backpack. This backpack will need to remain with your child at all times.

The EEG hookup can take approximately one hour, depending on the cooperation your child is. We may wrap them up in a blanket roll to keep them still and use distraction (movies, books, etc.).

We cannot use any sedation as it may change the EEG recording results. The EEG wires may become loose or dislodged and need to be re-glued at times. This can occur during the day or night. It is important for the EEG tech to repair the wire(s) as soon as possible to make sure the recording is accurate.

How is the glue removed?

The electrodes will be removed with a special solution. Hair can then be washed with any shampoo/conditioner we have “No More Tangles” to help comb through glue. It may take a few washes to come out. Please let your nurse know if there are any red or open areas on the scalp. Please remind your child to try not to itch, pull, or remove any of the wires.


EEG Sessions: An Introduction to EEG

Welcome to the Bernier Lab’s new blog series “EEG Sessions”, where we will share with you EEG related topics, our thoughts on recent papers, and other relevant information from the Bernier Lab. To start off, we will describe why we measure brain activity using electroencephalography (EEG) and what the data represents.

Here at the Bernier Lab, we research genotypes and phenotypes related to autism spectrum disorder (ASD), which means that we capture a thorough and multifaceted picture of each participant by integrating genetic sequencing, behavioral assessments, and neurophysiological measures. One of the neurophysiological measures we use regularly is electroencephalogram (EEG). EEG is a noninvasive procedure that tracks and records brain waves through electrodes that are affixed to the scalp. In our lab, we use an EEG netcap with hundreds of recording sites that is similar in structure to a swimming cap. EEG collects information about brain activity down to the millisecond(!), but it is hard to determine exactly where the brain signals originate within the brain. In other words, EEG provides a very reliable representation of when neural activity occurs, but we have to use discretion to determine where it occurs.

To better understand how brain activity relates to specific aspects of cognitive function, we control what our participants see or hear. Sometimes we show pictures or movies (with or without sounds). Other times we ask participants to sit quietly or close their eyes. We call this method “event-related potentials” (ERP), which refers to the fact that we record brain signals (i.e., potentials) in response to specific stimuli (i.e., events, such as a picture of a face). Stimuli may be visual (e.g. images flashing on a screen) or auditory (e.g. different beeps, tones, and sounds). We examine ERP responses in milliseconds and refer to specific parts of the brain activity, or brain waves, as ‘components.’ Components of interest are measured both in latency (When does the brain response occur?) and amplitude (How strong is the response?). There are predictable brain responses that occur at specific time points, often with expected amplitudes. In our ERP analyses, we measure selected components and compare them across different groups (e.g. children with ASD versus typically developing children).

In conducting EEG experiments, we hope to establish specific biomarkers that may aid in the diagnoses of autism. Currently, ASD can only be diagnosed through a series of clinical assessments. While these measures are reliable, they are built for toddlers and older children, simply due to the nature of the activities involved. Trying to diagnose children who are nonverbal or have cognitive impairments is especially challenging. Biomarkers could aid with diagnosis of children who are difficult to evaluate with traditional clinical ASD measures, including infants. This is especially significant because early intervention is critical and effective. If, for example, we found that a group of children with ASD share a brain signature that is different from other groups of children with ASD, we could use that knowledge to aid in early diagnosis and intervention. Designated biomarkers that aid early diagnosis of autism could also provide insight about anticipated behaviors and challenges for individual children. Such knowledge would be valuable in determining which treatments and therapies would be most beneficial for that child.

Establishing biomarkers for autism is no easy task. We know that ASD is a complex, multifaceted disorder, and as such, there will always be a number of factors that are difficult or impossible to address. Isolating ERPs can also prove challenging due to inherent brain differences associated with chronological age and developmental stage. Genetic events, comorbid disorders, and the heterogeneity of ASD further add to the complexity of establishing reliable biomarkers of autism. As a result, it is not uncommon for research to have mixed or contradictory findings. This is certainly true in EEG/ERP research. One of the goals of our EEG journal club is to think critically about existing research in order to improve our own methods and produce reliable findings with clinical applications.


