### Start EEGLAB

Source: https://github.com/sccn/eeglab/blob/develop/sample_locs/1ST_README.txt

Start the EEGLAB environment in Matlab. This is sometimes necessary before using EEGLAB functions like pop_chanedit.

```matlab
>> eeglab
```

--------------------------------

### eeglab

Source: https://context7.com/sccn/eeglab/llms.txt

Launches the EEGLAB environment or controls its state. It can start the GUI, run in headless mode for scripting, display version information, load tutorial datasets, or rebuild the GUI window.

```APIDOC
## eeglab

### Description
Launches the EEGLAB GUI, controls its state, or displays version information.

### Usage
```matlab
% Start EEGLAB GUI
eeglab;

% Start without GUI (for scripting)
eeglab nogui;

% Print the current version
eeglab versions;

% Load tutorial continuous dataset and refresh the GUI
eeglab continuous;

% Load tutorial epoched dataset
eeglab epoch;

% Force rebuild the EEGLAB window
eeglab rebuild;
```
```

--------------------------------

### Install EEGLAB Plugins Programmatically

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

Install EEGLAB plugins silently without user prompts. Specify the plugin name and its main function.

```matlab
plugin_askinstall('ICLabel', 'iclabel', 0);
plugin_askinstall('clean_rawdata', 'clean_artifacts', 0);
plugin_askinstall('firfilt', 'pop_eegfiltnew', 0);
plugin_askinstall('picard', 'picard', 0);
plugin_askinstall('dipfit', 'pop_dipfit_settings', 0);
```

--------------------------------

### Launch or control EEGLAB environment

Source: https://context7.com/sccn/eeglab/llms.txt

Use the `eeglab` function to start the EEGLAB GUI, run in headless mode for scripting, check the version, load tutorial datasets, or rebuild the GUI window.

```matlab
% Start EEGLAB GUI
eeglab;
```

```matlab
% Start without GUI (for scripting)
eeglab nogui;
```

```matlab
% Print the current version
eeglab versions;
% Output: EEGLAB v2026.0.0
```

```matlab
% Load tutorial continuous dataset and refresh the GUI
eeglab continuous;
```

```matlab
% Load tutorial epoched dataset
eeglab epoch;
```

```matlab
% Force rebuild the EEGLAB window
eeglab rebuild;
```

--------------------------------

### Clone EEGLAB with Submodules

Source: https://github.com/sccn/eeglab/blob/develop/README.md

Use this command to clone the EEGLAB repository and its submodules. Ensure you have Git installed and configured.

```bash
git clone --recurse-submodules https://github.com/sccn/eeglab.git
```

--------------------------------

### Minimal EEGLAB Plugin Registration Function

Source: https://context7.com/sccn/eeglab/llms.txt

This is a minimal example of an EEGLAB plugin registration function. Save it as `eegplugin_<name>.m` inside a plugin folder (e.g., `plugins/myplugin1.0/`). It defines the plugin version and adds a custom menu item to the EEGLAB interface.

```matlab
% Minimal plugin registration function (save as eegplugin_myplugin.m
% inside plugins/myplugin1.0/)
function vers = eegplugin_myplugin(fig, try_strings, catch_strings)

    vers = 'myplugin1.0';   % returned version string

    % Build the menu callback using EEGLAB's try/catch wrapper strings
    % try_strings.check_data  - checks EEG.data exists before running
    % catch_strings.new_and_hist - stores result and adds to history
    cb = [try_strings.check_data ...
          '[EEG, LASTCOM] = my_processing_func(EEG);'
          catch_strings.new_and_hist];

    % Add a menu item under "Tools"
    toolsmenu = findobj(fig, 'Label', 'Tools');
    uimenu(toolsmenu, ...
        'Label',    'Run My Processing',
        'Callback', cb, ...
        'Separator', 'on');
end

% To install: place the plugin folder under eeglab/plugins/ and restart EEGLAB
% EEGLAB then prints: EEGLAB: adding "myplugin" v1.0 (see >> help eegplugin_myplugin)
```

--------------------------------

### Convert Electrode Locations using EEGLAB

Source: https://github.com/sccn/eeglab/blob/develop/sample_locs/1ST_README.txt

Use this command to open the EEGLAB channel editor and convert electrode location files. Ensure EEGLAB is started first if Matlab cannot find the function.

```matlab
>> pop_chanedit(readlocs('Standard-10-20-Cap81.ced'));
```

--------------------------------

### Create Simple Input GUI with inputgui

Source: https://github.com/sccn/eeglab/blob/develop/functions/guifunc/README.txt

Use inputgui to create a basic graphical interface for user input. The 'geometry' and 'uilist' parameters define the layout and content of the input fields.

```matlab
[res userdat strhalt restag] = inputgui('geometry', { 1 1 }, 'uilist', ...
                          { { 'style' 'text' 'string' 'Enter a value' } ...
                            { 'style' 'edit' 'string' '' 'tag' 'editstr'} });
```

