The dataset includes raw EEG recordings collected during a perceptual decision-making task involving anticipatory cues for auditory and visual stimuli.

Folder Structure

The dataset is organized by participant:

[participant ID]/
    participantID_calib_000[block number].eeg
    participantID_calib_000[block number].vhdr
    participantID_calib_000[block number].vmrk
        ...
    participantID_test_000[block number].eeg
    participantID_test_000[block number].vhdr
    participantID_test_000[block number].vmrk
        ...

Description

• Each folder contains all data for a single participant (whose ID is the folder name).
• Each participant completed two phases:
  • calibXXXX: calibration phase
  • testXXXX: main experimental phase
• Each phase contains multiple recording blocks: 4 calibration bloacks and 3 test blocks

Data Format

EEG data are stored in BrainVision format, consisting of three files per recording:

• .eeg → raw signal data
• .vhdr → header file (metadata, channel info, sampling rate)
• .vmrk → marker file (event triggers)

These files should always be kept together.

Recording Parameters

• Sampling rate: 1000 Hz
• Number of channels: 32
• Electrode layout: standard 10–20 system
• Reference: FCz

Relevant event markers

Recommended tools

We recommend using Python MNE to visualize and process this raw data.

Analysis code

The code used to analyze this data is available on GitHub.