ERPgnostics.inter_heatmap — Methodinter_heatmap(pattern_detection_values::DataFrame)Plot interactive heatmap.
Arguments
pattern_detection_values::DataFrame
DataFrame with columns condition and estimate. Each condition is a value on y-axis.kwargs...
Additional styling behavior.
Return Value: Interactive Figure displaying heatmap.
ERPgnostics.inter_heatmap_image — Methodinter_heatmap_image(pattern_detection_values::DataFrame, events::DataFrame, erps::Array{Float64, 3})Plot heatmap and interactive ERP image.
Arguments
pattern_detection_values::DataFrame
DataFrame with columns condition and estimate. Each condition is a value on y-axis.events::DataFrame
DataFrame with columns of experimental events and rows with trials. Each value is an event value in a trial.erps::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.kwargs...
Additional styling behavior.
Return Value: Interactive Figure displaying topoplot series and interactive ERP image.
ERPgnostics.inter_toposeries — Methodinter_toposeries(tmp; positions = positions_128)
+
+plot_butterfly(times::Vector, plot_data::Union{DataFrame, AbstractMatrix}; kwargs...)
+plot_butterfly!(f::Union{GridPosition, GridLayout, Figure}, plot_data::Union{DataFrame, AbstractMatrix}; kwargs...)Plot interactive topoplot series.
Arguments
f::Union{GridPosition, GridLayout, Figure}
Figure,GridLayout, orGridPositionto draw the plot.data::Union{DataFrame, AbstractMatrix}
Data for the ERP plot visualization.kwargs...
Additional styling behavior.
Often used as:inter_toposeries(df; visual = (; colormap = :romaO)).
Keyword argumets (kwargs)
positions::Array = []
Adds a topoplot as an inset legend to the provided channel positions. Must be the same length asplot_data. To change the colors of the channel lines use thetopoposition_to_colorfunction.
Return Value: Interactive Figure displaying topoplot series.
ERPgnostics.inter_toposeries — Methodinter_toposeries(pattern_detection_values::DataFrame; positions::Vector{Point{2, Float64}} = positions_128)Plot interactive topoplot series.
Arguments
pattern_detection_values::DataFrame
DataFrame with columns condition and estimate. Each condition is resposible for a topoplot. Estimates will be projected across channels.kwargs...
Additional styling behavior.
Keyword arguments (kwargs)
positions::Vector{Point{2, Float64}} = positions_128
Array of topoplot coordinates for channels.
Return Value: Interactive Figure displaying topoplot series.
ERPgnostics.inter_toposeries_image — Methodinter_toposeries_image(pattern_detection_values, events, erps, time; positions::Vector{Point{2, Float64}} = positions_128)Plot interactive topoplot series and interactive ERP image.
Arguments
pattern_detection_values::DataFrame
DataFrame with columns condition and estimate. Each condition is resposible for a topoplot. Estimates will be projected across channels.events::DataFrame
DataFrame with columns of experimental events and rows with trials. Each value is an event value in a trial.erps::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.kwargs...
Additional styling behavior.
Keyword arguments (kwargs)
positions::Vector{Point{2, Float64}} = positions_128
Array of coordinates for channels.
Return Value: Interactive Figure displaying topoplot series and interactive ERP image.
ERPgnostics.inter_toposeries_image — Methodinter_toposeries_image(pattern_detection_values::DataFrame, events::DataFrame, erps::Array{Float64, 3}, timing; positions::Vector{Point{2, Float64}} = positions_128)Plot interactive topoplot series and interactive ERP image.
Arguments
pattern_detection_values::DataFrame
DataFrame with columns condition and estimate. Each condition is resposible for a topoplot. Estimates will be projected across channels.events::DataFrame
DataFrame with columns of experimental events and rows with trials. Each value is an event value in a trial.erps::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.timing::?
Timing of recording.kwargs...
Additional styling behavior.
Keyword arguments (kwargs)
positions::Vector{Point{2, Float64}} = positions_128
Array of topoplot coordinates for channels.
Return Value: Interactive Figure displaying topoplot series and interactive ERP image.
ERPgnostics.mult_chan_pattern_detector_probability — Methodmult_chan_pattern_detector_probability(erp_data, stat_function, events; n_permutations = 10)Plot heatmap and interactive ERP image.
Arguments
pattern_detection_values::DataFrame
DataFrame with columns condition and estimate. Each condition is a value on y-axis.events::DataFrame
DataFrame with columns of experimental events and rows with trials. Each value is an event value in a trial.erps::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.kwargs...
Additional styling behavior.
Return Value: Interactive Figure displaying topoplot series and interactive ERP image.
ERPgnostics.mult_chan_pattern_detector_probability — Methodmult_chan_pattern_detector_probability(erp_data::Array{Float64, 3}, stat_function, events::Data; n_permutations = 10)Plot heatmap and interactive ERP image.
Arguments
pattern_detection_values::Array{Float64, 3}
DataFrame with columns condition and estimate. Each condition is a value on y-axis.events::DataFrame
DataFrame with columns of experimental events and rows with trials. Each value is an event value in a trial.erps::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.kwargs...
