EEG summary

Question 1

Event-Related Potentials (ERPs)

Answer 1

ERPs are voltage changes in the EEG that are time-locked to specific events or stimuli. They represent the brain’s averaged electrical response to a particular type of stimulus.

Question 2

Key steps in ERP analysis

Answer 2

A. Preprocessing
B. ERP formation
C. ERP analysis methods

Question 3

Preprocessing

Answer 3

1. Re-referencing
2. Filtering
3. Artifact Handling

Question 4

Re-referencing

Answer 4

A technique in EEG analysis that establishes a neutral reference point for voltage measurements. Common methods include mastoid reference, average reference, and nose reference.

Question 5

Re-referencing impact

Answer 5

Choice of reference can affect ERP morphology but not topography

Question 6

Filtering

Answer 6

The process of removing unwanted frequencies from the EEG signal. High-pass filters eliminate slow drifts, low-pass filters remove muscle artifacts, and notch filters suppress power line noise (50/60 Hz).

Question 7

Filtering - important considerations

Answer 7

• Filter settings can distort ERP components
• Different filter settings can produce different ERP morphologies
• Always report filter settings in publications

Question 8

Artifact Handling

Answer 8

• Ocular artifact correction
• Artifact rejection

Question 9

Ocular Artifact Correction

Answer 9

ICA (Independent Component Analysis)
- Separates EEG into independent components
Identifies and removes eye movement components
- Reconstructs clean EEG
Regression-based methods

Question 10

Artifact rejection

Answer 10

• Automatic: Based on amplitude thresholds
• Manual: Visual inspection
• Criteria should be consistent across conditions

Question 11

ERP formation

Answer 11

1. Segmentation
2. Baseline correction
3. Averaging

Question 12

Segmentation

Answer 12

The process of cutting continuous EEG data into time-locked epochs around a stimulus, such as -100 to +1000 ms. Each condition should have unique trigger codes

Question 13

Baseline correction

Answer 13

A technique that subtracts the mean pre-stimulus activity (e.g., -100 to 0 ms) to normalize EEG epochs. Ensures all epochs start at approximately 0 μV

Question 14

Averaging

Answer 14

• Average all epochs per condition
• Improves signal-to-noise ratio
• Number of trials needed depends on component size and noise level

Question 15

ERP Analysis Methods

Answer 15

1. Peak analysis
2. Mean amplitude analysis

Question 16

Peak analysis

Answer 16

A method that measures the maximum or minimum voltage within a time window to analyze ERP components.

Question 17

Peak analysis - advantages

Answer 17

• Simple to implement
• Widely used and understood

Question 18

Peak analysis - disadvantages

Answer 18

• Sensitive to noise
• May miss sustained effects
• Assumes clear peak exists

Question 19

Mean Amplitude analysis

Answer 19

A method that calculates the average voltage within a time window. It is more robust to noise than peak analysis.

Question 20

Mean amplitude analysis - advantages

Answer 20

• More robust to noise
• Better for sustained effects
• No assumption about peak presence

Question 21

Mean amplitude analysis - disadvantages

Answer 21

• May miss brief effects
• Window selection can be arbitrary

Question 22

Frequency analysis

Answer 22

A. Fourier transform methods
B. Common frequency bands
C. Specialized Measures

Question 23

Basic fourier transform

Answer 23

• Decomposes signal into constituent frequencies
• Shows power at different frequencies
• Loses temporal information

Question 24

Time-frequency analysis

Answer 24

Methods like wavelet analysis and Short-Time Fourier Transform (STFT) that provide both frequency and temporal information about EEG signals. Shows how frequency content changes over time

Question 25

Common frequency bands

Answer 25

EEG signals are categorized into frequency bands such as - Delta (0.5-4 Hz): Sleep, deep relaxation - Theta (4-8 Hz): Memory, emotional processing - Alpha (8-13 Hz): Relaxed wakefulness, inhibition - Beta (13-30 Hz): Active thinking, focus - Gamma (>30 Hz): Feature binding, high-level processing

Question 26

C. Specialized measures

Answer 26

1. Event-Related Synchronization/ Desynchronization (ERS/ERD) 2. 2. Frontal Alpha Asymmetry (FAA)

Question 27

Event-Related Synchronization/ Desynchronization (ERS/ERD)

Answer 27

Measures changes in power relative to baseline, used for studying cognitive and motor processes.

Question 28

Frontal Alpha Asymmetry (FAA)

Answer 28

A measure comparing alpha power between left and right frontal regions, used in emotion, depression, and personality research.

Question 29

Multivariate Pattern Analysis (MVPA) - Basic principles

Answer 29

- Uses machine learning to identify patterns in neural activity - Considers multiple channels/timepoints simultaneously - Can detect subtle differences between conditions

Question 30

Multivariate Pattern Analysis (MVPA) - Common approaches

Answer 30

1. Classification - Train classifier to distinguish between conditions - Cross-validation to test generalization - Measure classification accuracy 2. Representational Similarity Analysis (RSA) - Compare patterns of neural activity - Create similarity matrices - Link neural patterns to behavioral or theoretical models

Question 31

Multivariate Pattern Analysis (MVPA) - Advantages

Answer 31

- Can detect distributed patterns - More sensitive than univariate methods - Links directly to information content

Question 32

Advanced analysis methods

Answer 32

A. Source localization B. Connectivity analysis

Question 33

Source localization

Answer 33

Techniques like LORETA and BESA estimate the neural sources of scalp EEG activity but require accurate head models due to the inverse problem.

Question 34

Connectivity analysis

Answer 34

1. Coherence - Measures frequency-specific synchronization - Shows functional connectivity between regions 2. Phase Synchrony - Measures phase relationships between signals - Independent of amplitude 3. Granger Causality - Assesses directional influences between signals - Based on predictability

Question 35

Best practices for method selection

Answer 35

1. Research question considerations 2. Data Quality Requirements 3. Validation strategies 4. Reporting standards

Question 36

Research question considerations

Answer 36

- Temporal precision needed - Spatial information required - Type of neural process (evoked vs. induced) - Expected effect size

Question 37

Data quality requirements

Answer 37

- Number of trials needed - Signal-to-noise ratio - Artifact levels - Recording parameters

Question 38

Validation strategies

Answer 38

- Multiple analysis approaches - Control analyses - Statistical validation - Cross-validation where appropriate

Question 39

Reporting standards

Answer 39

- Detailed methods description - Parameter specifications - Preprocessing steps - Statistical approaches