Analysis and Visualization for PhysioExperiment Objects
PhysioAnalysis provides 43 exported functions for spectral analysis, time-frequency decomposition, epoching, functional connectivity, statistical testing, and publication-quality visualization of multi-modal physiological signals. Built on PhysioCore, it operates directly on PhysioExperiment objects and covers the full analysis workflow from raw epochs to statistical inference and topographic visualization.
Installation
You can install PhysioAnalysis from r-universe:
install.packages("PhysioAnalysis",
repos = c("https://x-biosignal.r-universe.dev", "https://cloud.r-project.org"))Or install the development version from GitHub:
# install.packages("remotes")
remotes::install_github("x-biosignal/PhysioAnalysis")Quick Start
library(PhysioAnalysis)
# Create sample EEG data with events
signal_matrix <- matrix(rnorm(2500 * 32), nrow = 2500, ncol = 32)
pe <- PhysioExperiment(
assays = list(raw = signal_matrix),
samplingRate = 250
)
pe <- addEvents(pe, name = "stimulus", onset = c(1.0, 3.0, 5.0, 7.0))
# Epoch around stimulus events (-0.2 to 0.8 s)
pe_epoched <- epochData(pe, event = "stimulus", pre = 0.2, post = 0.8)
# Compute the average ERP
erp <- averageEpochs(pe_epoched)
# Run a cluster permutation test (condition A vs B)
result <- clusterPermutationTest(pe_epoched,
group = "condition", n_permutations = 1000)
# Visualize results
plotERP(erp, channels = c("Fz", "Cz", "Pz"))
plotTopomap(erp, time = 0.3)Features
FFT and Spectral Analysis
Frequency-domain analysis and spectral decomposition:
-
fftSignals()– compute FFT magnitude and phase spectra for all channels -
bandPower()– extract power in standard frequency bands (delta, theta, alpha, beta, gamma) or custom ranges -
hilbertTransform()– analytic signal via Hilbert transform for envelope extraction -
instantaneousAmplitude(),instantaneousPhase()– extract amplitude envelope and instantaneous phase from analytic signals
Time-Frequency Analysis
Multi-resolution time-frequency decomposition:
-
spectrogram()– short-time Fourier transform (STFT) with configurable window and overlap -
waveletTransform()– continuous wavelet transform (Morlet) for time-frequency representations -
plotSpectrogram()– visualize time-frequency power maps with customizable color scales
Epoching
Trial segmentation and averaging:
-
epochData()– segment continuous data into time-locked epochs around events -
averageEpochs()– compute trial-averaged waveforms (e.g., ERPs, ERFs) -
grandAverage()– compute grand averages across subjects or sessions -
epochTimes()– retrieve the time vector for epoched data
Connectivity Analysis
Functional and effective connectivity metrics:
-
Spectral coherence:
coherence(),crossSpectrum()– frequency-domain measures of linear coupling -
Phase synchrony:
plv()(Phase Locking Value),pli()(Phase Lag Index),wPLI()(weighted Phase Lag Index) – volume conduction-robust phase metrics -
Correlation:
correlationMatrix()– pairwise amplitude correlations across channels -
General interface:
connectivityMatrix()– compute any connectivity metric as a channel-by-channel matrix
Statistical Testing
Parametric, non-parametric, and mass-univariate statistical methods:
-
Classical tests:
tTestEpochs(),anovaEpochs()– point-by-point or window-based parametric tests across conditions -
Cluster permutation:
clusterPermutationTest()– non-parametric cluster-based permutation testing for family-wise error control over space and time -
Effect sizes:
effectSize()– Cohen’s d, Hedges’ g, and eta-squared -
Confidence intervals:
bootstrapCI()– bootstrap confidence intervals for any statistic -
Multiple comparisons:
correctPValues()– FDR (Benjamini-Hochberg) and Bonferroni correction -
Temporal analysis:
findSignificantWindows()– identify contiguous time windows with significant effects -
SPM methods:
spmTTest(),spmPairedTTest(),spmAnova()– Statistical Parametric Mapping for continuous signal analysis
Visualization
Publication-quality plots for physiological signal data:
-
Time series:
plotSignal()– single-channel waveform display with event markers -
Multi-channel:
plotMultiChannel()– stacked multi-channel display with vertical offset and scaling -
ERP plots:
plotERP()– event-related potential waveforms with confidence bands and condition overlays -
Power spectra:
plotPSD()– power spectral density plots with frequency band shading -
Topographic maps:
plotTopomap()– scalp topography with channel markers and either Shepard inverse-distance weighting (default) or Perrin spherical-spline interpolation -
Topomap series:
plotTopomapSeries()– temporal evolution of scalp topography at multiple time points -
Spectrogram:
plotSpectrogram()– time-frequency power maps with configurable color scales
Dependencies
- R (>= 4.2)
- PhysioCore – core data structures
- signal – DSP primitives
- methods, SummarizedExperiment, S4Vectors, stats, graphics, grDevices
- Suggests: ggplot2, testthat, knitr, rmarkdown
PhysioExperiment Ecosystem
PhysioAnalysis is part of the PhysioExperiment ecosystem, a suite of R packages for multi-modal physiological signal analysis:
| Package | Description |
|---|---|
| PhysioCore | Core data structures and accessors |
| PhysioIO | File I/O (EDF, HDF5, BIDS, CSV, MAT) |
| PhysioPreprocess | Signal preprocessing and artifact removal |
| PhysioAnalysis | Spectral analysis, epoching, statistics, visualization |
| PhysioMoCap | Motion capture data processing |
| PhysioOpenSim | OpenSim biomechanical modeling integration |
Visit the r-universe page to browse all available packages.
License
MIT License. See LICENSE for details.
Governance & support
Part of the Physio ecosystem. Community and policy documents live in the umbrella repository: