Electrodermal Activity (EDA) Analysis

Overview

Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR) or Skin Conductance (SC), measures the electrical conductance of the skin, reflecting sympathetic nervous system arousal and sweat gland activity. EDA is widely used in psychophysiology, affective computing, and lie detection.

Main Processing Pipeline

eda_process()

Automated processing of raw EDA signals returning tonic/phasic decomposition and SCR features.

signals, info = nk.eda_process(eda_signal, sampling_rate=100, method='neurokit')

Pipeline steps:

1. Signal cleaning (low-pass filtering)
2. Tonic-phasic decomposition
3. Skin conductance response (SCR) detection
4. SCR feature extraction (onset, peak, amplitude, rise/recovery times)

Returns:

• signals: DataFrame with:
  • EDA_Clean: Filtered signal
  • EDA_Tonic: Slow-varying baseline
  • EDA_Phasic: Fast-varying responses
  • SCR_Onsets, SCR_Peaks, SCR_Height: Response markers
  • SCR_Amplitude, SCR_RiseTime, SCR_RecoveryTime: Response features
• info: Dictionary with processing parameters

Methods:

• 'neurokit': cvxEDA decomposition + neurokit peak detection
• 'biosppy': Median smoothing + biosppy approach

Preprocessing Functions

eda_clean()

Remove noise through low-pass filtering.

cleaned_eda = nk.eda_clean(eda_signal, sampling_rate=100, method='neurokit')

Methods:

• 'neurokit': Low-pass Butterworth filter (3 Hz cutoff)
• 'biosppy': Low-pass Butterworth filter (5 Hz cutoff)

Automatic skipping:

• If sampling rate < 7 Hz, cleaning is skipped (already low-pass)

Rationale:

• EDA frequency content typically 0-3 Hz
• Remove high-frequency noise and motion artifacts
• Preserve slow SCRs (typical rise time 1-3 seconds)

eda_phasic()

Decompose EDA into tonic (slow baseline) and phasic (rapid responses) components.

tonic, phasic = nk.eda_phasic(eda_cleaned, sampling_rate=100, method='cvxeda')

Methods:

1. cvxEDA (default, recommended):

tonic, phasic = nk.eda_phasic(eda_cleaned, sampling_rate=100, method='cvxeda')

• Convex optimization approach (Greco et al., 2016)
• Sparse phasic driver model
• Most physiologically accurate
• Computationally intensive but superior decomposition

2. Median smoothing:

tonic, phasic = nk.eda_phasic(eda_cleaned, sampling_rate=100, method='smoothmedian')

• Median filter with configurable window
• Fast, simple
• Less accurate than cvxEDA

3. High-pass filtering (Biopac's Acqknowledge):

tonic, phasic = nk.eda_phasic(eda_cleaned, sampling_rate=100, method='highpass')

• High-pass filter (0.05 Hz) extracts phasic
• Fast computation
• Tonic derived by subtraction

4. SparsEDA:

tonic, phasic = nk.eda_phasic(eda_cleaned, sampling_rate=100, method='sparseda')

• Sparse deconvolution approach
• Alternative optimization method

Returns:

• tonic: Slow-varying skin conductance level (SCL)
• phasic: Fast skin conductance responses (SCRs)

Physiological interpretation:

• Tonic (SCL): Baseline arousal, general activation, hydration
• Phasic (SCR): Event-related responses, orienting, emotional reactions

eda_peaks()

Detect Skin Conductance Responses (SCRs) in phasic component.

peaks, info = nk.eda_peaks(eda_phasic, sampling_rate=100, method='neurokit',
                           amplitude_min=0.1)

Methods:

• 'neurokit': Optimized for reliability, configurable thresholds
• 'gamboa2008': Gamboa's algorithm
• 'kim2004': Kim's approach
• 'vanhalem2020': Van Halem's method
• 'nabian2018': Nabian's algorithm

Key parameters:

• amplitude_min: Minimum SCR amplitude (default: 0.1 µS)
  • Too low: false positives from noise
  • Too high: miss small but valid responses
• rise_time_max: Maximum rise time (default: 2 seconds)
• rise_time_min: Minimum rise time (default: 0.01 seconds)

Returns:

• Dictionary with:
  • SCR_Onsets: Indices where SCR begins
  • SCR_Peaks: Indices of peak amplitude
  • SCR_Height: Peak height above baseline
  • SCR_Amplitude: Onset-to-peak amplitude
  • SCR_RiseTime: Onset-to-peak duration
  • SCR_RecoveryTime: Peak-to-recovery duration (50% decay)

SCR timing conventions:

• Latency: 1-3 seconds after stimulus (typical)
• Rise time: 0.5-3 seconds
• Recovery time: 2-10 seconds (to 50% recovery)
• Minimum amplitude: 0.01-0.05 µS (detection threshold)

eda_fixpeaks()

Correct detected SCR peaks (currently placeholder for EDA).

corrected_peaks = nk.eda_fixpeaks(peaks)

Note: Less critical for EDA than cardiac signals due to slower dynamics.

