Cheatsheet

Quick reference for common EEG processing tasks in EegFun.jl.

Data Import

I want to...	Function
Load a BioSemi .bdf file	`EegFun.read_raw_data("file.bdf")`
Load a BrainVision file	`EegFun.read_raw_data("file.vhdr")`
Load an EEGLAB file	`EegFun.read_eeglab("file.set")`
Load a FieldTrip .mat file	`EegFun.read_fieldtrip("file.mat")`
Load a channel layout	`EegFun.read_layout("layout.csv")`
Create EegFun data structure	`EegFun.create_eegfun_data(raw, layout)`

Data Persistence (JLD2)

I want to...	Function
Save data to JLD2	`jldsave("file.jld2"; data = dat)`
Load data from JLD2	`load("file.jld2", "data")`
Smart-load EegFun data	`EegFun.read_data("file.jld2")`
Batch load all matching files	`EegFun.read_all_data("dir/pattern")`
Group loaded data by condition	`EegFun.group_by_condition(data)`

Preprocessing

I want to...	Function
Rereference to average	`EegFun.rereference!(dat, :avg)`
Rereference to specific channels	`EegFun.rereference!(dat, [:M1, :M2])`
High-pass filter	`EegFun.highpass_filter!(dat, 0.1)`
Low-pass filter	`EegFun.lowpass_filter!(dat, 30.0)`
Bandpass filter	`EegFun.bandpass_filter!(dat, 0.1, 30.0)`
Resample data	`EegFun.resample!(dat, 256)`
Apply baseline correction	`EegFun.baseline!(dat, (-0.2, 0.0))`
Create channel difference	`EegFun.channel_difference!(dat, channel_selection1=channels([:C3]), channel_selection2=channels([:C4]), channel_out=:laterality)`
Average channels into ROI	`EegFun.channel_average!(dat, channel_selections=[channels([:Fz, :Cz, :Pz])], output_labels=[:midline])`
Delete channels	`EegFun.channel_delete!(dat, channels([:M1, :M2]))`
Mark extreme values	`EegFun.is_extreme_value!(dat, 100)`
View trigger counts	`EegFun.trigger_count(dat)`
Search trigger sequences	`EegFun.search_sequence(dat.data.trigger, [1, 2])`

ICA

I want to...	Function
Run ICA decomposition	`EegFun.run_ica(dat)`
Identify EOG components	`EegFun.identify_eog_components(dat, ica)`
Identify ECG components	`EegFun.identify_ecg_components(dat, ica)`
Identify line noise components	`EegFun.identify_line_noise_components(dat, ica)`
Remove components	`EegFun.remove_ica_components!(dat, ica, component_selection=components([1, 3]))`
Restore components	`EegFun.restore_ica_components!(dat, ica, component_selection=components([1]))`

Epoching

I want to...	Function
Define epoch conditions	`cfg = [EegFun.EpochCondition(name="Cond1", trigger_sequences=[[1]])]`
Extract epochs	`EegFun.extract_epochs(dat, cfg, (-0.2, 1.0))`
Baseline correct epochs	`EegFun.baseline!(epochs, (-0.2, 0.0))`
Detect bad epochs automatically	`EegFun.detect_bad_epochs_automatic(epochs)`
Detect bad epochs interactively	`EegFun.detect_bad_epochs_interactive(epochs)`
Reject marked epochs	`EegFun.reject_epochs(epochs)`
Repair artifacts (interpolation)	`EegFun.repair_artifacts(epochs)`

ERP Operations

I want to...	Function
Average epochs into ERPs	`EegFun.average_epochs(epochs)`
Combine conditions	`EegFun.condition_combine(erps, [[1, 2]])`
Subtract conditions	`EegFun.condition_difference(erps, [(1, 2)])`
Average conditions	`EegFun.condition_average(erps, [[1, 2]])`
Compute grand average	`EegFun.grand_average(all_erps)`
Jackknife average	`EegFun.jackknife_average(all_erps)`
Compute GFP	`EegFun.gfp(erps)`
Realign ERPs	`EegFun.realign(erps, :peak)`
Compute LRP	`EegFun.lrp(erps, channel_selection1=channels([:C3]), channel_selection2=channels([:C4]))`

