EegFun

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Data ​

This demo demonstrates understanding the core data structures in EegFun.jl and how to access and subset data.

Core Data Structures ​

ContinuousData:

  • Raw EEG time series from continuous recording

  • Contains electrode data, triggers, sampling rate, metadata

  • Used for preprocessing, filtering, artifact detection

EpochData:

  • Segmented trials around experimental events

  • Organized by experimental conditions

  • Vector of DataFrames (one per trial)

ErpData:

  • Averaged event-related potentials

  • One waveform per condition

  • Used for statistical analysis and plotting

Data Access Functions ​

all_data: Complete data including channels and metadata

channel_data: Only EEG channel columns

meta_data: Only metadata columns (time, sample, triggers, etc.)

extra_data: Derived/computed channels (EOG, artifacts, boolean flags, etc.)

Workflow Summary ​

This demo shows basic data operations:

Load and Structure Data ​

  • Load raw BioSemi data

  • Load electrode layout

  • Create ContinuousData structure

Access Data Components ​

  • Extract all data, channel data, and metadata

  • Understand different data access patterns

Create Epochs ​

  • Define epoch conditions around triggers

  • Extract segmented trials

  • Access epoch data with selection functions

Subset Data ​

  • Subset by channel selection (specific electrodes)

  • Subset by sample selection (time intervals)

  • Create smaller datasets for analysis

Code Examples ​

Show Code
julia
# Demo: Data Manipulation
# Shows basic data access, subsetting, and manipulation functions.

using EegFun

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

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

# create EegFun data structure (EegFun.ContinuousData)
dat = EegFun.create_eegfun_data(dat, layout_file);

# data/channel access functions
EegFun.all_data(dat)
EegFun.meta_data(dat)
EegFun.channel_data(dat)
EegFun.extra_data(dat) # empty

# some epoched data
epoch_cfg = [EegFun.EpochCondition(name = "ExampleEpoch1", trigger_sequences = [[1]])]
epochs = EegFun.extract_epochs(dat, epoch_cfg, (-2, 4))

EegFun.all_data(epochs)
EegFun.all_data(epochs, epoch_selection = EegFun.epochs(1:2))
EegFun.meta_data(epochs)
EegFun.meta_data(epochs, epoch_selection = EegFun.epochs(1:2))
EegFun.channel_data(epochs)
EegFun.channel_data(epochs, epoch_selection = EegFun.epochs(1:2))

# We can subset out EegFun datatypes
dat_subset = EegFun.subset(dat, channel_selection = EegFun.channels([:Fp1, :Fp2])) # only Fp1 and Fp2
EegFun.all_data(dat_subset)

dat_subset = EegFun.subset(dat, sample_selection = x -> x.sample .<= 10_000)       # first 10000 samples
EegFun.all_data(dat_subset)

dat_subset = EegFun.subset(dat, sample_selection = x -> x.time .<= 10)             # first 10 seconds
EegFun.all_data(dat_subset)

See Also ​