ContextQMD
Back to Claude Scientific Skills

Electromyography

skills/neurokit2/references/emg.md

2.55.05.6 KB
Original Source

Electromyography

Checked 2026-07-23 against NeuroKit2 0.2.13 stable runtime/source, the official EMG API, and human surface-EMG guidance.

Acquisition contract

Record muscle and task, electrode type/size/orientation/inter-electrode distance, reference, skin preparation, amplifier gain/range, hardware filters, unit, sampling rate, synchronization, posture/contraction, and artifact annotations. Follow a muscle-specific placement protocol such as SENIAM when applicable.

EMG amplitude depends on geometry, subcutaneous tissue, cross-talk, electrode contact, hardware, and contraction history. It is not a direct universal force scale.

Stable processing pipeline

python
signals, info = nk.emg_process(emg, sampling_rate=1000)

Pinned 0.2.13 default columns:

text
EMG_Raw, EMG_Clean, EMG_Amplitude,
EMG_Activity, EMG_Onsets, EMG_Offsets

info contained event-level EMG_Activity, EMG_Onsets, EMG_Offsets, and sampling_rate. This is a default observation, not a universal schema.

Cleaning and amplitude

python
clean = nk.emg_clean(emg, sampling_rate=1000, method="biosppy")
amplitude = nk.emg_amplitude(clean)

Important stable details:

  • emg_clean() currently offers biosppy and none.
  • The BioSPPy path uses a fourth-order 100 Hz high-pass Butterworth filter followed by constant detrending.
  • emg_amplitude() takes only the cleaned vector; it has no sampling_rate argument in 0.2.13.

A 100 Hz high-pass may be unsuitable for some surface-EMG endpoints and impossible at low sampling rates. Do not treat the package default as a universal acquisition standard. Set sampling rate above twice the highest retained frequency with transition band margin; surface EMG is commonly acquired around 1000–2000 Hz, but the study, hardware, muscle, and endpoint determine requirements.

Activation detection

Stable signature:

text
emg_activation(
  emg_amplitude=None, emg_cleaned=None, sampling_rate=1000,
  method="threshold", threshold="default", duration_min="default",
  size=None, threshold_size=None, **kwargs
)

Use the correct input for the method:

  • threshold or mixture: pass emg_amplitude;
  • pelt, biosppy, or silva: pass emg_cleaned.
python
activity_signals, activity_info = nk.emg_activation(
    emg_amplitude=amplitude,
    sampling_rate=1000,
    method="threshold",
    threshold="default",
    duration_min=0.05,
)

Return order is (activity_signals, info). The DataFrame has same-length EMG_Activity, EMG_Onsets, and EMG_Offsets; the dict contains event indices. duration_min is seconds. size semantics are method-specific.

Automatic thresholds are hypotheses, not ground truth. Tune/validate on independent labeled contractions or a prespecified baseline. Report threshold, window, smoothing, minimum duration, and false/missed activation performance.

Missing data and artifacts

Inspect and annotate:

  • clipping/saturation and disconnected/flat channels;
  • motion/cable artifacts and low-frequency transients;
  • powerline interference;
  • ECG contamination, especially trunk/proximal sites;
  • cross-talk from adjacent muscles; and
  • baseline changes from contact/sweat.

Do not interpolate across a burst or activation onset. Segment long gaps, preserve a validity mask, and reject affected trials/windows under prespecified rules. Filtering cannot establish that a deflection came from the target muscle.

Event and interval features

python
epochs = nk.epochs_create(
    signals,
    events,
    sampling_rate=1000,
    epochs_start=-0.1,
    epochs_end=1.0,
    baseline_correction=False,
)
event_features = nk.emg_eventrelated(epochs)
interval_features = nk.emg_intervalrelated(signals)

Documented event-related features include EMG_Activation, EMG_Amplitude_Mean, EMG_Amplitude_Max, EMG_Amplitude_SD, EMG_Amplitude_Max_Time, and EMG_Bursts. Interval analysis returns EMG_Activation_N and EMG_Amplitude_Mean in the pinned official example. Columns are conditional; inspect output.

For startle or rapid onset work, hardware latency, synchronization, filter group delay, rectification/smoothing, and onset-definition error can dominate the result.

Normalization

Raw/envelope amplitude is usually not comparable across participants or sessions. Possible denominators include MVC, reference contraction, or within-participant standardization, but each changes the estimand.

If using MVC:

  • acquire it with a validated, safe protocol;
  • inspect fatigue, pain, effort, clipping, and target/cross-talk;
  • define which statistic and window represent MVC;
  • report repetitions and reliability; and
  • never divide by a near-zero/invalid reference.

Normalization does not make electrode/site differences disappear.

Interpretation boundary

NeuroKit2 EMG can support research on amplitude and detected activity. It does not provide validated motor-unit decomposition, neuromuscular diagnosis, fatigue monitoring, prosthetic control safety, rehabilitation decisions, or sleep scoring. Those require endpoint-specific acquisition, algorithms, standards, and independent validation.

Sources checked 2026-07-23