Analysis Functions

eda_analyze()

Automatically select appropriate analysis type based on data duration.

analysis = nk.eda_analyze(signals, sampling_rate=100)

Mode selection:

• Duration < 10 seconds → eda_eventrelated()
• Duration ≥ 10 seconds → eda_intervalrelated()

Returns:

• DataFrame with EDA metrics appropriate for analysis mode

eda_eventrelated()

Analyze stimulus-locked EDA epochs for event-related responses.

results = nk.eda_eventrelated(epochs)

Computed metrics (per epoch):

• EDA_SCR: Presence of SCR (binary: 0 or 1)
• SCR_Amplitude: Maximum SCR amplitude during epoch
• SCR_Magnitude: Mean phasic activity
• SCR_Peak_Amplitude: Onset-to-peak amplitude
• SCR_RiseTime: Time to peak from onset
• SCR_RecoveryTime: Time to 50% recovery
• SCR_Latency: Delay from stimulus to SCR onset
• EDA_Tonic: Mean tonic level during epoch

Typical parameters:

• Epoch duration: 0-10 seconds post-stimulus
• Baseline: -1 to 0 seconds pre-stimulus
• Expected SCR latency: 1-3 seconds

Use cases:

• Emotional stimulus processing (images, sounds)
• Cognitive load assessment (mental arithmetic)
• Anticipation and prediction error
• Orienting responses

eda_intervalrelated()

Analyze extended EDA recordings for overall arousal and activation patterns.

results = nk.eda_intervalrelated(signals, sampling_rate=100)

Computed metrics:

• SCR_Peaks_N: Number of SCRs detected
• SCR_Peaks_Amplitude_Mean: Average SCR amplitude
• EDA_Tonic_Mean, EDA_Tonic_SD: Tonic level statistics
• EDA_Sympathetic: Sympathetic nervous system index
• EDA_SympatheticN: Normalized sympathetic index
• EDA_Autocorrelation: Temporal structure (lag 4 seconds)
• EDA_Phasic_*: Mean, SD, min, max of phasic component

Recording duration:

• Minimum: 10 seconds
• Recommended: 60+ seconds for stable SCR rate
• Sympathetic index: ≥64 seconds required

Use cases:

• Resting state arousal assessment
• Stress level monitoring
• Baseline sympathetic activity
• Long-term affective state

Specialized Analysis Functions

eda_sympathetic()

Derive sympathetic nervous system activity from frequency band (0.045-0.25 Hz).

sympathetic = nk.eda_sympathetic(signals, sampling_rate=100, method='posada',
                                  show=False)

Methods:

• 'posada': Posada-Quintero method (2016)
  • Spectral power in 0.045-0.25 Hz band
  • Validated against other autonomic measures
• 'ghiasi': Ghiasi method (2018)
  • Alternative frequency-based approach

Requirements:

• Minimum duration: 64 seconds
• Sufficient for frequency resolution in target band

Returns:

• EDA_Sympathetic: Sympathetic index (absolute)
• EDA_SympatheticN: Normalized sympathetic index (0-1)

Interpretation:

• Higher values: increased sympathetic arousal
• Reflects tonic sympathetic activity, not phasic responses
• Complements SCR analysis

Use cases:

• Stress assessment
• Arousal monitoring over time
• Cognitive load measurement
• Complementary to HRV for autonomic balance

eda_autocor()

Compute autocorrelation to assess temporal structure of EDA signal.

autocorr = nk.eda_autocor(eda_phasic, sampling_rate=100, lag=4)

Parameters:

• lag: Time lag in seconds (default: 4 seconds)

Interpretation:

• High autocorrelation: persistent, slowly-varying signal
• Low autocorrelation: rapid, uncorrelated fluctuations
• Reflects temporal regularity of SCRs

Use case:

• Assess signal quality
• Characterize response patterns
• Distinguish sustained vs. transient arousal

eda_changepoints()

Detect abrupt shifts in mean and variance of EDA signal.

changepoints = nk.eda_changepoints(eda_phasic, penalty=10000, show=False)

Method:

• Penalty-based segmentation
• Identifies transitions between states

Parameters:

• penalty: Controls sensitivity (default: 10,000)
  • Higher penalty: fewer, more robust changepoints
  • Lower penalty: more sensitive to small changes

Returns:

• Indices of detected changepoints
• Optional visualization of segments

Use cases:

• Identify state transitions in continuous monitoring
• Segment data by arousal level
• Detect phase changes in experiments
• Automated epoch definition

Visualization

eda_plot()

Create static or interactive visualizations of processed EDA.

nk.eda_plot(signals, info, static=True)

Displays:

• Raw and cleaned EDA signal
• Tonic and phasic components
• Detected SCR onsets, peaks, and recovery
• Sympathetic index time course (if computed)

Interactive mode (static=False):

• Plotly-based interactive exploration
• Zoom, pan, hover for details
• Export to image formats

Simulation and Testing

eda_simulate()

Generate synthetic EDA signals with configurable parameters.

synthetic_eda = nk.eda_simulate(duration=10, sampling_rate=100, scr_number=3,
                                noise=0.01, drift=0.01)

