Package 'PhysioEMG'

Title: EMG Analysis Functions for PhysioExperiment Objects
Description: Provides electromyography (EMG) analysis functions for PhysioExperiment objects. Includes envelope extraction (RMS, Hilbert), muscle activation onset detection (Hodges-Bui, Teager-Kaiser), fatigue analysis (median frequency shift), and muscle synergy decomposition (NMF, PCA, ICA).
Authors: Yusuke Matsui
Maintainer: Yusuke Matsui <[email protected]>
License: MIT + file LICENSE
Version: 0.2.0
Built: 2026-05-16 05:28:24 UTC
Source: https://github.com/x-biosignal/PhysioEMG

Help Index

Normalize EMG Amplitude

Description

Normalizes EMG amplitude data using MVC or peak normalization. MVC normalization divides each channel by its maximum voluntary contraction value, yielding percentage-of-MVC units. Peak normalization divides by the within-trial peak so each channel ranges from 0 to 1.

Usage

emgAmplitudeNormalize(
  x,
  method = c("mvc", "peak"),
  mvc_data = NULL,
  assay_name = NULL,
  output_assay = "normalized"
)

Arguments

x

A PhysioExperiment object.
method

Normalization method: "mvc" (maximum voluntary contraction) or "peak" (peak of the trial).
mvc_data

A PhysioExperiment containing MVC trial data (required for "mvc" method). Must have the same number of channels.
assay_name

Assay to normalize (default: first assay).
output_assay

Output assay name (default: "normalized").

Value

A PhysioExperiment object with an additional assay named output_assay containing normalized amplitude values. For "peak" normalization, values range from 0 to 1. For "mvc" normalization, values represent proportion of maximum voluntary contraction.

References

De Luca, C.J. (1997). "The use of surface electromyography in biomechanics." Journal of Applied Biomechanics, 13(2), 135-163. doi:10.1123/jab.13.2.135

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

emgEnvelope() for computing amplitude envelopes prior to normalization, emgFatigue() for fatigue analysis, emgOnsetDetect() for muscle activation onset detection

EMG Coherence Network Analysis

Description

Builds a static muscle coordination network from pairwise magnitude-squared coherence between EMG channels.

Usage

emgCoherenceNetwork(
  x,
  freq_band = NULL,
  channels = NULL,
  nperseg = 256L,
  noverlap = NULL,
  assay_name = NULL,
  aggregate = c("mean", "max", "median"),
  threshold = NULL
)

Arguments

x

A PhysioExperiment object.
freq_band

Optional numeric vector c(low, high) in Hz. If NULL, uses all frequencies.
channels

Integer vector of channel indices to include. If NULL, uses all.
nperseg

Segment length for Welch estimation (default: 256).
noverlap

Overlap length (default: floor(nperseg / 2)).
assay_name

Input assay name. If NULL, uses default assay.
aggregate

Aggregation across frequency bins: "mean", "max", or "median".
threshold

Optional threshold for binary adjacency matrix.

Value

A list with:

network

Numeric matrix (channel x channel) of coherence strength.

adjacency

Logical matrix after thresholding, or NULL.

coherence

3D array (freq x channel x channel).

frequencies

Frequency vector (Hz) corresponding to coherence.

channel_names

Channel labels used in the network.

See Also

emgDynamicWaveletNetwork() for time-varying networks, emgInterpretNetworkKG() for annotation-aware interpretation.

EMG Coordination Structure Summary from Network Topology

Description

Quantifies higher-order muscle coordination structure from a weighted network matrix, including module structure, efficiency, and node roles.

Usage

emgCoordinationStructure(
  network,
  threshold = NULL,
  n_modules = NULL,
  max_modules = 6L,
  directed = FALSE,
  symmetrize = c("mean", "max", "min"),
  normalize = TRUE
)

Arguments

network

Numeric square matrix (channels x channels) or a list containing $network.
threshold

Optional edge-weight threshold. Values below threshold are set to zero before topology estimation.
n_modules

Optional number of modules. If NULL, chooses a value automatically by maximizing weighted modularity over candidates.
max_modules

Maximum number of candidate modules for automatic search.
directed

Logical; set TRUE if network is directed/asymmetric.
symmetrize

Method to convert directed matrices to undirected form: "mean", "max", or "min".
normalize

Logical; if TRUE, rescales weights to [0, 1].