--------------------------------

### Manage Datasets with eeg_store and eeg_retrieve

Source: https://context7.com/sccn/eeglab/llms.txt

Use `eeg_store` to save the current EEG dataset into the global `ALLEEG` array, either automatically selecting a slot or overwriting a specific one. `eeg_retrieve` loads a dataset from `ALLEEG` into the active `EEG` variable.

```matlab
[ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, 0);
```

```matlab
[ALLEEG, EEG] = eeg_store(ALLEEG, EEG, CURRENTSET);
```

```matlab
[ALLEEG, EEG] = eeg_store(ALLEEG, EEG, 3);
```

```matlab
[EEG, ALLEEG] = eeg_retrieve(ALLEEG, 2);
CURRENTSET = 2;
```

--------------------------------

### Standard Preprocessing Pipeline for ERP Analysis

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

This pipeline outlines the recommended order of operations for ERP analysis, including data loading, channel editing, artifact rejection, ICA, component classification, epoch extraction, baseline removal, and saving. Ensure correct file paths and channel names are used.

```matlab
EEG = pop_loadset('filename', 'data.set', 'filepath', '/path/');
% or: EEG = pop_fileio('/path/to/data.edf');
% or: [STUDY, ALLEEG] = pop_importbids(bidspath, 'studyName', 'MyStudy');
```

```matlab
EEG = pop_chanedit(EEG, 'lookup', 'standard-10-5-cap385.elp');
```

```matlab
EEG = pop_select(EEG, 'nochannel', {'EXG1','EXG2','EXG3','ECG','EMG'});
```

```matlab
EEG = pop_reref(EEG, []);
```

```matlab
EEG = pop_clean_rawdata(EEG, \
    'FlatlineCriterion', 5, \
    'ChannelCriterion', 0.8, \
    'LineNoiseCriterion', 4, \
    'Highpass', [0.25 0.75], \
    'BurstCriterion', 20, \
    'WindowCriterion', 0.25, \
    'BurstRejection', 'on', \
    'Distance', 'Euclidian', \
    'WindowCriterionTolerances', [-Inf 7]);
```

```matlab
EEG = pop_reref(EEG, []);
```

```matlab
% 7. Run ICA (pca -1 = auto-reduce for rank-deficient data)
% 'pca', -1 indicate to reduce the dimension by 1 to account for rank decrease by average reference in 6
EEG = pop_runica(EEG, 'icatype', 'runica', 'options', {'pca', -1});
```

```matlab
% 8. Classify and flag artifact components (ICLabel)
EEG = pop_iclabel(EEG, 'default');
EEG = pop_icflag(EEG, [NaN NaN; 0.9 1; 0.9 1; NaN NaN; NaN NaN; NaN NaN; NaN NaN]);
%                       Brain   Muscle  Eye    Heart  LineNoise ChanNoise Other
```

```matlab
% 9. Remove flagged components
EEG = pop_subcomp(EEG, find(EEG.reject.gcompreject), 0);
```

```matlab
% 10. Extract epochs
EEG = pop_epoch(EEG, {'xxx','yyy'}, [-1 2], 'epochinfo', 'yes');
EEG = eeg_checkset(EEG);
```

```matlab
% 11. Remove baseline
EEG = pop_rmbase(EEG, [-1000 0]);
```

```matlab
% 12. Save
EEG = pop_saveset(EEG, 'filename', 'processed.set', 'filepath', '/path/');
```

--------------------------------

### Load saved EEGLAB dataset (.set file)

Source: https://context7.com/sccn/eeglab/llms.txt

The `pop_loadset` function loads EEGLAB datasets from `.set` files. It can load the full dataset, only metadata ('info' mode), or multiple datasets into an array.

```matlab
% Load a single dataset (full data + structure)
EEG = pop_loadset('filename', 'subject01.set', 'filepath', '/data/');
```

```matlab
% Load only the dataset structure without data (useful for inspecting metadata)
EEG = pop_loadset('filename', 'subject01.set', 'filepath', '/data/', ...
                  'loadmode', 'info');
disp(EEG.nbchan)    % channel count available even without data
```

```matlab
% Load multiple datasets at once into an array
EEG = pop_loadset('filename', {'sub01.set','sub02.set'}, ...
                  'filepath', '/data/');
% EEG(1) and EEG(2) are separate dataset structures
```

```matlab
% Old-style (positional) calling convention
EEG = pop_loadset('subject01.set', '/data/');
```

--------------------------------

### Create STUDY from BIDS Data

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

Initiate a STUDY structure from BIDS data using pop_importbids. Options include specifying event types, importing channel locations, and naming the study.

```matlab
[STUDY, ALLEEG] = pop_importbids(filepath, 'eventtype', 'trial_type',
    'bidsevent', 'on', 'bidschanloc', 'on', 'studyName', 'MyStudy');
```

--------------------------------

### pop_loadset

Source: https://context7.com/sccn/eeglab/llms.txt

Loads a saved EEGLAB dataset (.set file) from disk into the EEG structure. It can load the full dataset, only metadata, or multiple datasets at once.

```APIDOC
## pop_loadset

### Description
Loads a saved EEGLAB dataset (.set file) from disk into the `EEG` structure. It can load the full dataset, only metadata, or multiple datasets at once.

### Usage
```matlab
% Load a single dataset (full data + structure)
EEG = pop_loadset('filename', 'subject01.set', 'filepath', '/data/');

