EEG/MEG Flashcards
Who recorded the first human electroencephalogram?
Hans Berger recorded the first human electroencephalogram (EEG) (recorded in 1924, paper published in 1929)
What are the neural bases of EEG?
= What is being measured?
There are two main types of electrical activity associated with neurons:
* action potentials: discrete voltage spikes that travel from the beginning of the axon at the cell body to the axon terminals, where neurotransmitters are released (1ms) — Electric activity: changes in membrane potentia
* postsynaptic potentials: voltages that arise when the neurotransmitters bind to receptors on the membrane of the postsynaptic cell, causing ion channels to open or close and leading to a graded change in the potential across the cell membrane (tens-nundreds ms) — Chemical activity: synaptic transmission
Also, MEG measures magnetic fields that always surround electical dipoles (pairs of positive and negative electrical charges separated by a small distance)
Footnote 2:
EEG does NOT measure action potentials but postsynaptic potentials (of pyramedial cells)
How do we measure electric activity in the brain?
- Intraparenchimal recordings
- Electrocorticography (EcoG)
- Magnetoencephalography (MEG)
- Electroencephalography (EEG)
What are two main types of electrophysiological monitoring?
Intracranial (Intraparenchimal recordings and EcoG) vs. Extracranial (MEG and EEG)
What methods are less invasive?
Extracranial (MEG and EEG)
Which type of electrophysiological monitoring has better spatial resolution?
Intracranial (Intraparenchimal recordings and EcoG)
What methods are cheaper?
Extracranial (MEG and EEG)
What are the main pros of EEG?
- It has high temporal resolution (milliseconds)
- It directly measures electrical activity of the brain
- It is relatively mobile and non-invasive
- It is relatively cheap
What are the main cons of EEG?
- It has poor spatial resolution
- The signal is weak (small signal to noise ratio)
- It is difficult to measure deep structures
Why does EEG has low spatial resolution?
- The electrical current spreads from the source to the receiver while crossing different media (CSF, skull, scalp)
- The dipolar activities of the different brain regions appear widespread over the scalp and generate a complex overlap in the EEG
- Electrical field spreads as a function of distance
- The activity from neural populations with different orientations cancels out
- 2⁄3 of the cortex lie in fissures and lead to widely distributed topographies on the scalp (showing not radially orientation activation)
- It’s hard to estimate the location of brain activity given the pattern of activity measured on the scalp
two simple reasons:
the blurring effects of the head volume conductor
and poor signal-to-noise ratio
How to collect EEG data?
Electric potential is measured from an array of electrodes placed on the scalp with respect to a reference point
What equipment is needed for EEG?
- cap
- electrodes bundles
- flat electrodes (to record electrooculography)
- gel (to facilitate the conductance)
- amplifier
- trigger box
- computer to record EEG data and software
- computer to run a task
What is 10-20 system?
Ir is an international system used for correct electrodes positioning: 10% or 20% of the total front–back (nasion-vertex-inion axis) or right–left distance of the skull
How many electrodes are used to record EEG?
64 is the most common, can go up to 256
+ Additional electrodes placed on the face to record horizontal (corner of the eye) and vertical (above and below the eye) eye movements
The more electrodes, the more spatial accuracy, but the more time to prepare.
What is impedance in EEG?
Impedance is a measure of the opposition to the flow of current, measured in kΩ. The lower (<10 kΩ) impedance the better. It means that more of the real brain signal is picked up, and less noise from the environment. High impedance in the electrodes can cause a reduction in signal amplitude, resulting in a weaker EEG signal. Lower impedance can be avhieved by cleaning the electrodes and the skin surface beforehand.
What is independent component analysis (ICA) used for?
Independent component analysis (ICA) is used to separate the presumably independent different sources of electrical activity in the brain. This can be useful in EEG for separating different types of artifacts (eye blinks, muscle activity) and for identifying specific neural sources (brain regions or frequency bands)
What are the main stages of cleaning EEG data?
- Preprocessing
- Plotting trials and conditions related to the time axis
- Epoching — cutting a signal around the region of interest
- Removing artifacts
What are the main stages in EEG data preprocessing?
- Filtering
- Referencing
- Interpolating bad channels
What is filtering?
It is the process of removing unwanted or irrelevant frequencies from the EEG signal:
* Low frequency filtering: drifts, sweating
* High frequency filtering: muscle contraction
* Line power filtering: line power interference (activity associated with electrical equipment, computers, 50 Hz in Europe, 60 Hz in the US)
What is referencing?
It is the process of determining change in voltage with respect to this reference (external electrodes, average of all channels, one specific channel)
What is interpolating of bad channels?
