Imaging Human Brain Functions > EEG > Flashcards

EEG Flashcards

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1
Q

What causes synaptic transmission in neurons?

A

Action Potentials in afferent fibers

Neurons communicate by sending electrical signals (action potentials) along nerve fibers, leading to synaptic activity (which causes tiny voltage changes at the connections between neurons)

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2
Q

What is a local field potential (LFP) and how is it created?

A

The sum of local potential changes across many neurons

LFP is created when many parallel pyramidal cells fire synchronously.

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3
Q

How do scalp electrodes measure electrical activity?

A

They measure electrical potential changes from the local field potential (LFP)

The larger the active synchronously firing cell population, the higher the amplitude of the EEG.

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4
Q

What effect does skull and cortical tissue have on EEG signals?

A

Induces blurring of the signal from the LFP (signal dampening)

This can affect the clarity and accuracy of the recorded signals.

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5
Q

Why can’t signals from cells in deep brain strucgtures be picked up well?

A

They orient themselves in different directions which impedes their sumation (so harder to get an LFP which can be measured by scalp electrodes)

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6
Q

Where do low frequency (slow) oscillations come from?

As opposed to high frequency (fast) ones? ASK WHY

A

Large neural populations, as opposed to smaller, more local neural populations

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7
Q

What is the 10-20 system in electrode positioning and why is it called that

A

A method ensuring electrodes are positioned at equal distances from four landmarks on the skull

They are usually positioned at the 10% and 20% distance from the landmarks

Landmarks include the nasion, inion, and left and right preauricular points.

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8
Q

Where is the naison

A

Where the forehead meets the bridge of the nose

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9
Q

Where is the inon

A

The bump at the back of the skull

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10
Q

Where are the left and right preauricular points

A

In front of each ear

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11
Q

If researchers are after higher resolution than the 10-20 system, what can they do?

A

They can place more electrodes at closer distances, for example the 10-10 and the 10-5 systems

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12
Q

What are the characteristics of Alpha waves?

A

8–13 Hz; relaxed wakefulness, cognitive inactivity

Alpha rhythms from sensorimotor areas are known as mu rhythms.

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13
Q

Time-locked vs phase-locked

A

time: The brain response always happens at roughly the same time after a stimulus across trials.

phase: The response happens not only at the same time but also at the same phase of the oscillation cycle (e.g., always at the peak of the wave).

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14
Q
A
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15
Q

What do the letters in electrode naming conventions represent?

And name all of them

A

Brain area

F = frontal, P = parietal, O = occipital, T = temporal, C = central

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16
Q

What do the numbers in the electrode naming conventions represent?

A

The side of the brain they are on (odd= left, even= right, z = middle)

Numbers increase as electrodes move farther from the midline

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17
Q

What is the difference between abrasive gel and hydrogel electrodes?

A

Abrasive gel is used for passive electrodes (no preAmp); hydrogel is used for active electrodes (preAmp with higher robustness)

Hydrogel has a preamplifier, increasing signal robustness.

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18
Q

What is the advantages and disadvantages of using abrasive gel and hydrogel

A

Advantages: They have the highest P300 spelling accuracy
Disadvantages: They are inconvenient, participants must wash hair after use. For abrasive gel, topmost layer of dead skin cells must be removed.

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19
Q

What are advantages and disadvantages of water based electrodes

These are soaked in water/saline to connect skin with electrode

A

Advantages: faster to set up, no need to wash hair, lowest short circuit noise, less time consuming
Disadvantages: new method, not as widely used so some research difficulties

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20
Q

What are advantages and disadvantages of dry electrodes

These use pins to connect to skin, using its natural moisture

A

Advantages: comfort, no need for conductive substance
Disadvantages: high sensitivity to movement artifacts

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21
Q

What are artifacts in EEG signals?

A

Unwanted electrical signals that do not come from brain activity, making data hard to interpret

Examples include muscle movements, eye movements, and electromagnetic noise.