% Load only the dataset structure without data (useful for inspecting metadata)
EEG = pop_loadset('filename', 'subject01.set', 'filepath', '/data/', ...
                  'loadmode', 'info');
disp(EEG.nbchan)    % channel count available even without data

% Load multiple datasets at once into an array
EEG = pop_loadset('filename', {'sub01.set','sub02.set'}, ...
                  'filepath', '/data/');
% EEG(1) and EEG(2) are separate dataset structures

% Old-style (positional) calling convention
EEG = pop_loadset('subject01.set', '/data/');
```
```

--------------------------------

### Create or Edit Multi-Subject STUDY with pop_study

Source: https://context7.com/sccn/eeglab/llms.txt

Assembles a STUDY structure for group-level analysis, grouping datasets by subject, condition, session, and group. Use 'gui' 'off' for scripting or 'on' for interactive editing. The 'add' parameter allows programmatic definition of dataset properties.

```matlab
% Create a new STUDY from all datasets currently in ALLEEG
[STUDY, ALLEEG] = pop_study([], ALLEEG, 'gui', 'off',
    'name',      'Auditory Oddball',
    'task',      'Three-stimulus oddball',
    'notes',     'N=20 healthy adults',
    'filename',  'oddball.study',
    'filepath',  '/data/studies/',
    'add', { ...
        {1 'subject' 'S01' 'condition' 'target'   'session' 1 'group' 'control'}
        {2 'subject' 'S01' 'condition' 'standard' 'session' 1 'group' 'control'}
        {3 'subject' 'S02' 'condition' 'target'   'session' 1 'group' 'control'}
        {4 'subject' 'S02' 'condition' 'standard' 'session' 1 'group' 'control'}
    });

% Edit an existing STUDY interactively
[STUDY, ALLEEG] = pop_study(STUDY, ALLEEG, 'gui', 'on');

% Inspect the STUDY structure
fprintf('Subjects: %s\n',    strjoin(STUDY.subject, ', '));
fprintf('Conditions: %s\n',  strjoin(STUDY.condition, ', '));
fprintf('Datasets: %d\n',    length(STUDY.datasetinfo));
```

--------------------------------

### Import Events from File

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

Use pop_importevent to load event data from a text file. Specify the file path and the fields to import, such as 'latency' and 'type'.

```matlab
EEG = pop_importevent(EEG, 'event', 'events.txt', 'fields', {'latency','type'});
```

--------------------------------

### Apply FIR Filters using pop_eegfiltnew

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

Use pop_eegfiltnew for FIR filtering. Specify 'locutoff' for high-pass and 'hicutoff' for low-pass filtering. This is the preferred method.

```matlab
EEG = pop_eegfiltnew(EEG, 'locutoff', 1);
           % 1 Hz high-pass
EEG = pop_eegfiltnew(EEG, 'hicutoff', 40);
          % 40 Hz low-pass
EEG = pop_eegfiltnew(EEG, 'locutoff', 1, 'hicutoff', 40);
  % Bandpass
```

--------------------------------

### Update EEGLAB Submodules

Source: https://github.com/sccn/eeglab/blob/develop/README.md

If you forgot to clone submodules initially, navigate to the EEGLAB directory and run these commands to update them. This is also useful for ensuring you have the latest submodule versions.

```bash
git submodule update --init --recursive --remote
git pull --recurse-submodules
```

--------------------------------

### Commit with Multi-line Message

Source: https://github.com/sccn/eeglab/blob/develop/CONTRIBUTING.md

Use this command to create a Git commit with a synopsis and a detailed message body, separated by two newlines. This format is recognized by GitHub for parsing commit messages.

```bash
$ git commit -m $'Commit synopsis\n\nHere is a much longer and wordier description of my proposed changes that doesn\'t fit into 80 characters.\nI can even spread the message body across multiple lines.'
```

--------------------------------

### pop_saveset

Source: https://context7.com/sccn/eeglab/llms.txt

Save an EEG dataset to disk. Writes the EEG structure to a .set file, optionally writing data to a separate binary .fdt file.

```APIDOC
## pop_saveset — Save an EEG dataset to disk

### Description
Writes the `EEG` structure to a `.set` file, optionally writing data to a separate binary `.fdt` file.

### Method
MATLAB Function

### Parameters
- **EEG** (struct) - The EEG structure to save.
- **filename** (string) - Optional. The name of the file to save.
- **filepath** (string) - Optional. The directory to save the file in.
- **savemode** (string) - Optional. Can be 'onefile' (data embedded in .set), 'twofiles' (.set + .fdt), or 'resave' (save to current location).
- **check** (string) - Optional. Enable consistency check ('on' or 'off').
- **version** (string) - Optional. Matlab version format (e.g., '7.3' for HDF5).

### Request Example
```matlab
% Save to a new file (one-file format, data embedded in .set)
EEG = pop_saveset(EEG, 'filename', 'subject01_filtered.set', ...
                       'filepath', '/data/processed/', ...
                       'savemode', 'onefile');