Value

A list with:

network

Processed undirected weighted network matrix.

node_metrics

Data.frame of node-level topology features.

modules

Named integer vector of module assignments.

summary

Global network topology summary.

See Also

emgCoherenceNetwork(), emgDynamicWaveletNetwork()

Directed EMG Network via Pairwise Granger Causality

Description

Estimates a directed muscle coordination network using pairwise Granger causality in the time domain.

Usage

emgDirectedGCNetwork(
  x,
  channels = NULL,
  assay_name = NULL,
  max_lag = 10L,
  score = c("f_stat", "delta_r2"),
  threshold = NULL,
  p_value_cutoff = NULL,
  standardize = TRUE
)

Arguments

x

A PhysioExperiment object.
channels

Integer vector of channel indices to include. If NULL, uses all.
assay_name

Input assay name. If NULL, uses default assay.
max_lag

Lag order (in samples) for autoregressive modeling.
score

Directed edge metric: "f_stat" or "delta_r2".
threshold

Optional threshold for adjacency based on selected score.
p_value_cutoff

Optional p-value threshold for adjacency.
standardize

Logical; if TRUE, z-score each channel before GC.

Value

A list with:

network

Directed numeric matrix (source x target).

p_values

Directed matrix of GC p-values.

adjacency

Logical directed matrix, or NULL.

channel_names

Channel labels used in the network.

lag

Lag order used for modeling.

Dynamic Wavelet Coherence Network for EMG

Description

Builds a time-varying coordination network by computing wavelet coherence between channel pairs and aggregating coherence within sliding windows.

Usage

emgDynamicWaveletNetwork(
  x,
  frequencies = seq(5, 120, by = 5),
  freq_band = NULL,
  channels = NULL,
  window_sec = 0.5,
  step_sec = 0.1,
  n_cycles = 7,
  smoothing_cycles = 3,
  assay_name = NULL,
  aggregate = c("mean", "max", "median"),
  threshold = NULL,
  respect_coi = TRUE
)

Arguments

x

A PhysioExperiment object.
frequencies

Numeric vector of wavelet center frequencies (Hz).
freq_band

Optional numeric vector c(low, high) for aggregation.
channels

Integer vector of channel indices to include. If NULL, uses all.
window_sec

Sliding window length in seconds.
step_sec

Sliding window step in seconds.
n_cycles

Number of Morlet cycles (default: 7).
smoothing_cycles

Smoothing width in cycles (default: 3).
assay_name

Input assay name. If NULL, uses default assay.
aggregate

Aggregation across time-frequency bins: "mean", "max", or "median".
threshold

Optional threshold for binary adjacency network per window.
respect_coi

Logical; if TRUE, masks frequencies below COI before aggregation.

Value

A list with:

network

3D array (window x channel x channel).

adjacency

Logical 3D array thresholded from network, or NULL.

window_times

Window center times in seconds.

static_summary

Mean network across windows.

frequencies

Frequency vector used for wavelet transform.

coi

Cone-of-influence frequency at each time sample.

See Also

emgCoherenceNetwork() for static spectral network, emgInterpretNetworkKG() for annotation-aware interpretation.

EMG Envelope Extraction

Description

Extracts the amplitude envelope from EMG signals using various methods. The RMS method computes root mean square over a sliding window. The Hilbert method uses the analytic signal via the Hilbert transform. The lowpass method rectifies the signal and applies a moving-average lowpass filter.