Results

Driving Data Classification Results

No instances of simulator sickness were observed in our experiments. The 23 participants completed 75 driving tasks and hence 75 samples of driving data and EEG data were acquired. The 7-dimension feature vectors of the driving data, i.e., steering wheel rotation angle, angular velocity, angular acceleration, total driving time, vehicle velocity, the number of collisions and the number of lane excursions, were calculated and processed by PCA and reduced to 2-dimensions. The Calinski-Harabasz score was utilized to determine the optimal number of clusters, which was 3 for our dataset (Figure 3). In addition, previous studies have suggested that driving style can be classified into Aggressive type, Moderate type, and Conservative type (Chu et al., 2017 Deng et al., 2017 Li et al., 2017 Palat et al., 2019), accordingly in this paper K is 3. Three random samples were selected as the initial clustering centroids and the samples were clustered into three driving style groups via the K-means algorithm (Figure 4).

Figure 3. Calinski-Harabasz score corresponding to different number of clusters.


Techniques Involving Magnetic Fields

Figure 3. An fMRI shows activity in the brain over time. This image represents a single frame from an fMRI. (credit: modification of work by Kim J, Matthews NL, Park S.)

In magnetic resonance imaging (MRI), a person is placed inside a machine that generates a strong magnetic field. The magnetic field causes the hydrogen atoms in the body’s cells to move. When the magnetic field is turned off, the hydrogen atoms emit electromagnetic signals as they return to their original positions. Tissues of different densities give off different signals, which a computer interprets and displays on a monitor.

Functional magnetic resonance imaging (fMRI) operates on the same principles, but it shows changes in brain activity over time by tracking blood flow and oxygen levels. The fMRI provides more detailed images of the brain’s structure, as well as better accuracy in time, than is possible in PET scans (Figure 3). With their high level of detail, MRI and fMRI are often used to compare the brains of healthy individuals to the brains of individuals diagnosed with psychological disorders. This comparison helps determine what structural and functional differences exist between these populations.

Link to Learning

Visit this virtual lab to learn more about MRI and fMRI.


Northwestern Now

6-month-old infants’ neural responses to human speech and lemur calls, provide new insight into how the link to cognition becomes so rapidly attuned to human speech

New research from Northwestern University provides the first evidence of underlying neural mechanisms that support infants’ acquisition of the unique language-cognition link in humans.

Even before infants can roll over in their cribs, research has shown that listening to language boosts their cognition. For infants as young as 3 months, listening to human speech supports their ability to form categories of objects (like “dog” or “bottle”).

In these first months, it is not just human language that can do this — listening to vocalizations of non-human primates like lemur calls also supports infant cognition. But by 6 months, infants’ responses to lemur calls fade out, and only listening to human speech continues to offer this cognitive advantage.

Cognitive psychologist Sandra Waxman, research specialist Kali Woodruff Carr and their colleagues identified developmental changes in 4- and 6-month-old infants’ neural responses to human speech and lemur calls, providing new insight into how the link to cognition becomes so rapidly attuned to human speech.

Woodruff Carr is a research specialist in the Institute for Policy Research (IPR). Waxman holds the Louis W. Menk Chair in psychology, is a professor of cognitive psychology and an IPR fellow.

“Measuring neural activity can reveal unique insights into the rapidly developing cognitive processes that are difficult to assess in preverbal infants,” said Woodruff Carr, the article’s first author. “These findings give us a glimpse into what goes on in the infant brain as babies begin to learn what sounds are for them and how language refers to the world around them.”

To learn about infants’ neural responses to language and lemur calls, Waxman, Woodruff Carr, and their co-authors used EEG (electroencephalography) to measure infants’ neural responses as they listened to human speech and lemur calls.

The researchers discovered emerging differences in infants’ neural activity. Human speech and calls from lemurs, who are some of humans’ closest evolutionary relatives, each engage early neural components of infants’ attention, but by the time they are 6 months old, they do so in distinct ways. Between 4 and 6 months, infants’ neural attention while listening to speech is enhanced, but their attention while listening to lemur calls is suppressed.

