Detect EMG Muscle Activation Onset and Offset

Identifies when muscle activation begins and ends. The Hodges-Bui method rectifies the signal, smooths it with a centered moving average (smooth_ms, as in the original method's low-pass conditioning), and thresholds the smoothed trace at a multiple of baseline standard deviations. The Teager-Kaiser method applies the Teager-Kaiser energy operator, then the same rectify-and-smooth conditioning, before thresholding. Without smoothing (smooth_ms = 0) the rectified band-limited signal dips below threshold at every carrier zero-crossing, so contiguous suprathreshold runs rarely reach min_duration_ms and genuine bursts go undetected. The Bonato method uses a statistical double-threshold on the (optionally whitened) signal, declaring activation when at least r of m successive samples exceed a chi-square threshold set from a target false-alarm probability. The AGLR method applies Staude & Wolf's approximated generalized-likelihood-ratio change detector for a variance increase and refines the onset to the estimated change point.

Usage

emgOnsetDetect(
  x,
  method = c("hodges_bui", "teager_kaiser", "bonato", "aglr"),
  threshold_sd = 3,
  baseline_sec = 0.2,
  min_duration_ms = 50,
  smooth_ms = 25,
  r = 5L,
  m = 10L,
  whiten = TRUE,
  false_alarm = 0.05,
  assay_name = NULL
)

Arguments

x

A PhysioExperiment object with EMG data.

method

Detection method: "hodges_bui" (baseline SD threshold), "teager_kaiser" (Teager-Kaiser energy operator), "bonato" (Bonato double-threshold) or "aglr" (approximated generalized likelihood ratio).

threshold_sd

Number of baseline SDs above mean for the threshold in the "hodges_bui" and "teager_kaiser" methods (default: 3).

baseline_sec

Duration of the baseline period in seconds from the signal start, used to estimate baseline statistics (default: 0.2).

min_duration_ms

Minimum activation duration in ms to accept (default: 50).

smooth_ms

Width in ms of the centered moving-average window applied to the rectified signal ("hodges_bui") or rectified Teager-Kaiser trace ("teager_kaiser") before baseline estimation and thresholding (default: 25, in the 10–50 ms range examined by Hodges & Bui 1996). Set to 0 to disable smoothing (pre-0.2.1 behavior). Ignored by the "bonato" and "aglr" methods, which operate on unsmoothed samples by design.

r, m

Bonato double-threshold parameters: activation requires at least r of m successive samples above the detection threshold (defaults: 5 of 10). For "aglr", m is the sliding analysis-window length in samples.

whiten

If TRUE (default), whiten the signal with an AR model estimated from the baseline before applying the "bonato"/"aglr" statistics, so the baseline approximates white Gaussian noise.

false_alarm

Target false-alarm probability that sets the detection threshold for the "bonato" (per m-sample window) and "aglr" (per sample) methods (default: 0.05).

assay_name

Input assay name (default: first assay).

Value

A list with two data.frames:

onsets

A data.frame with columns channel (integer channel index), sample (sample index of onset), and time_sec (onset time in seconds). For "bonato"/"aglr" a stat column holds the peak detector statistic of the activation.

offsets

A data.frame with the matching channel, sample, time_sec (and stat) for each offset.

If no activations are detected, both data.frames have zero rows.

References

Hodges, P.W. & Bui, B.H. (1996). "A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography." Electroencephalography and Clinical Neurophysiology, 101(6), 511-519. doi:10.1016/S0921-884X(96)95190-5

Bonato, P., D'Alessio, T. & Knaflitz, M. (1998). "A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait." IEEE Transactions on Biomedical Engineering, 45(3), 287-299. doi:10.1109/10.661154

Staude, G. & Wolf, W. (1999). "Objective motor response onset detection in surface myoelectric signals." Medical Engineering & Physics, 21(6-7), 449-467. doi:10.1016/S1350-4533(99)00067-3

Solnik, S., Rider, P., Steinweg, K., DeVita, P. & Hortobagyi, T. (2010). "Teager-Kaiser energy operator signal conditioning improves EMG onset detection." European Journal of Applied Physiology, 110(3), 489-498. doi:10.1007/s00421-010-1521-8

See also

emgEnvelope() for computing amplitude envelopes, emgAmplitudeNormalize() for amplitude normalization, emgFatigue() for fatigue analysis

Examples

pe <- make_emg_contraction()
emgOnsetDetect(pe, method = "bonato")$onsets
#>   channel sample time_sec     stat
#> 1       1   1495    1.494 51757.47