Usage

emgEnvelope(
  x,
  method = c("rms", "hilbert", "lowpass"),
  window_ms = 50,
  cutoff = 6,
  assay_name = NULL,
  output_assay = "envelope"
)

Arguments

x

A PhysioExperiment object with EMG data.
method

Envelope method: "rms" (root mean square), "hilbert" (Hilbert transform), or "lowpass" (rectification + lowpass filter).
window_ms

Window size in milliseconds for RMS method (default: 50).
cutoff

Cutoff frequency in Hz for lowpass method (default: 6).
assay_name

Input assay name (default: first assay).
output_assay

Output assay name (default: "envelope").

Value

A PhysioExperiment object with an additional assay named output_assay containing the amplitude envelope. The envelope matrix has the same dimensions as the input (time x channels) with non-negative values representing instantaneous signal amplitude.

References

De Luca, C.J. (1997). "The use of surface electromyography in biomechanics." Journal of Applied Biomechanics, 13(2), 135-163. doi:10.1123/jab.13.2.135

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

emgAmplitudeNormalize() for normalizing envelope values, emgOnsetDetect() for onset detection from envelope data, emgFatigue() for fatigue analysis using spectral features

EMG Fatigue Analysis

Description

Tracks median and mean frequency over time to assess muscle fatigue. Decreasing median frequency indicates fatigue due to reduced motor unit conduction velocity. The signal is divided into overlapping windows and the power spectral density is computed via FFT for each window.

Usage

emgFatigue(x, window_sec = 1, overlap = 0.5, assay_name = NULL)

Arguments

x

A PhysioExperiment object with EMG data.
window_sec

Analysis window in seconds (default: 1.0).
overlap

Overlap fraction between windows (default: 0.5).
assay_name

Input assay name (default: first assay).

Value

A data.frame with one row per channel per window, containing columns:

channel

Integer channel index.

window

Integer window number (1-indexed).

time_sec

Start time of the window in seconds.

median_freq

Median frequency (Hz) at which 50 percent of the spectral power is below.

mean_freq

Power-weighted mean frequency (Hz).

rms_amplitude

Root mean square amplitude of the window.

References

De Luca, C.J. (1984). "Myoelectrical manifestations of localized muscular fatigue in humans." Critical Reviews in Biomedical Engineering, 11(4), 251-279.

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

emgFatigueIndex() for a summary fatigue metric, emgSpectralMoments() for spectral moment analysis, emgEnvelope() for amplitude envelope extraction

EMG Fatigue Index

Description

Computes a fatigue index as the ratio of final to initial median frequency. Values less than 1 indicate fatigue (frequency decrease). The signal is internally analyzed with emgFatigue() using 0.5-second windows at 50 percent overlap, then the initial and final portions are compared.

Usage

emgFatigueIndex(x, initial_pct = 0.2, final_pct = 0.2, assay_name = NULL)

Arguments

x

A PhysioExperiment object with EMG data.
initial_pct

Percentage of signal used for initial estimate (default: 0.2).
final_pct

Percentage of signal used for final estimate (default: 0.2).
assay_name

Input assay name (default: first assay).

Value

A data.frame with one row per channel, containing columns:

channel

Integer channel index.

fatigue_index

Ratio of final to initial median frequency. Values less than 1 indicate fatigue.

initial_mdf

Mean median frequency (Hz) in the initial portion.

final_mdf

Mean median frequency (Hz) in the final portion.

References

De Luca, C.J. (1984). "Myoelectrical manifestations of localized muscular fatigue in humans." Critical Reviews in Biomedical Engineering, 11(4), 251-279.

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

emgFatigue() for detailed windowed fatigue tracking, emgSpectralMoments() for spectral moment analysis, emgEnvelope() for amplitude envelope extraction

Interpret EMG Network with Knowledge-Graph Metadata

Description

Adds metadata/KG context to high-weight network edges. This function does not require a specific backend and works with user-provided tables exported from PhysioAnnotationHub/physioKG workflows.

