Utilisateur
electroencephalography
high temporal resolution, low spatial resolution (because it is affected by the layers of the head)
EEG and averaged ERPs measure electrical potentials generated in the extracellular fluid as ions flow across the cell
membranes and neurons talk to one another via neurotransmitters.
In other words, it measures postsynaptic potentials
Forward: computing the EEG signals on the scalp given a known source configuration; predicting the scalp potentials (EEG data) from known brain activity.
easy
Inverse: estimating the brain sources (location and strength) from the measured EEG signals on the scalp; inferring where in the brain the EEG signals originate.
hard
The period of brain activity related to the preparation for performing an action.
- polarity
- timing
- scalp distribution
- sensitivity to task manipulation
- nomenclatures (pre, peri, post)
p/n depending on where in the visual field the stimulus is
50-70
sensory processing
90-100
sensory/ perceptual preprocessing
170-200
called N170 for faces
perceptual processing, expert recognition, visual discrimination
225-250
object recognition and categorization
deployment of covert attention
300
stimulus evaluation, encoding and maintainance
ERN; event-related negativity (negative)
Pe; error positivity (positive, but after negative response)
preperation of motor response
- Standardize electrode placement, especially reference placement
- Make sure that research has not been conducted before
- multiple comparison correction
- Make sure there is no baseline bias + baseline correction
- sufficient number of trials (or participants)
- remove artifacts (eye movement, muscle movement, noise)
- Keep in mind that different stimuli inherently create different ERPs
- Look at the entire ERP, not just the peak
The number of times per second the analog signal is converted to digital data. A higher sampling rate captures more data points. (normaly 512 Herz)
- active
- reference
- ground
- importing
where raw digital data, which represents the voltage measured over time from each electrode relative to a reference, is loaded into the analysis software or environment to begin the preprocessing sequence
- filtering
Remove unwanted frequency bands.
High-pass filters remove low drifts (attributed to sweat or amplifier issues)
Low-pass filters remove high frequencies (often associated with muscle noise)
- segmentation (epoching)
Dividing the continuous EEG recording into segments time-locked to specific events (like stimulus onset or response)
- baseline removal
Subtracting the average voltage during a baseline from the entire waveform, to remove anticipation activity.
- artifact handling
done with ICA
- bad connection (electrode pop, skin impedance, sweat)
- external electrical noise
- amplifier saturation/ drift
- ocular artifacts
- muscle artifacts
- time-domain (ERP)
- time-frequency
- frequency
- ERP refractoriness, the time it takes for the ERP to "reset"
- Offset transient, where sudden stimulus offsets elicit their own ERP components (long or short intervals)
- individual differences, e.g., due to unique patterns of brain folding
- gamma
- beta
- alpha
- theta
- delta
a negative ERP response when something unexpected occurs
Gamma - 60-30
Beta - 12-30
Alpha - 8 -12
Theta - 8-4
Delta - 4-1.5
BioSemi
From inion to nasion