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22
Q

Examples of biological artifacts

A

muscle movements, eye movements, sweating

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23
Q

Examples of techical artifacts

A

electromagnetic noise from power lines, electric lights or other fields

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24
Q

How do we counteract artifacts?

A

Can do visual/manual or automatic artifact detection/removal including electromyograms for biological artifacts (which measure muscle activity) or electrooculargrams (which measure eye movement activity)

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25
What is spontaneous EEG?
Measurable permanent activity in the brain that is always ongoing due to internal processes and mental tasks. Oscillations are non-phase locked. ## Footnote It is not triggered by a specific event and is primarily rhythmic oscillations with frequencies between 1 and 40 hz
26
What frequency range is associated with Delta waves and what are they associated with?
<4 Hz ## Footnote Delta waves are associated with deep sleep and unconscious states (including pathologic ones).
27
What frequency range is associated with Theta waves and what are they associated with?
4-8 hz ## Footnote Associated with drowsiness, light sleep and meditation.
28
What frequency range is associated with Alpha waves and what are they associated with?
8–13 Hz | relaxed wakefulness, cognitive inactivity (idling) ## Footnote Alpha rhythms from sensorimotor areas are known as mu rhythms; large amplitudes indicate resting sensorimotor areas
29
What frequency range is associated with Beta waves and what are they associated with?
13-30 hz ## Footnote altertness, active concentration, task engagement, excitement, anxiety, attention, vigilance.
30
What frequency is associated with Gamma waves and what are they associated with
30-100+ Hz; over 100 we generally cannot measure ## Footnote arousal and perceptual binding mechanisms (eg. integration of various aspects of a stimulus into a coherent overall perception)
31
General rule about frequency of oscillations and business of brain
Generally, the higher the frequency of oscillations, the busier the brain
32
What does a higher amplitude in EEG indicate?
Stronger, more synchronized neural activity ## Footnote It also depends on electrode placement (closer to source = higher amplitude) and neuron orientation.
33
What is the difference between phase-locked and non-phase-locked activity?
Phase-locked activity occurs at the same point in the wave cycle; the timing of non-phase-locked activity varies between trials ## Footnote This distinction is crucial in understanding ERPs (phase-locked) versus spontaneous oscillations (non-phase locked).
34
Why does averaging work for ERPs but not spontaneous EEGs?
ERPs are phase locked so averaging across trials enhances the consistent brain response while reducing random noise, leading to a better signal-noise ration on the other hand, spontaneous EEGs are not phase-locked so when trying to average, the signals cancel out and it is not possible to extract meaningful data.
35
What are Event-related Potentials (ERPs)?
Time and phase-locked voltage changes recorded in response to specific stimuli or events ## Footnote On their own single ERPs are not sufficiently strong and are hard to distinguish from spontaneous EEG recordings. However they can be enhanced by averaging responses from repeated stimuli presentations. This makes the consistent ERP signal stronger relative to noise
36
How do ERP component labels work
Named by polarity (P or N) + typical latency (in ms). P1 (P100): positive deflection at ~100 ms after stimulus. N100 (N1): negative deflection at ~100 ms. P300: positive deflection around 300 ms. N400: negative deflection around 400 ms. | These are waveform features in the EEG signal
37
What does the N100 component reflect?
Early sensory processing (attention) ## Footnote It functionally overlaps with early components like P1.
38
What does the P300 component represent?
Stimulus evaluation, decision-making, attention allocation ## Footnote Subcomponents include P3a and P3b.
39
What does the N400 component represent?
Reflects semantic processing | It shows larger amplitude for semantically incongruent stimuli. ## Footnote Important for language, autism, dyslexia, schizophrenia research
40
What Error potentials (ErrP)?
They indicate errors in actions Measured by frontal midline electrodes above ACC (known for conflict monitoring) peaks 100ms after an error | types: response, feedback, observation, interaction ## Footnote ErrPs can be detected in specific time windows and are useful in user-driven scenarios.