Usage

emgInterpretNetworkKG(
  network,
  node_metadata = NULL,
  kg_edges = NULL,
  threshold = NULL,
  top_n = 20L,
  window = NULL
)

Arguments

network

A square matrix (channels x channels) or 3D array (window x channels x channels).
node_metadata

Optional data.frame containing at least channel. Additional columns (e.g. kg_node, muscle_name, muscle_group) are carried into the edge table.
kg_edges

Optional data.frame of KG links. Expected columns are node_a, node_b, and optional relation.
threshold

Optional edge threshold. Default is 75th percentile of upper-triangle weights.
top_n

Maximum number of edges returned after thresholding.
window

Optional window index when network is a 3D array. If NULL, uses the mean across windows.

Value

A list with:

edge_table

Ranked edge table with optional metadata/KG annotations.

threshold

Applied threshold value.

network_matrix

Matrix used for interpretation.

summary

List of summary statistics.

kg_relation_summary

Relation counts from matched KG links, or NULL.

Detect EMG Muscle Activation Onset and Offset

Description

Identifies when muscle activation begins and ends using threshold-based or energy-based methods. The Hodges-Bui method thresholds the rectified signal at a multiple of baseline standard deviations. The Teager-Kaiser method applies the Teager-Kaiser energy operator before thresholding, which can be more sensitive to sudden onsets.

Usage

emgOnsetDetect(
  x,
  method = c("hodges_bui", "teager_kaiser"),
  threshold_sd = 3,
  baseline_sec = 0.2,
  min_duration_ms = 50,
  assay_name = NULL
)

Arguments

x

A PhysioExperiment object with EMG data.
method

Detection method: "hodges_bui" (baseline SD threshold) or "teager_kaiser" (Teager-Kaiser energy operator).
threshold_sd

Number of baseline SDs above mean for threshold (default: 3).
baseline_sec

Duration of baseline period in seconds from signal start (default: 0.2).
min_duration_ms

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

offsets

A data.frame with columns channel, sample, and time_sec for each activation offset. Rows correspond to matching onsets.

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

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

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

EMG Partial Coherence Network Analysis

Description

Builds a static network from pairwise partial coherence, estimated from the inverse cross-spectral density matrix at each frequency.

Usage

emgPartialCoherenceNetwork(
  x,
  freq_band = NULL,
  channels = NULL,
  nperseg = 256L,
  noverlap = NULL,
  assay_name = NULL,
  aggregate = c("mean", "max", "median"),
  threshold = NULL,
  ridge = 1e-06
)

Arguments

x

A PhysioExperiment object.
freq_band

Optional numeric vector c(low, high) in Hz.
channels

Integer vector of channel indices to include. If NULL, uses all.
nperseg

Segment length for Welch estimation (default: 256).
noverlap

Overlap length (default: floor(nperseg / 2)).
assay_name

Input assay name. If NULL, uses default assay.
aggregate

Aggregation across frequency bins: "mean", "max", or "median".
threshold

Optional threshold for binary adjacency matrix.
ridge

Ridge regularization added to spectral matrix inversion.

Value

A list with:

network

Numeric matrix (channel x channel) of partial coherence.

adjacency

Logical matrix after thresholding, or NULL.

partial_coherence

3D array (freq x channel x channel).

frequencies

Frequency vector (Hz).

channel_names

Channel labels used in the network.

See Also

emgCoherenceNetwork(), emgWPLINetwork()

EMG Spectral Moments

Description

Computes spectral moments (M0, M1, M2) over sliding windows. M0 is total power, M1 is the first spectral moment (related to mean frequency), and M2 is the second moment (related to bandwidth). These can be combined to derive the mean frequency (M1/M0) and spectral bandwidth.

Usage

emgSpectralMoments(x, window_sec = 1, overlap = 0.5, assay_name = NULL)

Arguments

x

A PhysioExperiment object with EMG data.
window_sec

Analysis window in seconds (default: 1.0).
overlap

Overlap fraction (default: 0.5).
assay_name

Input assay name (default: first assay).

Value

A data.frame with one row per channel per window, containing columns:

channel

Integer channel index.

window

Integer window number (1-indexed).

m0

Zeroth spectral moment (total power).

m1

First spectral moment (frequency-weighted power).

m2

Second spectral moment (frequency-squared-weighted power).