These results offer novel insights into how listening to language supports early cognition. They also illuminate the rapid organization of cortical networks in the infant brain for processing speech and language.

“This new evidence is exciting because it permits us to look ‘under the hood,’ to discover how the infant brain is modulated by listening to language,” Waxman explained. “Without non-invasive neural measures like EEG, we would not have been able to discover how infants so rapidly form the language-cognition link.”

“Developmental changes in auditory-evoked neural activity underlie infants’ links between language and cognition” published online June 1 in the Developmental Science journal.


Electroencephalogram (EEG)

Electroencephalogram (EEG) is a recording of the continuous electrical activity of the brain made by electrodes positioned on the scalp. It has many applications for clinical practice and both basic and applied research. The science of recording, analyzing, and interpreting EEG is part of a larger science called psychophysiology, which has its roots in both medicine and psychology. EEG is an important tool in cognitive neuroscience, the field of study that seeks to link human cognition and behavior with specific brain structures and processes. EEG has been used in the diagnosis of epilepsy and other neurological disorders and can also be used as a marker for the presence of numerous developmental abnormalities including, but not limited to, sensory and motor disorders. More recently, EEG has been used to study a variety of biologically based psychological disorders including depression, anxiety, and attention-deficit hyperactivity disorder (ADHD). EEG has also been used experimentally to learn about the cortical mechanisms involved in arousal, vigilance, mood regulation, and even higher cognitive functions such as language and mathematics. With the recent inventions of the related technologies of event-related potentials (ERPs), high-density electrode arrays, and computer-assisted topographic analysis (brain mapping), EEG promises to be a major tool in the neurosciences for years to come.

The biological source of an EEG recording is the postsynaptic membrane potentials of millions of pyramidal neurons that help to compose the human brain, or neocortex. The cells are organized into functional groups called microcolumns, which act as one unit when processing information. Because groups of neurons fire together, their tiny voltages summate and produce enough electrical activity to pass through the resistive mediums of brain tissue, skull, and scalp. Pyramidal cells, named for their triangular shape, are aligned so that their bodies are perpendicular to the surface of the scalp. This orientation means that for cells on the gyri (bumps) and some in the sulci (valleys) of the cortical surface their electrical fields project out to the scalp where they can be recorded.

The basic science underlying the EEG is that whenever an electrical current is passed through a circuit, its amplitude (measured on the y-axis of a graph) can be measured continuously at any point in time (measured in the x-axis) by a device generally known as a galvanometer. Early recordings were made with a few metallic electrodes that were filled with conductive paste and attached to the scalp, with the resultant deviations in voltage plotted with an attached ink pen

on a continuously scrolling drum or sheet of paper. Modern EEG recording is accomplished with multiple electrodes (as many as 256 at once), often positioned with the aid of an elastic cap, and the data are collected and analyzed entirely by microcomputer.

Hans Berger invented the method of human EEG in the 1920s, based upon previous work in animals by Richard Caton in the 1870s. These individuals demonstrated that the brain, like any living system, generates electrical potentials with regular patterns. The dominant patterns are labeled according to frequency and are called delta (.5–4 Hertz [Hz]), theta (4−7 Hz), alpha (8−13 Hz), and beta (13−30 Hz). Berger showed that these patterns are sensitive to both external cues and the internal states of the individual, such as level of arousal. For instance, alpha is present in conditions of relaxed wakefulness and can be suppressed by concentration on a difficult cognitive task. Delta and theta are hallmarks for the deeper stages of sleep, and abnormalities in the frequencies have been demonstrated in children with attention problems, depressed patients, and a variety of other disorders.


Analyzing an Electroencephalography Report (Eeg)…

Analyzing an Electroencephalography Report (EEG)…
The importance of complete and well recorded data in analyzing an EEG The summary of findings is only as detailed as the data reported.