41
How do ErrPs relate to/improve BCIs?
Support adaptive learning in BCIs; allowing systems to learn from errors and evaluation of actions.
42
Distinguish the types of ErrPs
Response, feedback, observation, interaction
43
What does the Mismatch Negativity (MMN) reflect?
Automatic response to unexpected auditory stimuli Occurs when brain detects change in input compared to predictions formed from past input Reflects a failure to predict incoming signals, leading to prediction error. | formally the N2a subcomponent of the N200
44
What are the two components of the MNN
Early component (100-140ms): - linked to basic sensory processing - originates in temporal cortex - driven by sensory features, not prediction comp Later component (140-200ms): - linked to prediction-comparison mechanisms - involves the prefrontal cortex - evaluates input against predictions
45
What is power spectrum analysis used for and what are its applications?
Analyzing energy distribution across EEG frequencies. EEG rhythms (delta, alpha, etc.) are defined by their frequency bands, revealed through power spectrum analysis. Power spectrum analysis helps us identify spontaneous oscillations and distinguish phase-locked vs non phase-locked activity. ## Footnote It has applications in sleep staging, seizure detection, and emotional state changes. In BCI research, it is used to track frequency-specific changes in neural activity.
46
Advantages and disadvantages of power spectrum analysis
Advantages: It is simple, widely used and interpretable. Disadvantages: assumes stationarity, can miss transient dynamics
47
What is the Fourier Transform
Mathematical operation that decomposes EEG signal from the time domain into its frequency/oscillatory components (type and strength of frequency)
48
The frequency of an EEG signal depends on ___
the type of neural activity
49
The power of a specific EEG signal depends on
the strength of the neural activiy
50
What does the discrete Fourier transform (DFT) do?
analyses EEG signal by dividing power over specific frequency components in the EEG signals resulting in discrete frequeency signals that correlate to the activity of certain neuron groups that share similar function/firing behavior ## Footnote Because the formula has a period nature the algorithm can repeat many calculations over the whole signal
51
What does the Fast Fourier Transform (FFT) method do?
The FFT is a computer algorithm that is used to compute the DFT (a mathematical operation) more efficiently. Analyzes EEG signals by dividing power over specific frequency components ## Footnote It is fast and accurate but sensitive to noise.
52
Advantages and Disadvantages of the FFT method
Advantages: it is fast, accurate, with high frequency resolution Disadvantages: easily affected by EEG signal noise, spectra leak into each other and difficulty differentiating between different frequency boundaries
53
What is one solution for disadvantages of FFT
Average period method: it splits original signal into N non overlapping, consecutive segments which are then averaged
54
What is Welch's method and how does it compare to FFT
A refinement of FT which splits data into overlapping, windowed segments. Advantages over FFT: it produces smoother, more robust spectra Disadvantages under FFT: lower frequency resolution, requires more data
55
What is Autoregressive model
Does not rely on Fourier decomposition. Instead, it builds a statistical model of the signal: Current value = weighted sum of past values + noise. (incorporates estimation error) From that model, you derive the power spectrum indirectly ## Footnote Needs a lot of stable data
56
What is the difference between power spectrum analysis and time frequency analysis?
Power spectrum analysis estimates the distribution of energy across frequency bands, assuming the EEG is stationary. In contrast, time–frequency analysis captures how these frequency components change over time, making it more suitable for task-related or dynamic EEG signals
57
What is Short-Time Fourier Transform (STFT)
A modification of the Fourier Transform for signals that change over time (a method of time-frequency analysis) Instead of analyzing the whole EEG signal at once, STFT: - Splits the signal into short, fixed-length time windows. - Applies a Fourier Transform to each window separately. - Produces a time–frequency representation (a spectrogram) with frequency power on the y-axis, time on the x-axis.
58
Disadvantage of STFT