References

De Luca, C.J. (1984). "Myoelectrical manifestations of localized muscular fatigue in humans." Critical Reviews in Biomedical Engineering, 11(4), 251-279.

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

emgFatigue() for median and mean frequency tracking, emgFatigueIndex() for summary fatigue metric, emgEnvelope() for time-domain amplitude analysis

EMG Weighted Phase-Lag Index (wPLI) Network

Description

Builds a static network from pairwise weighted phase-lag index (wPLI), which is less sensitive to zero-lag coupling artifacts.

Usage

emgWPLINetwork(
  x,
  freq_band = NULL,
  channels = NULL,
  nperseg = 256L,
  noverlap = NULL,
  assay_name = NULL,
  aggregate = c("mean", "max", "median"),
  threshold = NULL,
  debiased = FALSE
)

Arguments

x

A PhysioExperiment object.
freq_band

Optional numeric vector c(low, high) in Hz.
channels

Integer vector of channel indices to include. If NULL, uses all.
nperseg

Segment length for Welch estimation (default: 256).
noverlap

Overlap length (default: floor(nperseg / 2)).
assay_name

Input assay name. If NULL, uses default assay.
aggregate

Aggregation across frequency bins: "mean", "max", or "median".
threshold

Optional threshold for binary adjacency matrix.
debiased

Logical; if TRUE, uses debiased wPLI estimator.

Value

A list with:

network

Numeric matrix (channel x channel) of wPLI values.

adjacency

Logical matrix after thresholding, or NULL.

wpli

3D array (freq x channel x channel).

frequencies

Frequency vector (Hz).

channel_names

Channel labels used in the network.

See Also

emgCoherenceNetwork(), emgPartialCoherenceNetwork()

Create a Basic EMG PhysioExperiment

Description

Generates a synthetic multi-channel EMG PhysioExperiment object with simulated muscle activity. Each channel contains baseline noise with a burst of higher-amplitude activity in the middle 40 percent of the signal (from 30 to 70 percent), mimicking a typical voluntary contraction.

Usage

make_emg(n_time = 2000, n_channels = 4, sr = 1000)

Arguments

n_time

Number of time points (default: 2000).
n_channels

Number of EMG channels (default: 4).
sr

Sampling rate in Hz (default: 1000).

Value

A PhysioExperiment object with a single "raw" assay containing simulated EMG data (time x channels matrix), channel metadata in colData, and the specified sampling rate.

References

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

make_emg_contraction() for EMG with a defined contraction window, make_emg_fatigue() for EMG with fatigue progression, emgEnvelope() for extracting amplitude envelopes

Examples

pe <- make_emg()
pe
dim(SummarizedExperiment::assay(pe, "raw"))

Create EMG PhysioExperiment with Known Contraction

Description

Generates a synthetic single-channel EMG PhysioExperiment with a clearly defined contraction period. The contraction region has substantially higher amplitude than the baseline, making it suitable for testing onset detection algorithms.

Usage

make_emg_contraction(
  n_time = 5000,
  sr = 1000,
  contraction_start = 0.3,
  contraction_end = 0.7,
  baseline_sd = 0.01,
  contraction_sd = 0.5
)

Arguments

n_time

Number of time points (default: 5000).
sr

Sampling rate in Hz (default: 1000).
contraction_start

Proportion of signal where contraction begins (default: 0.3).
contraction_end

Proportion of signal where contraction ends (default: 0.7).
baseline_sd

Standard deviation of baseline noise (default: 0.01).
contraction_sd

Standard deviation of contraction activity (default: 0.5).

Value

A PhysioExperiment object with a single "raw" assay containing a one-channel EMG signal with a known contraction window. The contraction region is defined by contraction_start and contraction_end as proportions of total signal length.