% Save as two files: .set (structure) + .fdt (binary data) — memory efficient
EEG = pop_saveset(EEG, 'filename', 'subject01_filtered.set', ...
                       'filepath', '/data/processed/', ...
                       'savemode', 'twofiles');

% Resave to the same location already stored in EEG.filepath/EEG.filename
EEG = pop_saveset(EEG, 'savemode', 'resave');

% Save with extended consistency check and Matlab 7.3 format (HDF5, large files)
EEG = pop_saveset(EEG, 'filename', 'large_dataset.set', ...
                       'filepath', '/data/', ...
                       'check',   'on',
                       'version', '7.3');
```
```

--------------------------------

### Run MATLAB in Batch Mode

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

Execute MATLAB commands non-interactively for automation. Use the `-batch` flag followed by the desired command.

```bash
/Applications/MATLAB_R2025a.app/bin/matlab -batch "command_here"
```

```bash
/Applications/MATLAB_R2025a.app/bin/matlab -batch "cd('/Users/arno/GitHub/core_eeg/eeglab'); eeglab nogui; your_script"
```

--------------------------------

### Compute Paired T-test with Statcond

Source: https://github.com/sccn/eeglab/blob/develop/functions/statistics/README.txt

Demonstrates computing a paired t-test between two conditions using the statcond function. It shows both parametric and resampling (bootstrap and permutation) modes. For resampling, specify 'mode' and 'naccu' for the number of accurate repetitions.

```matlab
cond1 = rand(100,64,10);
cond2 = rand(100,64,10)+0.2;
```

```matlab
[t df p] = statcond({ cond1 cond2 });
```

```matlab
[t df p] = statcond({ cond1 cond2 }, 'mode', 'bootstrap', 'naccu', 1000);
```

```matlab
[t df p] = statcond({ cond1 cond2 }, 'mode', 'perm', 'naccu', 1000);
```

--------------------------------

### Save EEG Dataset with pop_saveset

Source: https://context7.com/sccn/eeglab/llms.txt

Use `pop_saveset` to save EEG data. Specify 'onefile' to embed binary data in the .set file, or 'twofiles' for separate .set and .fdt files for memory efficiency. 'resave' updates the existing file. Use 'check on' and 'version 7.3' for large datasets with extended checks.

```matlab
EEG = pop_saveset(EEG, 'filename', 'subject01_filtered.set', 
                       'filepath', '/data/processed/', 
                       'savemode', 'onefile');
```

```matlab
EEG = pop_saveset(EEG, 'filename', 'subject01_filtered.set', 
                       'filepath', '/data/processed/', 
                       'savemode', 'twofiles');
```

```matlab
EEG = pop_saveset(EEG, 'savemode', 'resave');
```

```matlab
EEG = pop_saveset(EEG, 'filename', 'large_dataset.set', 
                       'filepath', '/data/', 
                       'check',   'on', 
                       'version', '7.3');
```

--------------------------------

### pop_study

Source: https://context7.com/sccn/eeglab/llms.txt

Assembles a STUDY structure grouping multiple EEG datasets by subject, condition, session, and group for group-level statistical analysis.

```APIDOC
## pop_study — Create or edit a multi-subject STUDY

### Description
Assembles a STUDY structure grouping multiple EEG datasets by subject, condition, session, and group for group-level statistical analysis.

### Usage Examples

```matlab
% Create a new STUDY from all datasets currently in ALLEEG
[STUDY, ALLEEG] = pop_study([], ALLEEG, 'gui', 'off',
    'name',      'Auditory Oddball',
    'task',      'Three-stimulus oddball',
    'notes',     'N=20 healthy adults',
    'filename',  'oddball.study',
    'filepath',  '/data/studies/',
    'add', { ...
        {1 'subject' 'S01' 'condition' 'target'   'session' 1 'group' 'control'}
        {2 'subject' 'S01' 'condition' 'standard' 'session' 1 'group' 'control'}
        {3 'subject' 'S02' 'condition' 'target'   'session' 1 'group' 'control'}
        {4 'subject' 'S02' 'condition' 'standard' 'session' 1 'group' 'control'}
    });

% Edit an existing STUDY interactively
[STUDY, ALLEEG] = pop_study(STUDY, ALLEEG, 'gui', 'on');

% Inspect the STUDY structure
fprintf('Subjects: %s\n',    strjoin(STUDY.subject, ', '));
fprintf('Conditions: %s\n',  strjoin(STUDY.condition, ', '));
fprintf('Datasets: %d\n',    length(STUDY.datasetinfo));
```
```

--------------------------------

### Create Input GUI with Complex Geometry

Source: https://github.com/sccn/eeglab/blob/develop/functions/guifunc/README.txt

Define complex layouts for GUI elements using the 'geom' input in inputgui, similar to MATLAB's subplot function. This allows for precise control over element positioning and sizing.

```matlab
res = inputgui('geom', { {2 1 [0 0] [1 1]} {2 1 [1 0] [1 1]} }, 'uilist',
                          { { 'style' 'text' 'string' 'Enter a value' } ...
                            { 'style' 'edit' 'string' '' } });
```

