Electrodermal activity

Checked 2026-07-23 against NeuroKit2 0.2.13 stable runtime/source, the official EDA API/examples, and Society for Psychophysiological Research guidance.

Measurement contract

Record:

conductance versus resistance, physical unit, range, and calibration;
constant-voltage/current system and electrode material/area;
palmar/plantar or other site, laterality, placement, and skin preparation;
sampling rate, hardware filters, temperature, humidity, acclimation, and movement;
missing/detached/saturated intervals; and
participant/task factors and response definition.

Do not infer microsiemens from EDA or compare arbitrary sensor units with published µS thresholds. Sensor site, hardware, environment, and population require validation.

Default stable pipeline

signals, info = nk.eda_process(
    eda,
    sampling_rate=100,
    method="neurokit",
)

In stable 0.2.13 the NeuroKit pipeline performs cleaning, high-pass tonic/phasic decomposition, and NeuroKit SCR detection. It does not use cvxEDA by default.

The pinned default schema observed:

EDA_Raw, EDA_Clean, EDA_Tonic, EDA_Phasic,
SCR_Onsets, SCR_Peaks, SCR_Height, SCR_Amplitude,
SCR_RiseTime, SCR_Recovery, SCR_RecoveryTime

info was a flat dict containing SCR arrays plus sampling_rate. Treat this as a default 0.2.13 observation, not a universal schema.

There is no public eda_quality() in stable 0.2.13. Quality must combine acquisition metadata, missing/flat/clipped/motion checks, raw/clean overlays, decomposition plausibility, and response review.

Make decomposition explicit

clean = nk.eda_clean(eda, sampling_rate=100, method="neurokit")
components = nk.eda_phasic(
    clean,
    sampling_rate=100,
    method="highpass",
)

eda_clean() options include neurokit, biosppy, and none. The NeuroKit path uses a 3 Hz low-pass, and skips it below 7 Hz.

eda_phasic() returns a DataFrame with EDA_Tonic and EDA_Phasic. Methods include:

highpass: default stable method; phasic high-pass separation;
smoothmedian: median-smoothed tonic estimate;
cvxeda: convex optimization; needs optional cvxopt;
sparseda: sparse decomposition.

These methods estimate different latent components and are not interchangeable. Report method, all kwargs, optional dependency versions, convergence/failure behavior, and sensitivity. Do not call one decomposition “physiologically true” without appropriate validation.

SCR detection

markers, peak_info = nk.eda_peaks(
    components["EDA_Phasic"],
    sampling_rate=100,
    method="neurokit",
    amplitude_min=0.1,
)

Stable methods include neurokit, gamboa2008, kim2004, vanhalem2020, and nabian2018. For neurokit and kim2004, amplitude_min is a fraction relative to the largest amplitude in the analyzed signal—not an absolute µS threshold.

eda_peaks() returns (signals, info):

marker/feature DataFrame: SCR_Onsets, SCR_Peaks, SCR_Height, SCR_Amplitude, SCR_RiseTime, SCR_Recovery, SCR_RecoveryTime;
info dict: event-indexed arrays and sampling rate.

Marker columns are same-length arrays; feature values are placed at relevant marker locations and are otherwise missing. Use info for event-level arrays. Do not average same-length feature columns as if every sample were an independent response.

eda_fixpeaks() is documented as a placeholder that does not currently correct EDA peaks.

Missingness and artifacts

EDA motion/electrode artifacts can resemble fast responses, while detachment can look flat. Before decomposition:

inspect raw units, range, clipping, steps, flatlines, and missing runs;
segment long discontinuities;
annotate motion, temperature changes, and contact problems;
avoid broad interpolation across SCR morphology; and
keep an artifact/validity mask through epoching.

Filtering cannot restore a detached or saturated channel. A low response count can be physiological, methodological, or a sensor failure; it is not automatically a “non-responder.”

Create epochs only after event and signal clocks are aligned:

epochs = nk.epochs_create(
    signals,
    events,
    sampling_rate=100,
    epochs_start=-1,
    epochs_end=10,
    baseline_correction=False,
)
features = nk.eda_eventrelated(epochs)

Stable event-related output is conditional on available columns. Documented features include EDA_SCR, first-response amplitude/time/rise/recovery fields, tonic/phasic summaries, labels, conditions, and event onset. Inspect features.columns.

Prespecify response latency/window, overlap handling, baseline approach, minimum amplitude definition, non-response coding, and trial artifact rules. Slow responses can overlap adjacent events; a peak in a window is not automatically elicited by that event.

Interval analysis and sympathetic index

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

The pinned official example showed six columns, including SCR count/amplitude, EDA_Tonic_SD, EDA_Sympathetic, EDA_SympatheticN, and EDA_Autocorrelation; output depends on duration and available columns.

eda_sympathetic() supports posada and ghiasi, with a default 0.045–0.25 Hz band. The implementation/documentation uses at least 64 seconds to support the spectral estimate. Report exact usable duration, frequency band, estimator, normalization, and units. Do not turn this index into a direct clinical sympathetic-state measure.

Bounded pipeline

python skills/neurokit2/scripts/eda_pipeline.py \
  --input deidentified.csv --column EDA --root . --deidentified \
  --sampling-rate 100 --unit uS \
  --clean-method neurokit --phasic-method highpass \
  --peak-method neurokit --amplitude-min 0.1

The helper rejects missing/non-finite samples, records the observed schema, and makes decomposition/threshold semantics explicit.

Interpretation boundary

EDA indexes eccrine sweat-gland activity under the recording conditions. It does not uniquely identify stress, emotion, deception, pain, diagnosis, or intent. Compare within a theory-driven design with contextual measures and validated preprocessing. Do not use this workflow for clinical/driver/workplace monitoring or medical-device validation.