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

See Also

emgOnsetDetect() for detecting the contraction onset, emgEnvelope() for extracting the amplitude envelope, make_emg() for basic multi-channel EMG, make_emg_fatigue() for EMG with fatigue progression

Examples

pe <- make_emg_contraction()
pe
onset <- emgOnsetDetect(pe)
onset$onsets

Create EMG PhysioExperiment with Fatigue Progression

Description

Generates a synthetic single-channel EMG PhysioExperiment that simulates a fatiguing isometric contraction. The median frequency of the signal decreases progressively over time (from 80 Hz to approximately 44 Hz across 10 segments), mimicking the spectral compression characteristic of muscle fatigue.

Usage

make_emg_fatigue(n_time = 10000, sr = 1000)

Arguments

n_time

Number of time points (default: 10000).
sr

Sampling rate in Hz (default: 1000).

Value

A PhysioExperiment object with a single "raw" assay containing a one-channel EMG signal exhibiting progressive median frequency decrease across 10 equal-length segments.

References

De Luca, C.J. (1997). "The use of surface electromyography in biomechanics." Journal of Applied Biomechanics, 13(2), 135-163. doi:10.1123/jab.13.2.135

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

emgFatigue() for tracking median frequency over time, emgFatigueIndex() for computing a summary fatigue metric, emgSpectralMoments() for spectral moment analysis, make_emg() for basic multi-channel EMG

Examples

pe <- make_emg_fatigue()
pe
fatigue <- emgFatigue(pe)
head(fatigue)

Muscle Synergy Decomposition

Description

Decomposes multi-channel EMG into muscle synergies using matrix factorization.

Usage

muscleSynergy(
  x,
  n_synergies,
  method = c("nmf", "pca", "ica"),
  max_iter = 200L,
  tol = 1e-04,
  assay_name = NULL
)

Arguments

x

A PhysioExperiment object with multi-channel EMG.
n_synergies

Number of synergies to extract.
method

Decomposition method: "nmf" (non-negative matrix factorization), "pca" (principal component analysis), or "ica" (independent component analysis).
max_iter

Maximum iterations for NMF (default: 200).
tol

Convergence tolerance for NMF (default: 1e-4).
assay_name

Input assay name (default: first assay).

Value

A list with:

  • W: Synergy weight matrix (n_synergies x channels)

  • H: Activation pattern matrix (time x n_synergies)

  • vaf: Variance accounted for (0-1)

  • method: Method used

  • original_data: Original data matrix for reconstruction

References

De Luca, C.J. (1997). "The use of surface electromyography in biomechanics." Journal of Applied Biomechanics, 13(2), 135-163. doi:10.1123/jab.13.2.135

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

synergyReconstruct() for reconstructing data from synergies, synergyCompare() for comparing synergy solutions, emgEnvelope() for amplitude envelope extraction

Compare Two Synergy Results

Description

Computes pairwise correlation between synergy weight vectors from two decompositions. Uses best-match pairing.

Usage

synergyCompare(result1, result2)

Arguments

result1

First result from muscleSynergy.
result2

Second result from muscleSynergy.

Value

A data.frame with columns: synergy1, synergy2, correlation.

References

De Luca, C.J. (1997). "The use of surface electromyography in biomechanics." Journal of Applied Biomechanics, 13(2), 135-163. doi:10.1123/jab.13.2.135

Merletti, R. & Parker, P.A. (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." Wiley-IEEE Press. doi:10.1002/0471678384

See Also

muscleSynergy() for computing synergy decompositions, synergyReconstruct() for reconstructing data from synergies

Reconstruct Data from Synergies

Description

Reconstructs EMG data using a subset of synergies.

Usage

synergyReconstruct(synergy_result, n_synergies)

Arguments

synergy_result

Result from muscleSynergy.
n_synergies

Number of synergies to use for reconstruction.

Value

A list with:

  • reconstructed: Reconstructed data matrix

  • vaf: VAF of the reconstruction

References

De Luca, C.J. (1997). "The use of surface electromyography in biomechanics." Journal of Applied Biomechanics, 13(2), 135-163. doi:10.1123/jab.13.2.135

See Also

muscleSynergy() for computing the initial decomposition, synergyCompare() for comparing synergy solutions