--------------------------------

### eeg_store / eeg_retrieve

Source: https://context7.com/sccn/eeglab/llms.txt

Manage the ALLEEG dataset array. Store a processed dataset back into the ALLEEG global array, or retrieve a specific dataset from it.

```APIDOC
## eeg_store / eeg_retrieve — Manage the ALLEEG dataset array

### Description
Store a processed dataset back into the ALLEEG global array, or retrieve a specific dataset from it.

### Method
MATLAB Function

### Parameters
- **ALLEEG** (struct) - The ALLEEG global array.
- **EEG** (struct) - The EEG dataset to store or retrieve.
- **index** (numeric) - The index in ALLEEG to store/retrieve from. Use 0 for auto-selection during store.

### Return Values
- **ALLEEG** (struct) - The updated ALLEEG array.
- **EEG** (struct) - The retrieved or stored EEG dataset.
- **CURRENTSET** (numeric) - The index of the current dataset in ALLEEG.

### Request Example
```matlab
% Store the current EEG dataset into ALLEEG (auto-select slot)
[ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, 0);
% CURRENTSET holds the assigned index

% Overwrite the current slot in ALLEEG with the modified EEG
[ALLEEG, EEG] = eeg_store(ALLEEG, EEG, CURRENTSET);

% Store into a specific slot (e.g. slot 3)
[ALLEEG, EEG] = eeg_store(ALLEEG, EEG, 3);

% Retrieve dataset 2 from ALLEEG as the active dataset
[EEG, ALLEEG] = eeg_retrieve(ALLEEG, 2);
CURRENTSET = 2;
```
```

--------------------------------

### eeg_getversion

Source: https://context7.com/sccn/eeglab/llms.txt

Retrieves the EEGLAB version string and optionally a numeric representation and release date for version comparisons.

```APIDOC
## eeg_getversion

### Description
Retrieves the EEGLAB version string and optional numeric representation.

### Usage
```matlab
% Get version string only
vers = eeg_getversion();

% Get version string and numeric value
[vers, vnum, releaseDate] = eeg_getversion();

% Conditional version check in a script
[~, vnum] = eeg_getversion();
if vnum < 2024
    error('EEGLAB 2024 or newer required');
end
```
```

--------------------------------

### pop_importdata

Source: https://context7.com/sccn/eeglab/llms.txt

Imports EEG data from various sources (Matlab variable, ASCII, binary float) into an EEGLAB EEG structure. It supports headless operation with key-value pairs.

```APIDOC
## pop_importdata

### Description
Imports EEG data from a Matlab variable, ASCII file, or binary float file and constructs the `EEG` structure. Accepts `'key', value` pairs for headless use.

### Usage
```matlab
% Import a Matlab variable already in the workspace
% mydata is a [channels x timepoints] matrix
mydata = randn(64, 30000);   % 64 channels, 30000 samples
EEG = pop_importdata(... 
    'setname',    'My EEG recording', ...
    'data',       'mydata', ...        % workspace variable name
    'dataformat', 'array', ...
    'srate',      256, ...             % sampling rate in Hz
    'chanlocs',   'standard-10-20.locs', ...
    'subject',    'S01', ...
    'condition',  'rest');

% Verify the structure
disp(EEG.nbchan)   % 64
disp(EEG.srate)    % 256
disp(EEG.pnts)     % 30000

% Import from a float32 binary file
EEG = pop_importdata(... 
    'setname',    'Recorded EEG', ...
    'data',       '/data/subject01.fdt', ...
    'dataformat', 'float32le', ...
    'nbchan',     64, ...
    'srate',      512, ...
    'xmin',       0);
```
```

--------------------------------

### Select Data Subsets with pop_select

Source: https://context7.com/sccn/eeglab/llms.txt

Returns a new dataset by retaining or excluding specific channels, time ranges, or epoch indices. Supports selection by channel number, label, epoch index, and continuous time windows.

```matlab
% Retain only channels 1–64 (remove auxiliary channels)
EEG = pop_select(EEG, 'channel', 1:64);
```

```matlab
% Keep channels by label
EEG = pop_select(EEG, 'channel', {'Fp1','Fp2','F3','F4','Fz',
                                   'C3','C4','Cz','P3','P4','Pz'});
```

```matlab
% Remove channels by label (exclude EOG and EMG)
EEG = pop_select(EEG, 'nochannel', {'EOG1','EOG2','EMG1','EMG2'});
```

```matlab
% Keep only epochs 10–50
EEG = pop_select(EEG, 'trial', 10:50);
```

```matlab
% Keep continuous data from 30 s to 120 s
EEG = pop_select(EEG, 'time', [30  120]);
```

```matlab
% Keep two time windows of continuous data
EEG = pop_select(EEG, 'time', [10 60; 70 120]);
```

```matlab
% Trim each epoch to [-100 500] ms
EEG = pop_select(EEG, 'time', [-0.1  0.5]);   % in seconds
```

--------------------------------

### Display Question Dialog with questdlg2

Source: https://github.com/sccn/eeglab/blob/develop/functions/guifunc/README.txt

Replace the standard MATLAB questdlg function with questdlg2 for customizable question dialogs. This function allows specifying default answers and button labels.

```matlab
ButtonName = questdlg2('What is your favorite color?', 'Color Question', ...
                         'Red', 'Green', 'Blue', 'Green');
```