Parameters:

• duration: Signal length in seconds
• sampling_rate: Sampling frequency (Hz)
• scr_number: Number of SCRs to include
• noise: Gaussian noise level
• drift: Slow baseline drift magnitude
• random_state: Seed for reproducibility

Returns:

• Synthetic EDA signal with realistic SCR morphology

Use cases:

• Algorithm testing and validation
• Educational demonstrations
• Method comparison

Practical Considerations

Sampling Rate Recommendations

• Minimum: 10 Hz (adequate for slow SCRs)
• Standard: 20-100 Hz (most commercial systems)
• High-resolution: 1000 Hz (research-grade, oversampled)

Recording Duration

• SCR detection: ≥10 seconds (depends on stimulus)
• Event-related: Typically 10-20 seconds per trial
• Interval-related: ≥60 seconds for stable estimates
• Sympathetic index: ≥64 seconds (frequency resolution)

Electrode Placement

• Standard sites:
  • Palmar: distal/middle phalanges (fingers)
  • Plantar: sole of foot
• High density: Thenar/hypothenar eminence
• Avoid: Hairy skin, low sweat gland density areas
• Bilateral: Left vs. right hand (typically similar)

Signal Quality Issues

Flat signal (no variation):

• Check electrode contact and gel
• Verify proper placement on sweat gland-rich areas
• Allow 5-10 minute adaptation period

Excessive noise:

• Movement artifacts: minimize participant motion
• Electrical interference: check grounding, shielding
• Thermal effects: control room temperature

Baseline drift:

• Normal: slow changes over minutes
• Excessive: electrode polarization, poor contact
• Solution: use eda_phasic() to separate tonic drift

Non-responders:

• ~5-10% of population have minimal EDA
• Genetic/physiological variation
• Not indicative of equipment failure

Best Practices

Preprocessing workflow:

# 1. Clean signal
cleaned = nk.eda_clean(eda_raw, sampling_rate=100, method='neurokit')

# 2. Decompose tonic/phasic
tonic, phasic = nk.eda_phasic(cleaned, sampling_rate=100, method='cvxeda')

# 3. Detect SCRs
signals, info = nk.eda_peaks(phasic, sampling_rate=100, amplitude_min=0.05)

# 4. Analyze
analysis = nk.eda_analyze(signals, sampling_rate=100)

Event-related workflow:

# 1. Process signal
signals, info = nk.eda_process(eda_raw, sampling_rate=100)

# 2. Find events
events = nk.events_find(trigger_channel, threshold=0.5)

# 3. Create epochs (-1 to 10 seconds around stimulus)
epochs = nk.epochs_create(signals, events, sampling_rate=100,
                          epochs_start=-1, epochs_end=10)

# 4. Event-related analysis
results = nk.eda_eventrelated(epochs)

# 5. Statistical analysis
# Compare SCR amplitude across conditions

Clinical and Research Applications

Emotion and affective science:

• Arousal dimension of emotion (not valence)
• Emotional picture viewing
• Music-induced emotion
• Fear conditioning

Cognitive processes:

• Mental workload and effort
• Attention and vigilance
• Decision-making and uncertainty
• Error processing

Clinical populations:

• Anxiety disorders: heightened baseline, exaggerated responses
• PTSD: fear conditioning, extinction deficits
• Autism: atypical arousal patterns
• Psychopathy: reduced fear responses

Applied settings:

• Lie detection (polygraph)
• User experience research
• Driver monitoring
• Stress assessment in real-world settings

Neuroimaging integration:

• fMRI: EDA correlates with amygdala, insula activity
• Concurrent recording during brain imaging
• Autonomic-brain coupling

Interpretation Guidelines

SCR amplitude:

• 0.01-0.05 µS: Small but detectable
• 0.05-0.2 µS: Moderate response
• >0.2 µS: Large response
• Context-dependent: Normalize within-subject

SCR frequency:

• Resting: 1-3 SCRs per minute (typical)
• Stressed: >5 SCRs per minute
• Non-specific SCRs: Spontaneous (no identifiable stimulus)

Tonic SCL:

• Range: 2-20 µS (highly variable across individuals)
• Within-subject changes more interpretable than absolute levels
• Increases: arousal, stress, cognitive load
• Decreases: relaxation, habituation

References

• Boucsein, W. (2012). Electrodermal activity (2nd ed.). Springer Science & Business Media.
• Greco, A., Valenza, G., & Scilingo, E. P. (2016). cvxEDA: A convex optimization approach to electrodermal activity processing. IEEE Transactions on Biomedical Engineering, 63(4), 797-804.
• Posada-Quintero, H. F., Florian, J. P., Orjuela-Cañón, A. D., Aljama-Corrales, T., Charleston-Villalobos, S., & Chon, K. H. (2016). Power spectral density analysis of electrodermal activity for sympathetic function assessment. Annals of biomedical engineering, 44(10), 3124-3135.
• Dawson, M. E., Schell, A. M., & Filion, D. L. (2017). The electrodermal system. In Handbook of psychophysiology (pp. 217-243). Cambridge University Press.