Introduction
The electroencephalogram, also known as the EEG is the recording of electrical activity of the brain in various states of rest or stimulation. The test is performed using the electroencephalography machine which records the brainwaves in Hz or cycles per second. The characteristics of normal brainwave have been established. Their frequency is typically between 1 and 30 hertz, having a dominant rhythm of 10 HZ and average amplitude (the height of the wave) of 20 to 100 micro volts. Not surprising, the different areas of the brain demonstrate varied frequencies. For example, the waves of the occipital lobe (processes visual stimuli) have a lower frequency than the frontal and parietal lobes (process cognitive function, speech and language and soma sensory processes, respectfully). There are four waves which are typically studied in humans. The Alpha waves which have a frequency range of 8 to 13 HZ and are seen when the subject is in a relaxed state with eyes closed. The Beta waves range 14 to 30 HZ, have lower amplitude and are generally seen when the subject is in an attentive or alert state. The Delta wave is a very large wave (high amplitude) with a frequency of 4 HZ or less and is found when the subject is in deep sleep. Theta waves are also large with a frequency of 4 to 7 Hz normal in children, but abnormal in adults. In the ADI activity the Alpha waves were the focus for analysis. The EEG was performed using the ADI system to look at the effect of various interfering signals, examine the effect on alpha waves caused by opening and shutting the eyes, how alpha waves are affected by performing simple math equations as well as the effects on the brainwaves while listening to rock music. The exercise is used to show the students what is already published about what is known about the brain waves of humans. Materials and Methods

A computer system with ADInstruments Lab tutor software, PowerLab, Shielded Bio Amp Cable & snap-connect Shielded Lead Wires, EEG Flat electrodes, Electrode cream or past, Alcohol swabs, ink pen, abrasive pads and gel, adhesive tape and self-adhesive elastic bandage. packet titled “Electroencephalography (EEG)”, SPB11c, 9 February 2009 and LabTutor: EEG Report dated 20 July 2012, citing the volunteer as “Ashley”. Given what is known about Alpha waves the anticipation is to see the Alpha waves change with the changes in stimuli. A consideration in analysis is to first know the waves of the artifacts present at the time of the study. Artifacts are the unwanted signals caused by the volunteer moving in ways that are not requested by the study as well as external causes such as electrical fields of equipment or power supplies in the immediate area of the room. Four exercises were performed on the student volunteer, Ashley. Exercise 1:, Recognizing Artifacts, Exercise 2: Alpha & Beta Rhythm, Exercise 3: Effects of Mental Activity and Exercise 4: Effect of Auditory Stimulation. According to the data provided, the volunteer was asked to lie on her back, two electrodes were placed on the forehead, just below the hairline and a third electrode was to be placed on the back of the head in the area of the occipital lobe. The cables attached to the electrodes are terminated at the ADI machine. Preset software is utilized in these exercises, I suspect to lessen the need for the students to know the detailed technical aspects of the machinery and the software used to perform the study. In Exercise 1, the volunteer was asked to blink her eyes in rapid succession, move the eyes up, down and sideways and move her head in a repeated fashion during which the facilitator is to use the computer to annotate the start and stop of such.


How is EEG applied?

The application range for EEG is extremely wide, ranging from clinical applications (e.g., diagnosis of neurodegenerative brain diseases [1]) to engineering projects (e.g., Brain-Computer-Interfaces [2]), academic human behavior research (e.g., cognitive psychology [3, 4]), to commercial human behavior research (e.g. neuromarketing [5]).

Each of these research areas use EEG to trace electrical signals or from the brain, driven by the firing of neurons.


Electroencephalogram (EEG)

Electroencephalogram (EEG) is a recording of the continuous electrical activity of the brain made by electrodes positioned on the scalp. It has many applications for clinical practice and both basic and applied research. The science of recording, analyzing, and interpreting EEG is part of a larger science called psychophysiology, which has its roots in both medicine and psychology. EEG is an important tool in cognitive neuroscience, the field of study that seeks to link human cognition and behavior with specific brain structures and processes. EEG has been used in the diagnosis of epilepsy and other neurological disorders and can also be used as a marker for the presence of numerous developmental abnormalities including, but not limited to, sensory and motor disorders. More recently, EEG has been used to study a variety of biologically based psychological disorders including depression, anxiety, and attention-deficit hyperactivity disorder (ADHD). EEG has also been used experimentally to learn about the cortical mechanisms involved in arousal, vigilance, mood regulation, and even higher cognitive functions such as language and mathematics. With the recent inventions of the related technologies of event-related potentials (ERPs), high-density electrode arrays, and computer-assisted topographic analysis (brain mapping), EEG promises to be a major tool in the neurosciences for years to come.