--------------------------------

### pop_spectopo

Source: https://context7.com/sccn/eeglab/llms.txt

Computes and plots power spectral density (PSD) for all channels and overlays topographic maps at selected frequencies.

```APIDOC
## pop_spectopo — Plot channel or component power spectra and scalp maps

### Description
Computes and plots power spectral density (PSD) for all channels and overlays topographic maps at selected frequencies.

### Usage Examples

```matlab
% Plot channel spectra for the full dataset, show topo maps at 10, 20, 30 Hz
pop_spectopo(EEG, 1, [], 'EEG',
             'freqs',      [10  20  30],
             'freqrange',  [1   50],
             'percent',    100);

% Plot spectra using 50 % of data (faster)
pop_spectopo(EEG, 1, [], 'EEG',
             'percent', 50, 'freqrange', [1 80]);

% Plot component spectra and show the 3 components contributing most at 10 Hz
pop_spectopo(EEG, 0, [], 'EEG',   % dataflag=0 -> components
             'freqs',     [10],
             'nicamaps',  3,
             'freqrange', [1 50]);

% Capture output: spectra matrix, frequencies, components, etc.
[spectra, freqs, speccomp, contrib] = pop_spectopo(EEG, 1, [0 EEG.xmax*1000]);
% spectra: [nchans x nfreqs], freqs: [1 x nfreqs]
```
```

--------------------------------

### Retrieve EEGLAB version information

Source: https://context7.com/sccn/eeglab/llms.txt

The `eeg_getversion` function returns the EEGLAB version string and optionally a numeric representation and release date. Useful for conditional logic in scripts.

```matlab
% Get version string only
vers = eeg_getversion();
% vers = '2026.0.0'
```

```matlab
% Get version string and numeric value
[vers, vnum, releaseDate] = eeg_getversion();
% vers        = '2026.0.0'
% vnum        = 2026    (numeric, for comparisons)
% releaseDate = '14-Feb-2026 14:55:42'
```

```matlab
% Conditional version check in a script
[~, vnum] = eeg_getversion();
if vnum < 2024
    error('EEGLAB 2024 or newer required');
end
```

--------------------------------

### Plot ERP Data

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

Configure ERP plotting parameters with pop_erpparams and then generate the plot using std_erpplot. Specify channels and the design to use for plotting.

```matlab
STUDY = pop_erpparams(STUDY, 'topotime', 350);
STUDY = std_erpplot(STUDY, ALLEEG, 'channels', {ALLEEG(1).chanlocs.labels}, 'design', 1);
```

--------------------------------

### Plot Power Spectra and Scalp Maps with pop_spectopo

Source: https://context7.com/sccn/eeglab/llms.txt

Computes and plots power spectral density (PSD) for channels or components, overlaying topographic maps at specified frequencies. Use 'percent' to control the data fraction used for computation, affecting speed and accuracy.

```matlab
% Plot channel spectra for the full dataset, show topo maps at 10, 20, 30 Hz
pop_spectopo(EEG, 1, [], 'EEG',
             'freqs',      [10  20  30],
             'freqrange',  [1   50],
             'percent',    100);

% Plot spectra using 50 % of data (faster)
pop_spectopo(EEG, 1, [], 'EEG',
             'percent', 50, 'freqrange', [1 80]);

% Plot component spectra and show the 3 components contributing most at 10 Hz
pop_spectopo(EEG, 0, [], 'EEG', ...   % dataflag=0 -> components
             'freqs',     [10],
             'nicamaps',  3,
             'freqrange', [1 50]);

% Capture output: spectra matrix, frequencies, components, etc.
[spectra, freqs, speccomp, contrib] = pop_spectopo(EEG, 1, [0 EEG.xmax*1000]);
% spectra: [nchans x nfreqs], freqs: [1 x nfreqs]
```

--------------------------------

### Add New Events Programmatically

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

This script demonstrates how to add new events to the EEG structure by iterating through existing events and creating new ones based on specific criteria. Ensure event consistency after modification using eeg_checkset.

```matlab
for i = 1:length(EEG.event)
    if strcmpi(EEG.event(i).type, 'stimulus')
        EEG.event(end+1) = EEG.event(i);
        EEG.event(end).latency = EEG.event(i).latency - 0.1*EEG.srate;  % 100ms before
        EEG.event(end).type = 'cue';
    end
end
EEG = eeg_checkset(EEG, 'eventconsistency');
```

--------------------------------

### Import raw data into EEG structure

Source: https://context7.com/sccn/eeglab/llms.txt

Use `pop_importdata` to import EEG data from Matlab variables, ASCII, or binary files into an `EEG` structure. Supports headless operation with key-value pairs.

```matlab
% Import a Matlab variable already in the workspace
% mydata is a [channels x timepoints] matrix
mydata = randn(64, 30000);   % 64 channels, 30000 samples
EEG = pop_importdata( ...
    'setname',    'My EEG recording', ...
    'data',       'mydata', ...        % workspace variable name
    'dataformat', 'array',
    'srate',      256, ...             % sampling rate in Hz
    'chanlocs',   'standard-10-20.locs', ...
    'subject',    'S01', ...
    'condition',  'rest');

% Verify the structure
disp(EEG.nbchan)   % 64
disp(EEG.srate)    % 256
disp(EEG.pnts)     % 30000
```

```matlab
% Import from a float32 binary file
EEG = pop_importdata( ...
    'setname',    'Recorded EEG', ...
    'data',       '/data/subject01.fdt', ...
    'dataformat', 'float32le', ...
    'nbchan',     64, ...
    'srate',      512, ...
    'xmin',       0);
```

