Datasets › PhyAAt
PhyAAt (Physiology of Auditory Attention)
The dataset contains a collection of physiological signals (EEG, GSR, PPG) obtained from an experiment of the auditory attention on natural speech. Ethical Approval was acquired for the experiment. Details of the experiment can be found here https://phyaat.github.io/experiment
Dataset
The dataset contain three physiological signals recorded at sampling rate of 128Hz from 25 healthy subjects during the experiment. Electroenceplogram (EEG) signal is recorded using a 14-channel Emotiv Epoc device. Two signal streams of Galvanic Skin Response (GSR) were recorded, instantaneous sample and moving averaged signal. From photoplethysmogram (PPG) sensor (pulse sensor), a raw signal, inter-beat interval (IBI), and pulse rate were recorded. All the signals were properly labeled.
- EEG Channels: 'AF3', 'F7', 'F3', 'FC5', 'T7', 'P7', 'O1', 'O2', 'P8', 'T8', 'FC6', 'F4', 'F8', 'AF4'
- GSR Signal: Instantaneous and moving averaged signal streams
- PPG: PPG (ECG like signal), IBI (Inter Beat Interval ) and BPM (Beats per minute)
Download the dataset
Using Python
To download the dataset, install phyaat library and download through it.
pip install phyaat
import phyaat as ph
#to download dataset of subject 1 in given path 'dirpath
dirPath = ph.download_data(baseDir='../PhyAAt_Data', subject=1,verbose=0,overwrite=False)
#to download dataset of all the subjects
dirPath = ph.download_data(baseDir='../PhyAAt_Data', subject=-1,verbose=0,overwrite=False)
Manually
If you are using other programming framework such as matlab or R, Download dataset manually from Github repository and extract all the csv files.
For more details on downloading and using dataset, check here: Getting Started
Helper Scripts
There are starter scripts and benchmark code to start building models. They are available here - https://phyaat.github.io/modeling/
Benchmarks archive 2025-07-28
All 4 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Attention Score Prediction | PhyAAt | SVM MAE 29.65 | PhyAAt: Physiology of Auditory Attention to Speech Dataset | Nikeshbajaj/phyaat +1 | 1 | Compare |
| LWR Classification | PhyAAt | SVM Accuracy 81 | PhyAAt: Physiology of Auditory Attention to Speech Dataset | Nikeshbajaj/phyaat +1 | 1 | Compare |
| Noise Level Prediction | PhyAAt | SVM MAE 4.75 | PhyAAt: Physiology of Auditory Attention to Speech Dataset | Nikeshbajaj/phyaat +1 | 1 | Compare |
| Semanticity prediction | PhyAAt | SVM Accuracy 56 | PhyAAt: Physiology of Auditory Attention to Speech Dataset | Nikeshbajaj/phyaat +1 | 1 | Compare |
Papers archive 2025-07-28
1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 4. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| PhyAAt: Physiology of Auditory Attention to Speech Dataset | 2 | 4 | 23 May 2020 | not harvested |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- PhyAAt
1 variant name, as the archive lists them.
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