The biological source of an EEG recording is the postsynaptic membrane potentials of millions of pyramidal neurons that help to compose the human brain, or neocortex. The cells are organized into functional groups called microcolumns, which act as one unit when processing information. Because groups of neurons fire together, their tiny voltages summate and produce enough electrical activity to pass through the resistive mediums of brain tissue, skull, and scalp. Pyramidal cells, named for their triangular shape, are aligned so that their bodies are perpendicular to the surface of the scalp. This orientation means that for cells on the gyri (bumps) and some in the sulci (valleys) of the cortical surface their electrical fields project out to the scalp where they can be recorded.

The basic science underlying the EEG is that whenever an electrical current is passed through a circuit, its amplitude (measured on the y-axis of a graph) can be measured continuously at any point in time (measured in the x-axis) by a device generally known as a galvanometer. Early recordings were made with a few metallic electrodes that were filled with conductive paste and attached to the scalp, with the resultant deviations in voltage plotted with an attached ink pen

on a continuously scrolling drum or sheet of paper. Modern EEG recording is accomplished with multiple electrodes (as many as 256 at once), often positioned with the aid of an elastic cap, and the data are collected and analyzed entirely by microcomputer.

Hans Berger invented the method of human EEG in the 1920s, based upon previous work in animals by Richard Caton in the 1870s. These individuals demonstrated that the brain, like any living system, generates electrical potentials with regular patterns. The dominant patterns are labeled according to frequency and are called delta (.5–4 Hertz [Hz]), theta (4−7 Hz), alpha (8−13 Hz), and beta (13−30 Hz). Berger showed that these patterns are sensitive to both external cues and the internal states of the individual, such as level of arousal. For instance, alpha is present in conditions of relaxed wakefulness and can be suppressed by concentration on a difficult cognitive task. Delta and theta are hallmarks for the deeper stages of sleep, and abnormalities in the frequencies have been demonstrated in children with attention problems, depressed patients, and a variety of other disorders.


Analyzing an Electroencephalography Report (Eeg)…

Analyzing an Electroencephalography Report (EEG)…
The importance of complete and well recorded data in analyzing an EEG The summary of findings is only as detailed as the data reported.

Introduction
The electroencephalogram, also known as the EEG is the recording of electrical activity of the brain in various states of rest or stimulation. The test is performed using the electroencephalography machine which records the brainwaves in Hz or cycles per second. The characteristics of normal brainwave have been established. Their frequency is typically between 1 and 30 hertz, having a dominant rhythm of 10 HZ and average amplitude (the height of the wave) of 20 to 100 micro volts. Not surprising, the different areas of the brain demonstrate varied frequencies. For example, the waves of the occipital lobe (processes visual stimuli) have a lower frequency than the frontal and parietal lobes (process cognitive function, speech and language and soma sensory processes, respectfully). There are four waves which are typically studied in humans. The Alpha waves which have a frequency range of 8 to 13 HZ and are seen when the subject is in a relaxed state with eyes closed. The Beta waves range 14 to 30 HZ, have lower amplitude and are generally seen when the subject is in an attentive or alert state. The Delta wave is a very large wave (high amplitude) with a frequency of 4 HZ or less and is found when the subject is in deep sleep. Theta waves are also large with a frequency of 4 to 7 Hz normal in children, but abnormal in adults. In the ADI activity the Alpha waves were the focus for analysis. The EEG was performed using the ADI system to look at the effect of various interfering signals, examine the effect on alpha waves caused by opening and shutting the eyes, how alpha waves are affected by performing simple math equations as well as the effects on the brainwaves while listening to rock music. The exercise is used to show the students what is already published about what is known about the brain waves of humans. Materials and Methods