Additional styling behavior.
Return Value: Interactive Figure displaying topoplot series and interactive ERP image.
ERPgnostics.mult_chan_pattern_detector_probability — Methodmult_chan_pattern_detector_probability(erp_data::Array{Float64, 3}, stat_function, events::DataFrame; n_permutations = 10)Plot heatmap and interactive ERP image.
Arguments
pattern_detection_values::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.events::DataFrame
DataFrame with columns of experimental events and rows with trials. Each value is an event value in a trial.stat_function::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.kwargs...
Additional styling behavior.
Return Value: Interactive Figure displaying topoplot series and interactive ERP image.
ERPgnostics.mult_chan_pattern_detector_probability — Methodmult_chan_pattern_detector_probability(erp_data::Array{Float64, 3}, stat_function::Function, events::DataFrame; n_permutations = 10)Pattern detector.
Method:
- For each channel permute data n_permutations of times.
- For each permuted data use filter for smearing.
- Use pattern dection function.
- Average all this datasets in one. That how we get random data with no pattern: noerp_data.
- Take the the data where we expect to find a pattern: erp_data. Sort its trials by experimental condition.
- Smear and use pattern detection function.
- Find absolute difference of values between erp_data and noerp_data.
- Do it for each channel and each variable.
Arguments
erp_data::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.stat_function::Function
Function used for pattern detection.
For instance,Images.entropyformImages.jl.events::DataFrame
DataFrame with columns of experimental events and rows with trials. Each value is an event value in a trial.kwargs...
Additional styling behavior.
Keyword arguments (kwargs)
n_permutations::Number = 10
Number fo permutations of the algorithm.
Return Value: DataFrame with pattern detection values. Dimensions: experimental events, trials.
ERPgnostics.mult_chan_pattern_detector_probability_meanfilter — Methodmult_chan_pattern_detector_probability_meanfilter(erp_data::Array{Float64, 3}, stat_function::Function, events::DataFrame; n_permutations = 10)Pattern detector.
Method:
- For each channel permute data n_permutations of times.
- For each permuted data use filter for smearing.
- Use pattern dection function.
- Average all this datasets in one. That how we get random data with no pattern: noerp_data.
- Take the the data where we expect to find a pattern: erp_data. Sort its trials by experimental condition.
- Smear and use pattern detection function.
- Find absolute difference of values between erp_data and noerp_data.
- Do it for each channel and each variable.
Arguments
pattern_detection_values::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.stat_function::Function
Function used for pattern detection.
For instance,Images.entropyformImages.jl.events::DataFrame
DataFrame with columns of experimental events and rows with trials. Each value is an event value in a trial.kwargs...
Additional styling behavior.
Keyword arguments (kwargs)
n_permutations::Number = 10
Number fo permutations of the algorithm.
Return Value: DataFrame with pattern detection values. Dimensions: experimental events, trials.
ERPgnostics.mult_chan_pattern_detector_probability_meanfilter — Methodmult_chan_pattern_detector_probability_meanfilter(erp_data::Array{Float64, 3}, stat_function::Function, events::DataFrame; n_permutations = 10)Pattern detector.
Method:
- For each channel permute data n_permutations of times.
- For each permuted data use filter for smearing.
- Use pattern dection function.
- Average all this datasets in one. That how we get random data with no pattern: noerp_data.
- Take the the data where we expect to find a pattern: erp_data. Sort its trials by experimental condition.
- Smear and use pattern detection function.
- Find absolute difference of values between erp_data and noerp_data.
- Do it for each channel and each variable.
Arguments
erp_data::Array{Float64, 3}
3-dimensional Array of voltages of Event-related potentials. Dimensions: channels, time of recording, trials.stat_function::Function
Function used for pattern detection.
For instance,Images.entropyformImages.jl.events::DataFrame
DataFrame with columns of experimental events and rows with trials. Each value is an event value in a trial.kwargs...
Additional styling behavior.
Keyword arguments (kwargs)
n_permutations::Number = 10
Number fo permutations of the algorithm.
Return Value: DataFrame with pattern detection values. Dimensions: experimental events, trials.
ERPgnostics.simulate_6patterns — Functionsimulate_6patterns(μ = 3.2, σ = 0.5)Simulate 6 ERP patterns in one dataset.
Simulated patterns: Sigmoid, One-sided fan, Two-sided fan, Diverging bar, Hourglass bar, Tilted bar.
Columns in resulting sim_6patterns Data Frame to simulate this patterns: Δlatency, duration, durationB, iscar, continuous, duration_linear.
Arguments
μ::Float = 0.5
Controls mean.σ::Float = 3.2
Controls standart deviation.
Return Value: sim_6patterns::Matrix{Float64} with voltages and sim_evts::DataFrame with events.
ERPgnostics.simulate_alldata — Functionsimulate_alldata(μ = 3.2, σ = 0.5)Simulate 6 ERP patterns in one dataset.
Arguments
evts::DataFrame
tmpmaxlength::DataFrame
tmp
Return Value: basis.
Internally, we use a PlotConfig struct to keep track of common plotting options, so that all functions have a similar API.