EegFun.jl

A Julia package for EEG/ERP data analysis and visualization. Currently under active development.

Documentation

View the full documentation →

The documentation includes:

• Installation instructions and getting started guide
• Tutorials and Demos
• Complete API reference

Installation

You can install EegFun.jl through the standard Julia package manager.

Standard Installation

From the Julia REPL, enter Pkg mode by pressing ] and run:

add EegFun

Or using Pkg in the code:

using Pkg
Pkg.add("EegFun")

Development Version (vía GitHub)

To install the latest development version directly from GitHub:

using Pkg
Pkg.add(url="https://github.com/igmmgi/EegFun.jl")

Features

• EEG/ERP Analysis
• EEG/ERP Interactive Plots (via Makie.jl)
• Time-Frequency Analysis
• Raw data to full ERP batch preprocessing pipelines

Showcase

Data Browser

Show Code

using EegFun

# raw data file and channel coordinates
dat = EegFun.read_raw_data("my_raw_file.bdf");
layout_file = EegFun.read_layout("my_layout.csv");
EegFun.polar_to_cartesian_xy!(layout_file)

# Julia EegFun type
dat = EegFun.create_eegfun_data(dat, layout_file);

EegFun.plot_databrowser(dat);

ICA Data Browser

Show Code

using EegFun

# raw data file and channel coordinates
dat = EegFun.read_raw_data("my_raw_file.bdf");

layout_file = EegFun.read_layout("my_layout.csv");
EegFun.polar_to_cartesian_xy!(layout_file)

dat = EegFun.create_eegfun_data(dat, layout_file);

# rereference data and apply 1Hz high-pass filter for ICA
EegFun.rereference!(dat, :avg)
EegFun.highpass_filter!(dat, 1)

# calculate EOG channels
EegFun.channel_difference!(
    dat,
    channel_selection1 = EegFun.channels([:Fp1, :Fp2]),
    channel_selection2 = EegFun.channels([:IO1, :IO2]),
    channel_out = :vEOG,
); # vertical EOG = mean(Fp1, Fp2) - mean(IO1, I02)
EegFun.channel_difference!(
    dat,
    channel_selection1 = EegFun.channels([:F9]),
    channel_selection2 = EegFun.channels([:F10]),
    channel_out = :hEOG,
); # horizontal EOG = F9 - F10

# detect some extreme values
EegFun.is_extreme_value!(dat, 200);

# ICA on continuous data without extreme values
ica_result = EegFun.run_ica(dat; sample_selection = EegFun.samples_not(:is_extreme_value_200)) 

EegFun.plot_ica_component_activation(dat, ica_result)

Plot Gallery

Epochs (Grid Layout)	ERP (Topo Layout)
ICA Component Topographies	ICA Component Spectra
ERP Measurement GUI	ERP Filter GUI
Time-Frequency Analysis	Triggers
Plot GUI	Artifact Detection

More Plot Examples

Artifact Detection

ERP Image (Topo Layout)

Epoch Plots (Grid Layout)

ERP (Topo Layout)