Plot Databrowser

This demo demonstrates the interactive databrowser for exploring and analyzing EEG data in real-time.

What is the Databrowser?

The databrowser is an interactive visualization tool for navigating through EEG data:

• Continuous data: Scroll through raw recordings dynamically

• Epoch data: Navigate individual trials interactively

• Analysis settings: Select regions, channels, and configurations visually

Key Features

Interactive Navigation:

• Scroll through time: Mouse wheel or drag to pan

• Zoom in/out: Adjust time scale for detailed inspection

• Channel selection: Click channels to select for analysis

• Region selection: Drag to select time regions

• Epoch browsing: Navigate through individual trials

Visual Overlays:

• Trigger markers: See event timing

• Epoch intervals: Visualize extraction boundaries

• Extreme values: Highlight artifact-contaminated samples

• Channel repair: Mark and repair problematic channels

ICA Integration:

plot_databrowser(dat, ica_result)

Analysis Settings

The databrowser can also be used to return analysis settings for further processing:

fig, ax, analysis_settings = plot_databrowser(dat)
dat_new = apply_analysis_settings(dat, analysis_settings)

Extract user-selected:

• Time regions

• Channel subsets

• Sample masks

Keyboard Shortcuts

Key	Action
I	Show help/info
R	Open channel repair menu
C	Clear current selections
Arrow keys	Navigate time intervals

Custom Styling

Customize visual appearance:

plot_databrowser(dat,
    channel_line_width = 2,
    selection_color = (:red, 0.5)
)

Working with Files

Load data directly from saved files:

plot_databrowser("dat.jld2")                   # saved continuous data
plot_databrowser("epochs.jld2", "ica.jld2")    # saved epochs and ica result

Common Workflows

1. Initial Quality Control:

• Load raw data

• Scroll through recording

• Identify noisy channels

• Mark extreme values

• Plan preprocessing strategy

2. Artifact Identification:

• Add is_extreme_value!() column

• Visualize artifact-contaminated regions

• Select clean segments for ICA

3. Epoch Validation:

• Mark epoch intervals with mark_epoch_intervals!()

• Verify trigger timing

• Check epoch boundaries

Use Cases

Interactive channel repair:

• Select bad channels visually

• Choose repair method (spherical spline, neighbors)

• Apply and verify results immediately

Spectral analysis region selection:

• Identify interesting time intervals

• Select for further time-frequency analysis

• Export settings for batch processing

Preprocessing validation:

• Verify filtering effects

• Check rereferencing results

• Validate artifact rejection

Workflow Summary

This demo shows:

1. Basic databrowser: Simple visualization

2. Analysis settings: Capture and apply user selections

3. Additional overlays: Extreme values, epoch markers

4. Custom styling: Visual customization

5. ICA integration: Component inspection

6. Epoch browsing: Individual trial navigation

7. File loading: Direct file inspection

The databrowser is essential for interactive EEG exploration and quality control!

Code Examples

Show Code

# Demo: Interactive Databrowser
# Shows the interactive databrowser for continuous and epoched data with ICA,
# analysis settings, custom channels, and file-based loading.

using EegFun
# Note: EegFun.example_path() resolves bundled example data paths.
# When using your own data, simply pass the file path directly, e.g.:
# dat = EegFun.read_raw_data("/path/to/your/data.bdf")
using JLD2

const DEMO_OUTPUT = "./demos/output/"
mkpath(DEMO_OUTPUT)

# read raw data
dat = EegFun.read_raw_data(EegFun.example_path("data/bdf/example1.bdf"));

# read and prepare layout file
layout = EegFun.read_layout(EegFun.example_path("layouts/biosemi/biosemi72.csv"));
EegFun.polar_to_cartesian_xy!(layout)

# create EegFun data structure (EegFun.ContinuousData)
# dat = EegFun.create_eegfun_data(dat); # works but no layout info available, thus, no topographic plots
dat = EegFun.create_eegfun_data(dat, layout);

# Basic databrowser
EegFun.plot_databrowser(dat);

# return some analysis settings
# check highpass filter, lowpass filter, rereference and mark some regions
# these are returned in analysis_settings
_, _, analysis_settings = EegFun.plot_databrowser(dat)

# we can now apply these settings to the data
dat_new = EegFun.apply_analysis_settings(dat, analysis_settings)

# settings applied
EegFun.plot_databrowser(dat_new)

#######################################################################
# We can add new columns to our data
dat = EegFun.read_raw_data(EegFun.example_path("data/bdf/example1.bdf"));
dat = EegFun.create_eegfun_data(dat, layout);

# Some minimal preprocessing (average reference and highpass filter)
EegFun.rereference!(dat, :avg)
EegFun.highpass_filter!(dat, 0.5)

# E.g. mark data sections (some extreme values and intervals around triggers; Boolean)
EegFun.is_extreme_value!(dat, 100);
EegFun.mark_epoch_intervals!(dat, [1, 2], [-0.2, 1.0]) # simple epoch marking with trigger 1 and 2

# Add new channel with a 10 Hz sine wave (50 μV amplitude)
dat.data[:, :sinewave] = 50.0 .* sin.(2π .* 10.0 .* dat.data[:, :time])

# these channels can now be viewed in the databrowser under the Extra channels tab
EegFun.plot_databrowser(dat)

EegFun.all_labels(dat)
EegFun.meta_labels(dat)
EegFun.channel_labels(dat)
EegFun.extra_labels(dat)

# try some custom styling
EegFun.plot_databrowser(dat; channel_line_width = 2, selection_color = (:green, 0.1))


#######################################################################
# run ica
ica_result = EegFun.run_ica(dat; sample_selection = EegFun.samples_not(:is_extreme_value_100), percentage_of_data = 25)
EegFun.plot_databrowser(dat, ica_result)


#######################################################################
# EPOCHS
epoch_cfg = [EegFun.EpochCondition(name = "ExampleEpoch1", trigger_sequences = [[1]])]
epochs = EegFun.extract_epochs(dat, epoch_cfg, (-0.5, 1.0))

# databrowser for epochs
EegFun.plot_databrowser(epochs[1])  # can now browse epochs
EegFun.plot_databrowser(epochs[1], ica_result)


# We can save our EegFun data to a file and pass a file name to plot_databrowser 
# plot_databrowser epochs from file
jldsave(joinpath(DEMO_OUTPUT, "dat.jld2"), data = dat)
jldsave(joinpath(DEMO_OUTPUT, "epochs.jld2"), data = epochs)
jldsave(joinpath(DEMO_OUTPUT, "ica.jld2"), data = ica_result)

# Plot by loading from file
EegFun.plot_databrowser(joinpath(DEMO_OUTPUT, "dat.jld2"))
EegFun.plot_databrowser(joinpath(DEMO_OUTPUT, "epochs.jld2"))
EegFun.plot_databrowser(joinpath(DEMO_OUTPUT, "dat.jld2"), joinpath(DEMO_OUTPUT, "ica.jld2"))
EegFun.plot_databrowser(joinpath(DEMO_OUTPUT, "epochs.jld2"), joinpath(DEMO_OUTPUT, "ica.jld2"))

See Also

• API Reference