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Global Field Power ​

This demo shows how to compute Global Field Power (GFP) and Global Dissimilarity from ERP data.

What are GFP and Global Dissimilarity? ​

  • Global Field Power (GFP) is the standard deviation across all channels at each time point. It provides a reference-independent measure of overall response strength — high GFP means strong, synchronised activity across the scalp.

  • Global Dissimilarity measures the rate of topographic change over time. Peaks in dissimilarity indicate moments when the scalp distribution is changing rapidly, which may mark transitions between ERP components or brain states.

Key Functions ​

FunctionReturns
gfpDataFrame with :time and :gfp columns
global_dissimilarityDataFrame with :time and :dissimilarity columns
gfp_and_dissimilarityDataFrame with both :gfp and :dissimilarity

Options ​

  • channel_selection — restrict to specific channels

  • normalize — scale to 0–100% for cross-dataset comparison

  • condition_selection — compute for specific conditions

Workflow Summary ​

GFP Calculation ​

  • All channels or a subset; raw or normalised

Global Dissimilarity ​

  • Identify topographic transition points

Combined Calculation ​

  • Compute both metrics efficiently in one call

Code Examples ​

Show Code
julia
# Demo: Global Field Power (GFP) and Global Dissimilarity
# Shows how to compute reference-independent measures of response strength
# and topographic change from ERP data.

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 ERP DATA
#######################################################################

dat = EegFun.read_data(EegFun.example_path("data/julia/erps/example1_erps_good.jld2"))


#######################################################################
# GLOBAL FIELD POWER (GFP)
#######################################################################

# GFP = standard deviation across all channels at each time point
# High GFP = strong, synchronized activity

# All channels
gfp_result = EegFun.gfp(dat)

# Specific channels
gfp_result = EegFun.gfp(dat, channel_selection = EegFun.channels([:Cz, :Pz, :Fz]))

# Normalized to 0-100% (for comparing across datasets)
gfp_result = EegFun.gfp(dat, normalize = true)


#######################################################################
# GLOBAL DISSIMILARITY
#######################################################################

# Global dissimilarity = rate of topographic change over time
# Peaks indicate transitions between brain states or ERP components

gd_result = EegFun.global_dissimilarity(dat)

# With normalization
gd_result = EegFun.global_dissimilarity(dat, normalize = true)


#######################################################################
# COMBINED CALCULATION
#######################################################################

# Calculate both GFP and global dissimilarity in one call
result = EegFun.gfp_and_dissimilarity(dat)

# Access the values (one result per condition)
result[1].time
result[1].gfp
result[1].dissimilarity


#######################################################################
# MULTIPLE CONDITIONS
#######################################################################

# GFP for all conditions
gfp_all = EegFun.gfp(dat)

# GFP for a specific condition
gfp_cond1 = EegFun.gfp(dat, condition_selection = EegFun.conditions([1]))

See Also ​