A computer system with ADInstruments Lab tutor software, PowerLab, Shielded Bio Amp Cable & snap-connect Shielded Lead Wires, EEG Flat electrodes, Electrode cream or past, Alcohol swabs, ink pen, abrasive pads and gel, adhesive tape and self-adhesive elastic bandage. packet titled “Electroencephalography (EEG)”, SPB11c, 9 February 2009 and LabTutor: EEG Report dated 20 July 2012, citing the volunteer as “Ashley”. Given what is known about Alpha waves the anticipation is to see the Alpha waves change with the changes in stimuli. A consideration in analysis is to first know the waves of the artifacts present at the time of the study. Artifacts are the unwanted signals caused by the volunteer moving in ways that are not requested by the study as well as external causes such as electrical fields of equipment or power supplies in the immediate area of the room. Four exercises were performed on the student volunteer, Ashley. Exercise 1:, Recognizing Artifacts, Exercise 2: Alpha & Beta Rhythm, Exercise 3: Effects of Mental Activity and Exercise 4: Effect of Auditory Stimulation. According to the data provided, the volunteer was asked to lie on her back, two electrodes were placed on the forehead, just below the hairline and a third electrode was to be placed on the back of the head in the area of the occipital lobe. The cables attached to the electrodes are terminated at the ADI machine. Preset software is utilized in these exercises, I suspect to lessen the need for the students to know the detailed technical aspects of the machinery and the software used to perform the study. In Exercise 1, the volunteer was asked to blink her eyes in rapid succession, move the eyes up, down and sideways and move her head in a repeated fashion during which the facilitator is to use the computer to annotate the start and stop of such.


All You Need to Know About Electroencephalography (EEG)

An EEG (electroencephalogram) is a special test that is used to record brain waves in order to diagnose seizures or epilepsy.

The Electrical Activity of the Brain

The brain consists of billions of cells, half of which are neurons, half of which help and facilitate the activity of neurons. These neurons are densely interconnected via synapses, which act as gateways of inhibitory or excitatory activity.

Any synaptic activity generates a subtle electrical impulse referred to as a postsynaptic potential. Of course, the burst of a single neuron is difficult to reliably detect without direct contact with it. However, whenever thousands of neurons fire in sync, they generate an electrical field which is strong enough to spread through tissue, bone, and skull. Eventually, it can be measured on the head surface.

Use of an EEG Machine

Electroencephalography (EEG) is a technique with over a hundred years of history, and while it was originally used more strictly in the fields of psychology, medicine, and neuroscience, it is widely used today in gaming, human-computer-interaction, neuromarketing, simulations, and beyond.

Due to this increased use and demand for high-quality EEG devices, there are now numerous companies that are able to cater to the specific needs of EEG users. Each offers something unique for the consumer – whether it’s the number of channels, a stationary or portable device, the predefined metrics offered, or of course the price.

How is an EEG done?

The EEG test is done by applying various electrodes to the scalp. These electrodes are applied with glue and will remain on the scalp during your entire stay at the hospital. Once the electrodes are applied they will be plugged into a box and placed in a backpack. This backpack will need to remain with your child at all times.

The EEG hookup can take approximately one hour, depending on the cooperation your child is. We may wrap them up in a blanket roll to keep them still and use distraction (movies, books, etc.).

We cannot use any sedation as it may change the EEG recording results. The EEG wires may become loose or dislodged and need to be re-glued at times. This can occur during the day or night. It is important for the EEG tech to repair the wire(s) as soon as possible to make sure the recording is accurate.

How is the glue removed?

The electrodes will be removed with a special solution. Hair can then be washed with any shampoo/conditioner we have “No More Tangles” to help comb through glue. It may take a few washes to come out. Please let your nurse know if there are any red or open areas on the scalp. Please remind your child to try not to itch, pull, or remove any of the wires.


Northwestern Now

6-month-old infants’ neural responses to human speech and lemur calls, provide new insight into how the link to cognition becomes so rapidly attuned to human speech

New research from Northwestern University provides the first evidence of underlying neural mechanisms that support infants’ acquisition of the unique language-cognition link in humans.