--------------------------------

### Apply Legacy FIR Filter using pop_eegfilt

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

The legacy pop_eegfilt function can also be used for FIR filtering. Note the different argument order for high-pass and low-pass.

```matlab
EEG = pop_eegfilt(EEG, 1, 0);
   % 1 Hz high-pass
EEG = pop_eegfilt(EEG, 0, 40);
  % 40 Hz low-pass
```

--------------------------------

### Create STUDY Design

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

Define a STUDY design using std_makedesign. This involves specifying the dataset, design name, and variables with their types and values.

```matlab
STUDY = std_makedesign(STUDY, ALLEEG, 1, 'name', 'Design1',
    'variable1', 'type', 'values1', {'target','standard'},
    'vartype1', 'categorical', 'subjselect', STUDY.subject);
```

--------------------------------

### Re-reference EEG Data with pop_reref

Source: https://context7.com/sccn/eeglab/llms.txt

Re-reference EEG data using `pop_reref`. Options include average reference (pass `[]`), referencing a specific electrode by index or name, or using a pair of electrodes. Channels can be excluded, and the old reference can be kept. ICA decompositions can be removed during re-referencing.

```matlab
EEG = pop_reref(EEG, []);
```

```matlab
EEG = pop_reref(EEG, 48);
```

```matlab
EEG = pop_reref(EEG, 'Cz');
```

```matlab
EEG = pop_reref(EEG, [65 66], 'keepref', 'on');
```

```matlab
EEG = pop_reref(EEG, [], 'exclude', [65 66 67 68]);
```

```matlab
EEG = pop_reref(EEG, [], 'refica', 'remove');
```

--------------------------------

### Event-Related Time-Frequency Analysis with pop_newtimef

Source: https://context7.com/sccn/eeglab/llms.txt

Performs event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) using wavelet decomposition or FFT. Specify 'typeplot' to analyze channels (1) or components (0). Use 'plotersp' and 'plotitc' to control output plotting.

```matlab
% Time-frequency decomposition of channel 10 (Cz), default settings
pop_newtimef(EEG, 1, 10);   % typeplot=1 -> channel data

% Channel 10, specify epoch window, 3–50 Hz, 3–0.5 wavelet cycles
pop_newtimef(EEG, 1, 10, [-500  800], [3  0.5], ...
             'freqs',   [3  50],
             'baseline', [-200  0],
             'plotitc',  'on');

% Component 2 time-frequency (typeplot=0)
pop_newtimef(EEG, 0, 2, [-500 800], [3 0.5], ...
             'freqscale', 'log',
             'alpha',      0.05);   % mask non-significant at p<0.05

% Return ERSP and ITC matrices without plotting
[ersp, itc, ~, times, freqs] = pop_newtimef(EEG, 1, 10, ...
    [-500  800], [3  0.5], 'plotersp', 'off', 'plotitc', 'off');
% ersp: [nfreqs x ntimes], itc: [nfreqs x ntimes]
fprintf('ERSP size: %d freqs x %d times\n', length(freqs), length(times));
```

--------------------------------

### pop_newtimef

Source: https://context7.com/sccn/eeglab/llms.txt

Computes and plots event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) for a single channel or component using wavelet decomposition or FFT.

```APIDOC
## pop_newtimef — Event-related time-frequency analysis (ERSP / ITC)

### Description
Computes and plots event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) for a single channel or component using wavelet decomposition or FFT.

### Usage Examples

```matlab
% Time-frequency decomposition of channel 10 (Cz), default settings
pop_newtimef(EEG, 1, 10);   % typeplot=1 -> channel data

% Channel 10, specify epoch window, 3–50 Hz, 3–0.5 wavelet cycles
pop_newtimef(EEG, 1, 10, [-500  800], [3  0.5],
             'freqs',   [3  50],
             'baseline', [-200  0],
             'plotitc',  'on');

% Component 2 time-frequency (typeplot=0)
pop_newtimef(EEG, 0, 2, [-500 800], [3 0.5],
             'freqscale', 'log',
             'alpha',      0.05);   % mask non-significant at p<0.05

% Return ERSP and ITC matrices without plotting
[ersp, itc, ~, times, freqs] = pop_newtimef(EEG, 1, 10,
    [-500  800], [3  0.5], 'plotersp', 'off', 'plotitc', 'off');
% ersp: [nfreqs x ntimes], itc: [nfreqs x ntimes]
fprintf('ERSP size: %d freqs x %d times\n', length(freqs), length(times));
```
```