Resolution is limited by fixed window size
59
What is Wavelet transform
Another method of time-frequnecy analysis Uses short “wiggly” functions (wavelets) that can stretch or compress which are convolved with the EEG signal to see how well they match at different scales (frequencies) and times. ## Footnote Low-frequency (slow) rhythms → use longer wavelets → better frequency precision, poorer timing precision. High-frequency (fast) rhythms → use shorter wavelets → better timing precision, poorer frequency precision.
60
What is Empirical Mode Decomposition (EMD)
Another method of time-frequency analysis Adaptive ,data-driven method that breaks signal into intrinsic mode functions (IMFs) (simpler components)
61
Advantages and disadvantages of EMD
Advantages: works well for nonlinear and non-stationary signals Disadvantages: Can produce artifacts if IMFs overalp or mix timescales
62
What is Wigner-Ville Distribution method and what are its advantages/disadvantages
Classical method for non-stationary signals Advantages: strong at avoiding spectral leakage Disadvantages: sensitive to noise, can be harder to interpret
63
What are intrinsic mode functions
A signal, like EEG, decomposed into simpler components
64
What does time frequency power measure
Changes in amplitude over time within specific frequency bands
65
What does phase synchrony measure?
Consistency of the wave peaks and troughs of two oscillatory phases between trials or brain regions (functional connectivity) over time ## Footnote Synchronization of phases is thought to support functional connectivity, and stronger synchrony suggests more efficient information transfer between neural populations
66
What is cross-frequency coupling (CFC)?
It describes the interaction of neural oscillations across different frequency bands (eg. theta and gamma). Osciallations cannot work in isolation; their coordinated activity supports functional connectivity and efficient comunication.
67
Types of coupling
phase-amplitude, power-power, phase-phase, phase-frequency
68
phase-amplitude coupling
phase of a slower oscillation (like theta) modulating the amplitude of a faster oscillation (eg. gamma)
69
power-power coupling
power of 2 different frequency bands fluctuating in a correlated manner, reflecting coordinated increases and decreases in neural population activity across frequencies ## Footnote suggesting synchronization of brain regions operating at distinct rhythms to optimize communication
70
phase-phase coupling
phases of two frequencies stay in a stable relationship, sllowing different brain areas to synchronise their timing ## Footnote this is essential for information transfer and integration across brain areas
71
phase-frequency coupling
phase of slow oscillation modulates instantaneous frequency of fast oscillation, allowing fleible adjustment of neural timing and enhancing communication efficiency
72
What is cross-frequency analysis?
a connectivity-based method which includes cross-frequency coupling and fross-frequency directionality
73
What are the benefits of EEG?
High temporal resolution, noninvasive, easy to apply, widely used in research and clinics, can record during movement (within limits)
74
What is coupling in the context of brain frequencies?
Phases of two frequencies stay in a stable relationship, allowing different brain areas to synchronise their timing.
75
What does phase-to-frequency coupling refer to?
The phase of a slow oscillation modulates the instantaneous frequency of a fast oscillation.
76
What is one benefit of EEG
High temporal resolution (millisecond scale).
77
What is a noninvasive method that can record brain activity during movement?
Coupling methods (unlike fMRI).
78
List three applications of EEG in clinical and research settings.
* Attention * Memory * Language
79
What are limitations of EEG as a method?
Low spatial resolution, low signal-noise ratio, requires expert integration or large high-quality data sets for machine learning, and each analysis method has its own weaknesses
80
What is a challenge faced when integrating coupling with other imaging modalities?
Integration with multimodal imaging (fMRI, MEG, etc.) is still limited.
81
True or False: EEG has a high signal-to-noise ratio.
False. It has a low one, which is one of its limitations.
82
Fill in the blank: EEG requires expert interpretation or large high-quality datasets for _______.
machine learning.
83
What is one weakness of FFT in analysis methods?
FFT is noise-sensitive.
84
What does Granger causality miss in its analysis?
Nonlinear effects.
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