MOV-AAD Dataset

Motivation

Auditory attention decoding (AAD) is often evaluated on static, simplified speech scenes that poorly match everyday listening conditions. MOV-AAD addresses this gap by providing a large-scale dataset designed to study auditory attention under moving, naturalistic conversations. The dataset combines 64-channel EEG with synchronized physiological recordings, enabling analysis of cross-modal neural and physiological markers of attention and listening effort in ecologically valid listening scenarios.

Key Features

• 64-channel EEG recordings from 50 participants
• Synchronized multimodal physiological signals (eye tracking, respiration, GSR, PPG, SpO₂, temperature, motion)
• Dynamic spatial movement of conversational speech sources (±90° azimuth)
• Naturalistic conversational speech stimuli with behavioral attention tracking
• Over 4,800 trials with approximately 45 minutes of multi-conversation tasks recordings plus 30 minutes of single-conversation tasks recordings per participant

Applications

This dataset can be used for:

• Robust auditory attention decoding in realistic spatial dynamics
• Multimodal attention modeling integrating neural and autonomic measures
• Listening effort assessment using peripheral physiological markers
• Intersubject neural response analysis during naturalistic speech
• Benchmarking selective auditory attention algorithms

Dataset Structure

The final dataset structure will be finalized and documented here upon release.

MOV-AAD/
├── participants/
│   ├── sub-001/
│   │   ├── eeg/
│   │   ├── physio/
│   │   ├── behavioral/
│   │   └── stimuli/
│   ├── sub-002/
│   └── ...sub-050/
├── stimuli/
├── preprocessing_scripts/
├── README.md
└── dataset_info.json

File Formats

• EEG data: 64 channels @ 1200 Hz with 60 Hz notch filter
• Physiological signals: 1200 Hz sampling rate
• Audio recordings: Synchronized playback recordings for temporal alignment
• Behavioral data: Event markers and timestamps
• Spatial information: Azimuth trajectories

Download Instructions

# Using wget
wget [ZENODO_URL]

# Using curl
curl -O [ZENODO_URL]

# Extract
unzip MOV-AAD-dataset.zip

# Example loading code will be provided
# upon dataset release with full documentation

Preprocessing Notes

• EEG channels with abnormal variance are automatically detected and replaced via spherical interpolation
• 3s pre-onset baseline segment included for each trial
• Temporal alignment established via cross-correlation of recorded audio with original stimulus
• Peripheral modalities provided in minimally processed form (1200 Hz with hardware notch filtering)
• Preprocessing scripts will be provided for reproducibility

If you use the MOV-AAD dataset or baseline models in your research, please cite our paper:

@article{mov_aad_2026,
  title={MOV-AAD: A Large-Scale Multimodal Dataset for Auditory Attention During Moving Conversations},
  author={LastName, FirstName and Collaborator, Gavin and JointAuthor, Author},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2026}
}