--------------------------------

### Apply FIR Filter with pop_eegfilt

Source: https://context7.com/sccn/eeglab/llms.txt

Apply FIR filters (highpass, lowpass, bandpass, notch) to EEG data using `pop_eegfilt`. Set cutoff frequencies to 0 to skip a filter type. A notch filter can be applied by passing a range like [45 55] and setting `revfilt` to 1. An optional filter order and plotting of the frequency response are available.

```matlab
EEG = pop_eegfilt(EEG, 1, 0);
```

```matlab
EEG = pop_eegfilt(EEG, 0, 40);
```

```matlab
EEG = pop_eegfilt(EEG, 1, 40);
```

```matlab
EEG = pop_eegfilt(EEG, 45, 55, [], 1);
```

```matlab
filtorder = 3 * fix(EEG.srate / 0.5); 
EEG = pop_eegfilt(EEG, 0.5, 100, filtorder, 0, 0, 1);
```

--------------------------------

### Precompute Measures for STUDY

Source: https://github.com/sccn/eeglab/blob/develop/CLAUDE.md

Use std_precomp to precompute various measures for a STUDY. Options include saving trials, removing ICA components, interpolating channels, and recomputing ERPs.

```matlab
[STUDY, ALLEEG] = std_precomp(STUDY, ALLEEG, {}, 'savetrials', 'on',
    'rmicacomps', 'on', 'interp', 'on', 'recompute', 'on', 'erp', 'on');
```

--------------------------------

### pop_select

Source: https://context7.com/sccn/eeglab/llms.txt

Selects subsets of EEG data based on channels, time ranges, or epoch indices. It can retain or exclude specific data portions and supports selection by channel labels or time windows.

```APIDOC
## pop_select — Select subsets of data (channels, time, epochs)

### Description
Returns a new dataset retaining or excluding specific channels, time ranges, or epoch indices.

### Usage Examples

```matlab
% Retain only channels 1–64 (remove auxiliary channels)
EEG = pop_select(EEG, 'channel', 1:64);

% Keep channels by label
EEG = pop_select(EEG, 'channel', {'Fp1','Fp2','F3','F4','Fz',
                                   'C3','C4','Cz','P3','P4','Pz'});

% Remove channels by label (exclude EOG and EMG)
EEG = pop_select(EEG, 'nochannel', {'EOG1','EOG2','EMG1','EMG2'});

% Keep only epochs 10–50
EEG = pop_select(EEG, 'trial', 10:50);

% Keep continuous data from 30 s to 120 s
EEG = pop_select(EEG, 'time', [30  120]);

% Keep two time windows of continuous data
EEG = pop_select(EEG, 'time', [10 60; 70 120]);

% Trim each epoch to [-100 500] ms
EEG = pop_select(EEG, 'time', [-0.1  0.5]);   % in seconds
```
```

--------------------------------

### Validate EEG Structure with eeg_checkset

Source: https://context7.com/sccn/eeglab/llms.txt

Employ `eeg_checkset` to validate and optionally repair the EEG structure. Specific flags like 'data', 'contdata', 'epoch', 'ica', 'chanloc', 'icaconsist', and 'makeur' allow targeted checks or modifications.

```matlab
EEG = eeg_checkset(EEG);
```

```matlab
[EEG, changes] = eeg_checkset(EEG, 'data');
```

```matlab
EEG = eeg_checkset(EEG, 'contdata');
```

```matlab
EEG = eeg_checkset(EEG, 'epoch');
```

```matlab
EEG = eeg_checkset(EEG, 'ica');
```

```matlab
EEG = eeg_checkset(EEG, 'chanloc');
```

```matlab
[ALLEEG, icaconsist] = eeg_checkset(ALLEEG, 'icaconsist');
disp(icaconsist)
```

```matlab
EEG = eeg_checkset(EEG, 'makeur');
```

--------------------------------

### Run ICA Decomposition with pop_runica

Source: https://context7.com/sccn/eeglab/llms.txt

Performs Independent Component Analysis (ICA) on EEG data using specified algorithms like Infomax, JADE, or FastICA. Populates `EEG.icaweights` and `EEG.icasphere`. Options include 'extended' mode for sub-Gaussian components and channel subsetting.

```matlab
% Run Infomax ICA with default settings
EEG = pop_runica(EEG, 'icatype', 'runica');
```

```matlab
% Run with the 'extended' option to capture sub-Gaussian (e.g., line noise) components
EEG = pop_runica(EEG, 'icatype', 'runica', 'extended', 1);
```

```matlab
% Run ICA on a subset of channels (skip EOG channels 65, 66)
EEG = pop_runica(EEG, 'icatype', 'runica', ...
                      'chanind', setdiff(1:EEG.nbchan, [65 66]));
```

```matlab
% Run JADE algorithm
EEG = pop_runica(EEG, 'icatype', 'jader');
```

```matlab
% After ICA, inspect weights
disp(size(EEG.icaweights))  % [nComponents x nChannels]
disp(size(EEG.icasphere))   % [nChannels x nChannels]
```

```matlab
% Compute ICA activations manually
icaact = (EEG.icaweights * EEG.icasphere) * reshape(EEG.data, EEG.nbchan, []);
```