Even before infants can roll over in their cribs, research has shown that listening to language boosts their cognition. For infants as young as 3 months, listening to human speech supports their ability to form categories of objects (like “dog” or “bottle”).

In these first months, it is not just human language that can do this — listening to vocalizations of non-human primates like lemur calls also supports infant cognition. But by 6 months, infants’ responses to lemur calls fade out, and only listening to human speech continues to offer this cognitive advantage.

Cognitive psychologist Sandra Waxman, research specialist Kali Woodruff Carr and their colleagues identified developmental changes in 4- and 6-month-old infants’ neural responses to human speech and lemur calls, providing new insight into how the link to cognition becomes so rapidly attuned to human speech.

Woodruff Carr is a research specialist in the Institute for Policy Research (IPR). Waxman holds the Louis W. Menk Chair in psychology, is a professor of cognitive psychology and an IPR fellow.

“Measuring neural activity can reveal unique insights into the rapidly developing cognitive processes that are difficult to assess in preverbal infants,” said Woodruff Carr, the article’s first author. “These findings give us a glimpse into what goes on in the infant brain as babies begin to learn what sounds are for them and how language refers to the world around them.”

To learn about infants’ neural responses to language and lemur calls, Waxman, Woodruff Carr, and their co-authors used EEG (electroencephalography) to measure infants’ neural responses as they listened to human speech and lemur calls.

The researchers discovered emerging differences in infants’ neural activity. Human speech and calls from lemurs, who are some of humans’ closest evolutionary relatives, each engage early neural components of infants’ attention, but by the time they are 6 months old, they do so in distinct ways. Between 4 and 6 months, infants’ neural attention while listening to speech is enhanced, but their attention while listening to lemur calls is suppressed.

These results offer novel insights into how listening to language supports early cognition. They also illuminate the rapid organization of cortical networks in the infant brain for processing speech and language.

“This new evidence is exciting because it permits us to look ‘under the hood,’ to discover how the infant brain is modulated by listening to language,” Waxman explained. “Without non-invasive neural measures like EEG, we would not have been able to discover how infants so rapidly form the language-cognition link.”

“Developmental changes in auditory-evoked neural activity underlie infants’ links between language and cognition” published online June 1 in the Developmental Science journal.


Techniques Involving Magnetic Fields

Figure 3. An fMRI shows activity in the brain over time. This image represents a single frame from an fMRI. (credit: modification of work by Kim J, Matthews NL, Park S.)

In magnetic resonance imaging (MRI), a person is placed inside a machine that generates a strong magnetic field. The magnetic field causes the hydrogen atoms in the body’s cells to move. When the magnetic field is turned off, the hydrogen atoms emit electromagnetic signals as they return to their original positions. Tissues of different densities give off different signals, which a computer interprets and displays on a monitor.

Functional magnetic resonance imaging (fMRI) operates on the same principles, but it shows changes in brain activity over time by tracking blood flow and oxygen levels. The fMRI provides more detailed images of the brain’s structure, as well as better accuracy in time, than is possible in PET scans (Figure 3). With their high level of detail, MRI and fMRI are often used to compare the brains of healthy individuals to the brains of individuals diagnosed with psychological disorders. This comparison helps determine what structural and functional differences exist between these populations.

Link to Learning

Visit this virtual lab to learn more about MRI and fMRI.


Results

Driving Data Classification Results

No instances of simulator sickness were observed in our experiments. The 23 participants completed 75 driving tasks and hence 75 samples of driving data and EEG data were acquired. The 7-dimension feature vectors of the driving data, i.e., steering wheel rotation angle, angular velocity, angular acceleration, total driving time, vehicle velocity, the number of collisions and the number of lane excursions, were calculated and processed by PCA and reduced to 2-dimensions. The Calinski-Harabasz score was utilized to determine the optimal number of clusters, which was 3 for our dataset (Figure 3). In addition, previous studies have suggested that driving style can be classified into Aggressive type, Moderate type, and Conservative type (Chu et al., 2017 Deng et al., 2017 Li et al., 2017 Palat et al., 2019), accordingly in this paper K is 3. Three random samples were selected as the initial clustering centroids and the samples were clustered into three driving style groups via the K-means algorithm (Figure 4).

