W3 - EEG Flashcards

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1
Q

What is EEG

A

Method of detecting neural activity via. electrodes on the scalp. Electrodes pick up small fluctuations of electrical signals from activity of (mostly cortical) neurons

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2
Q

What are the properties of the raw signals picked up by EEG

A
  • Very noisy and might not look like much, but they are systematically related to cognitive processes > Use these signals to learn something about cognition when people perform tasks
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3
Q

What are 2 types of EEG

A

Scalp: Non-Invasive Intra-Cranial: Measure directly at exposed cortex

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4
Q

Who invented EEG and how

A

Hans Berger. Detected first EEG with wife’s scalp in 1924.

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5
Q

What is the Alpha Rhythm

A

Alpha rhythm – When people closed their eyes, - Inconsistent electrical signal varying between 8 - 13 Hz - Used two electrodes (silver wires and later foil), one attached to the front of the head and one to the rear, and recorded the potential/voltage difference.

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6
Q

What are advantages/disadvantages of EEG

A

Cheap Temporal Resolution Poor Spatial Resolution

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7
Q

How is EEG Recorded: What are the 4 tools

A

Electrode Cap > Amplifier > EEG Recording Experimental Stimulation > EEG Recording

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8
Q

What are the channels in EEG

A

10 - 32 - 64 - 128 - 256

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9
Q

How are numbers on the scalp displayed

A

Odd and Even. Each corresponds. F = frontal P = parietal C = central O = occipital T = temporal

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10
Q

Neurophysiology: Where is the origin of EEG Signal

A

Post-synaptic potentials (Voltage when NT binds to post-synaptic membrane) > Causes ion channels to open/close, leading to graded changes in potential across membrane Not Action Potential

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11
Q

What can the post-synaptic potential be considered as

A
  • Understood as a small “dipole” (magnet) - Signals from single cells are not strong enough - Many neurons spatially align > summed potentials add up and create the signals we can record
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12
Q

Many neurons spatially align > summed potentials add up and create the signals we can record: What is this called and where is the origin

A

Pooled activity from groups of similarly oriented neurons mostly comes from large cortical pyramid cells

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13
Q

What is the functional unit of EEG (How many Neurons must be spatially aligned)

A

The functional unit is >10,000 simultaneously activated neurons

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14
Q

What determines the sign of the recorded potentials

A

Orientation of the neurons determines the sign of the recorded potentials Some orientations lead to signals which cannot be recorded

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15
Q

What are the limitations of EEG (Neurophysiology)

A

1.) Biased to Gyri (Sulci harder to detect / masked by gri signals) 2.) Meninges, CSF and skull “smear” the EEG signal > Localisation difficult - INVERSE PROBLEM: one given scalp configuration of signals can have multiple dipole solutions!

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16
Q

What is EEG measured in relation to

A

In relation to a reference electrode - Reference either a neutral point or average of all scalp electrodes

17
Q

Typical amplitude of EEG and Steps to make it clear

A

1.) 10μV to 100μV (Tiny) 2.) Amplified by 1,000/100,000x 3.) Signal is digitalized. Typical sample frequency is 256-1024Hz, but can be >4000Hz 4.) Signal is band-pass filtered to remove the low (<0.5-1Hz) and high frequencies (typically >35-70Hz) because they cannot reflect brain activity.

18
Q

What is the most relevant step in EEG signal analysis

A

Finding all the artefacts that are not brain signals! - sweating - electrical noise (“notch filter”) - eye movements and blinks

19
Q

How does eye movement affect EEG.

A
  • Eye can be regarded as a dipole - Signals originating from the eye will contaminate the signal of interest (And will be much larger)
20
Q

How do we remove eye movement in signal

A
  • Record eye signal by placing electrodes next to and under the eye to capture horizontal and vertical eye movements - Remove by excluding contaminated trials, or mathematical algorithms, such as ICA
21
Q

What is wrong with single EEG-trial studies

A

Far too noisy. Too many variance between sessions from same participants and between participants

22
Q

What is P and N

A

Deflection in amplitude. P = Signal < 0 N = Signal > 0

23
Q

How can ERP be analysized

A

1.) Peak-amplitude (used in 70%)) 2.) Area-under-the-curve (used in 20%) 3.) peak-to-peak (used in 10%) 4.) on-set of component (ambiguous) No clear rule, and results might differ between measures

24
Q

Woodman & Luck (1999): What is the signal they used and what did it index.

A

N2pc (second negativity, posterior contralateral) > index attention Strongest over the posterior cortex contralateral to where the observer is attending

25
Q

Woodman & Luck (1999): Study Aims

A

Visual Search Task: Parallel / Serial Serial Fashion: Attention switch from one hemifield to the other, until the target is found . Parallel: Nothing

26
Q

Woodman & Luck (1999): Study Overview

A

People to attend one hemifield first by manipulating probability specific colour was target (C75 and C25)

27
Q

Woodman & Luck (1999): Study Results

A

Target Absent + C75 v C25: N2pc Target Absent + C75 same C25: No shift Target Present + C75 (Target) v C25: No shift Target Present C75 v C25 (target): N2pc All supported serial search

28
Q

Gehring et al., 1993: Study Aims

A

ERN: Whether a cognitive mechanism for the detection of and compensation for errors.

29
Q

What is the ERN

A

The ERN is a negative deflection of up to 10μV in amplitude observed at central electrodes ~80-100ms after an erroneous response.

30
Q

Gehring et al., 1993: Study Overview and 2 Hypotheses

A

Emphasise accuracy/speed in a Flanker-task H1.) Incongruent displays should lead to more errors H2.) Error detection should only matter in the accuracy condition

31
Q

Gehring et al., 1993: First Hypothesis supported?

A

Yes. ERN on incorrect trial in comparison to correct trials (Recorded with EMG)

32
Q

Gehring et al., 1993: Second Hypothesis supported?

A

Yes. The ERN was strongest when people emphasised accuracy, and weakest for speed

33
Q

Gehring et al., 1993: Further study of ERN compensation for errors

A

Investigated how ERNs of different sizes were related to response parameters, which might in turn be related to correcting/avoiding errors

34
Q

Gehring et al., 1993: Further study of ERN compensation results

A

1.) The greater the ERN, the lower the response force > trying to correct for the error 2.) The greater the ERN, the higher the probability to get it right on next trial > successful learning from errors 3.) The greater the ERN, the slower the response on next trial > successful learning from errors