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Plot Time-Frequency ​

This demo shows how to plot time-frequency decomposition results as heatmaps.

When to Use ​

Use plot_tf to visualise the output of tf_morlet, tf_multitaper, or tf_stft — the three time-frequency decomposition methods in EegFun.

Key Parameters ​

ParameterDefaultDescription
channelFirst channelWhich channel to plot
baseline_intervalnothingPre-stimulus interval for on-the-fly baseline correction
baseline_method:db:db, :percent, :relchange, :absolute, :relative, or :zscore
colormap:viridisColour map for the heatmap
colorrangeautoExplicit (min, max) colour range
ylogscalefalseLog-scale the frequency axis
colorbartrueShow colour bar

What You'll Learn ​

  1. Plotting time-frequency data for a specific channel

  2. Applying baseline correction on the fly (dB, percent, relative change)

  3. Using log-scaled frequency axes

  4. Customising colour maps and colour range for publication figures

Code Examples ​

Show Code
julia
# Demo: Plotting Time-Frequency Data
# Shows how to plot time-frequency decomposition results as heatmaps,
# with options for baseline correction, colour maps, and log-scaled axes.

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")

#######################################################################
# LOAD DATA AND RUN TF DECOMPOSITION
#######################################################################

dat = EegFun.read_raw_data(EegFun.example_path("data/bdf/example1.bdf"))
layout = EegFun.read_layout(EegFun.example_path("layouts/biosemi/biosemi72.csv"))
EegFun.polar_to_cartesian_xy!(layout)
dat = EegFun.create_eegfun_data(dat, layout)

EegFun.highpass_filter!(dat, 0.5)
EegFun.lowpass_filter!(dat, 100.0)

epoch_cfg = EegFun.EpochCondition(name = "ExampleEpoch1", trigger_sequences = [[1]])
epochs = EegFun.extract_epochs(dat, epoch_cfg, (-0.5, 1.0))  # -200 to 1000 ms

EegFun.baseline!(epochs, (-0.2, 0.0))

# Morlet wavelet decomposition
tf_data = EegFun.tf_morlet(epochs, frequencies = 2:2:60)

#######################################################################
# BASIC TIME-FREQUENCY PLOT
#######################################################################

# plot first available channel
EegFun.plot_tf(tf_data)

#######################################################################
# SPECIFIC CHANNEL
#######################################################################

EegFun.plot_tf(tf_data, channel_selection = EegFun.channels(:Cz))

#######################################################################
# WITH BASELINE CORRECTION
#######################################################################

# apply dB baseline 
EegFun.plot_tf(tf_data, channel_selection = EegFun.channels(:Cz), baseline_interval = (-0.3, 0.0), baseline_method = :db)

# percentage change baseline
EegFun.plot_tf(tf_data, channel_selection = EegFun.channels(:Cz), baseline_interval = (-0.3, 0.0), baseline_method = :percent)

#######################################################################
# LOG-SCALED FREQUENCY AXIS
#######################################################################

EegFun.plot_tf(tf_data, channel_selection = EegFun.channels(:Cz), baseline_interval = (-0.3, 0.0), ylogscale = true)

#######################################################################
# CUSTOM COLOUR MAP AND RANGE
#######################################################################

EegFun.plot_tf(
    tf_data,
    channel_selection = EegFun.channels(:Cz),
    baseline_interval = (-0.3, 0.0),
    colormap = :RdBu,
    colorrange = (-15.0, 15.0),
)

See Also ​