Figure 3. Calinski-Harabasz score corresponding to different number of clusters.


EEG Sessions: An Introduction to EEG

Welcome to the Bernier Lab’s new blog series “EEG Sessions”, where we will share with you EEG related topics, our thoughts on recent papers, and other relevant information from the Bernier Lab. To start off, we will describe why we measure brain activity using electroencephalography (EEG) and what the data represents.

Here at the Bernier Lab, we research genotypes and phenotypes related to autism spectrum disorder (ASD), which means that we capture a thorough and multifaceted picture of each participant by integrating genetic sequencing, behavioral assessments, and neurophysiological measures. One of the neurophysiological measures we use regularly is electroencephalogram (EEG). EEG is a noninvasive procedure that tracks and records brain waves through electrodes that are affixed to the scalp. In our lab, we use an EEG netcap with hundreds of recording sites that is similar in structure to a swimming cap. EEG collects information about brain activity down to the millisecond(!), but it is hard to determine exactly where the brain signals originate within the brain. In other words, EEG provides a very reliable representation of when neural activity occurs, but we have to use discretion to determine where it occurs.

To better understand how brain activity relates to specific aspects of cognitive function, we control what our participants see or hear. Sometimes we show pictures or movies (with or without sounds). Other times we ask participants to sit quietly or close their eyes. We call this method “event-related potentials” (ERP), which refers to the fact that we record brain signals (i.e., potentials) in response to specific stimuli (i.e., events, such as a picture of a face). Stimuli may be visual (e.g. images flashing on a screen) or auditory (e.g. different beeps, tones, and sounds). We examine ERP responses in milliseconds and refer to specific parts of the brain activity, or brain waves, as ‘components.’ Components of interest are measured both in latency (When does the brain response occur?) and amplitude (How strong is the response?). There are predictable brain responses that occur at specific time points, often with expected amplitudes. In our ERP analyses, we measure selected components and compare them across different groups (e.g. children with ASD versus typically developing children).

In conducting EEG experiments, we hope to establish specific biomarkers that may aid in the diagnoses of autism. Currently, ASD can only be diagnosed through a series of clinical assessments. While these measures are reliable, they are built for toddlers and older children, simply due to the nature of the activities involved. Trying to diagnose children who are nonverbal or have cognitive impairments is especially challenging. Biomarkers could aid with diagnosis of children who are difficult to evaluate with traditional clinical ASD measures, including infants. This is especially significant because early intervention is critical and effective. If, for example, we found that a group of children with ASD share a brain signature that is different from other groups of children with ASD, we could use that knowledge to aid in early diagnosis and intervention. Designated biomarkers that aid early diagnosis of autism could also provide insight about anticipated behaviors and challenges for individual children. Such knowledge would be valuable in determining which treatments and therapies would be most beneficial for that child.

Establishing biomarkers for autism is no easy task. We know that ASD is a complex, multifaceted disorder, and as such, there will always be a number of factors that are difficult or impossible to address. Isolating ERPs can also prove challenging due to inherent brain differences associated with chronological age and developmental stage. Genetic events, comorbid disorders, and the heterogeneity of ASD further add to the complexity of establishing reliable biomarkers of autism. As a result, it is not uncommon for research to have mixed or contradictory findings. This is certainly true in EEG/ERP research. One of the goals of our EEG journal club is to think critically about existing research in order to improve our own methods and produce reliable findings with clinical applications.


How is EEG applied?

The application range for EEG is extremely wide, ranging from clinical applications (e.g., diagnosis of neurodegenerative brain diseases [1]) to engineering projects (e.g., Brain-Computer-Interfaces [2]), academic human behavior research (e.g., cognitive psychology [3, 4]), to commercial human behavior research (e.g. neuromarketing [5]).

Each of these research areas use EEG to trace electrical signals or from the brain, driven by the firing of neurons.


Watch the video: EEG - Die Elektroenzephalografie (August 2022).