It is correcting data from one electrode based on the average of its surroundings
Needed when the recording is poor or missing due to a malfunctioning electrode or other issues.
What are event-related potentials (ERPs)?
(also referred to as evoked potentials/response) Event-related potentials (ERPs) are small changes in the electrical activity of the brain that occur in response to a specific event or stimulus.
They are widely used to study the neural mechanisms of perception, attention, memory, and decision-making, and are particularly useful for studying the temporal dynamics of brain activity, as they provide a high temporal resolution of the neural processes occurring in response to a specific event.
Who recorded first (sensory) ERP?
Hallowell and Pauline Davis recorded first unambiguous sensory ERP from awake humans in 1939.
The researchers were able to see clear ERPs on single trials during periods in which the EEG was quiescent
Who recorded first cognitive ERP?
Grey Wallter and his colleagues reported the first cognitive ERP component (contingent negative variation) in 1964.
On each trial of this study, subjects were presented with a warning signal followed by a target stimulus. In the absence of a task, each of these two stimuli elicited the sort of sensory ERP response that one would expect for these stimuli. However, if subjects were required to press a button upon detecting the target, a large negative voltage (contingent negative variation) was observed during the period between the warning signal and the target.The CNV was not a sensory response, but reflected the subject’s preparation for the upcoming target.
What does CNV reflect?
It reflects the subject’s preparation for the upcoming target in “press the button when detect the target stimulus” condition vs. passive viewing condition.
What are the most commonly studied ERPs?
- Visual P1 — in lateral occipital electrodes, peaking 100 ms post stimulus, reflects visual processing
- N170 — in lateralized posterior electrodes, peaking 170 ms post stimulus, associated with face processing
- P300 (and components) — in frontal electrodes, peaking 300 ms post stimulus, associated with attention and decision-making processes, observed in response to rare or relevant stimuli (target stimulus)
- Mismatching negativity — negative deflection at midline scalp sites, 200 ms post a deviant stimulus.
- Readiness potential — lateralized negative deflection above motor areas, preceding contralateral movement
- Error-related negativity — negative deflection in frontal and central sites, onset after erroneous response
Also mind responses to different types of stimuli: visual, auditory, somatosensory, language-related
What are the features of EEG?
- Frequency
- Amplitude
- Phase
What is frequency?
Frequency is number of oscillations per time unit (measure in Hz)
What is amplitude?
Amplitude is the strength of the pattern in terms of microvolts of electrical energy
What is phase?
Phase Diffrence is the time difference between two corresponding points.
What are the main frequency ranges (aka. brain rhythms)?
- Delta (1-4 Hz = 1-4 cycles per second) — non-REM sleep stages (N3)
- Theta (4-8 Hz): hippocampal theta — learning and memory, midfrontal theta — cognitive control
- Alpha (8-12 Hz, discovered by Berger) — inhibits the cortex not in use (gatekeeping) => mechanisms of attention
- Beta (13-30 Hz) — waking consciousness, active concentration, motor processing
- Gamma (30-70 Hz) — conscious perception, working memory, attention + gamma and theta interaction, can overlap with muscle contraction frequencies — studied more with MEG and intracranial techniques, not with EEG
How are ERPs measured?
ERPs are typically measured by averaging the EEG signals recorded in response to multiple presentations of the same stimulus (across multiple trials).
What is univariate analysis of EEG?
Traditional univariate (activation-based) analysis focuses on analyzing one EEG signal at a time, comparing levels of activation across conditions, usually averaging across recording units. Univariate analysis methods are used to extract information about the amplitude, frequency, and temporal characteristics of the EEG signal.
Univariate analysis methods
* Time-domain analysis (measuring the amplitude, identifying specific components of the EEG signal)
* Frequency-domain analysis (measuring the power or amplitude, identifying specific frequency bands)
* Event-related potential analysis
What is multivariate analysis of EEG?
Multivariate analysis (information-based) refers to the use of statistical methods that can analyze the relationship between multiple EEG signals simultaneously, identifying neural sources of activity, and determining the functional connectivity between different brain regions and investigating how spatially distributed activity patterns contain task-relevant information.
Multivariate analysis methods:
* Principal component analysis (PCA): reduces the dimensionality of the data by identifying the patterns of variation that capture the most variation in the data
* Independent component analysis (ICA): separates the different sources of electrical activity in the brain by identifying and isolating their unique characteristics
* Representational similarity analysis (RSA): uses the patterns of similarity or dissimilarity between neural representations to draw inferences about the neural mechanisms underlying cognitive processes (similar stimuli or cognitive processes should be more similar to each other)
* Time-frequency analysis
* Connectivity analysis (mathematical models to analyze the interactions between different brain regions and the functional connectivity)