ERP Measurements & Export

I want to...	Function
Extract mean amplitude	`EegFun.erp_measurements("erps", "mean_amplitude", analysis_interval=(0.3, 0.5))`
Extract peak amplitude	`EegFun.erp_measurements("erps", "max_peak_amplitude", analysis_interval=(0.3, 0.5))`
Extract peak latency	`EegFun.erp_measurements("erps", "max_peak_latency", analysis_interval=(0.3, 0.5))`
Explore measurements in GUI	`EegFun.plot_erp_measurement_gui(erps)`

Time-Frequency Analysis

I want to...	Function
Morlet wavelet decomposition	`EegFun.tf_morlet(epochs, frequencies=4:1:30, n_cycles=3)`
Multitaper decomposition	`EegFun.tf_multitaper(epochs, frequencies=4:1:30)`
STFT decomposition	`EegFun.tf_stft(epochs, frequencies=4:1:30)`
Baseline correct TF data	`EegFun.tf_baseline!(tf, (-0.2, 0.0); method=:db)`
TF channel average	`EegFun.channel_average!(tf, channel_selections=[channels([:Fz, :Cz])], output_labels=[:frontal])`
TF channel difference	`EegFun.channel_difference!(tf, channel_selection1=channels([:C3]), channel_selection2=channels([:C4]))`
TF condition difference	`EegFun.condition_difference(tf_data, [(1, 2)])`
TF condition average	`EegFun.condition_average(tf_data, [[1, 2]])`
TF grand average	`EegFun.grand_average(all_tf)`

Statistics

I want to...	Function
Prepare group data for stats	`EegFun.prepare_stats("erps", :paired, condition_selection=conditions([1, 2]))`
Run analytic t-test	`EegFun.analytic_test(stat_data)`
Run permutation test	`EegFun.permutation_test(stat_data, n_permutations=1000)`
Test decoding against chance	`EegFun.test_against_chance(decoded_list)`
Cluster permutation (decoding)	`EegFun.test_against_chance_cluster(decoded_list)`

Plotting

I want to...	Function
Browse continuous data	`EegFun.plot_databrowser(dat)`
Browse with ICA	`EegFun.plot_databrowser(dat, ica)`
Plot ERPs	`EegFun.plot_erp(erps, channel_selection=channels(:Pz))`
Plot topography	`EegFun.plot_topography(erps, interval_selection=times(0.3, 0.5))`
Plot ERP statistics	`EegFun.plot_erp_stats(result, channel_selection=channels(:Pz))`
Plot TF power	`EegFun.plot_tf(tf, channel_selection=channels(:Cz))`
Plot TF statistics	`EegFun.plot_tf_stats(result, channel_selection=channels(:Cz))`
Plot TF topography stats	`EegFun.plot_topography_stats(result, freq_range=(8.0, 12.0))`
Plot ICA components	`EegFun.plot_topography(ica)`
Plot epochs grid	`EegFun.plot_epochs(epochs)`
Plot ERP image	`EegFun.plot_erp_image(epochs, channel_selection=channels(:Pz))`
Plot GFP	`EegFun.plot_gfp(erps)`
Plot channel layout	`EegFun.plot_layout_2d(layout)`
Plot artifact detection	`EegFun.plot_artifact_detection(epochs)`
Plot frequency spectrum	`EegFun.plot_frequency_spectrum(dat)`
Plot filter response	`EegFun.plot_filter_response(dat)`
Plot decoding results	`EegFun.plot_decoding(decoded)`
Plot RSA results	`EegFun.plot_rsa(rsa_result)`

Selection Helpers

I want to select...	Helper
Specific channels	`EegFun.channels([:Fz, :Cz, :Pz])`
All channels except	`EegFun.channels_not([:M1, :M2])`
Channel range	`EegFun.channels(1:32)`
Specific conditions	`EegFun.conditions([1, 2])`
All conditions except	`EegFun.conditions_not([3])`
Time interval	`EegFun.times((0.3, 0.5))`
Specific participants	`EegFun.participants([1, 2, 3])`
All participants except	`EegFun.participants_not([10])`
Specific samples	`EegFun.samples(:is_extreme_value_100)`
All samples except	`EegFun.samples_not(:is_extreme_value_100)`
Specific components	`EegFun.components([1, 3, 5])`