{"url":"/dataset/phyaat","name":"PhyAAt","full_name":"Physiology of Auditory Attention","description_markdown":"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](https://phyaat.github.io/experiment)** \r\n\r\n### Dataset\r\nThe 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.\r\n\r\n- EEG Channels: 'AF3', 'F7', 'F3', 'FC5', 'T7', 'P7', 'O1', 'O2', 'P8', 'T8', 'FC6', 'F4', 'F8', 'AF4'\r\n- GSR Signal: Instantaneous and moving averaged signal streams\r\n- PPG:  PPG (ECG like signal), IBI (Inter Beat Interval ) and BPM (Beats per minute)\r\n\r\n### Download the dataset\r\n#### Using Python\r\nTo download the dataset, install **phyaat** library and download through it.\r\n\r\n***pip install phyaat***\r\n\r\n```\r\nimport phyaat as ph\r\n\r\n#to download dataset of subject 1 in given path 'dirpath\r\n\r\ndirPath = ph.download_data(baseDir='../PhyAAt_Data', subject=1,verbose=0,overwrite=False) \r\n\r\n#to download dataset of all the subjects\r\n\r\ndirPath = ph.download_data(baseDir='../PhyAAt_Data', subject=-1,verbose=0,overwrite=False)\r\n```\r\n#### Manually\r\nIf you are using other programming framework such as matlab or R, Download dataset manually from\r\n**[Github repository](https://github.com/Nikeshbajaj/PhyaatDataset)**\r\nand extract all the csv files.\r\n\r\nFor more details on downloading and using dataset, check here:  **[Getting Started](https://phyaat.github.io/introduction)**\r\n\r\n### Helper Scripts\r\nThere are starter scripts and benchmark code to start building models. They are available here - **[https://phyaat.github.io/modeling/](https://phyaat.github.io/modeling/)**","description_withheld":null,"homepage":"https://phyaat.github.io","introduced_date":"2020-05-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/phyaat-physiology-of-auditory-attention-to","title":"PhyAAt: Physiology of Auditory Attention to Speech Dataset","first_author":"Nikesh Bajaj","url":null},"license":{"name":"The 3-Clause BSD License","url":"https://phyaat.github.io/license.txt"},"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"},{"name":"EEG","url":"/datasets/modality/eeg"}],"tasks":[{"name":"Semanticity prediction","url":"/task/semanticity-prediction","datasets_with_task":"/datasets/task/semanticity-prediction"},{"name":"Attention Score Prediction","url":"/task/attention-score-prediction","datasets_with_task":"/datasets/task/attention-score-prediction"},{"name":"Noise Level Prediction","url":"/task/noise-level-prediction","datasets_with_task":"/datasets/task/noise-level-prediction"},{"name":"LWR Classification","url":"/task/lwr-classification","datasets_with_task":"/datasets/task/lwr-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["PhyAAt"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/attention-score-prediction-on-phyaat","task":"Attention Score Prediction","dataset_variant":"PhyAAt","rows":1,"metrics":["MAE"],"first_row_in_archive_order":{"model":"SVM","paper":"/paper/phyaat-physiology-of-auditory-attention-to","metrics":{"MAE":"29.65"},"code_links":[{"title":"Nikeshbajaj/phyaat","url":"https://github.com/Nikeshbajaj/phyaat"},{"title":"Nikeshbajaj/Notebooks","url":"https://github.com/Nikeshbajaj/Notebooks/tree/master/PhyAAt_Notebooks"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/lwr-classification-on-phyaat","task":"LWR Classification","dataset_variant":"PhyAAt","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"SVM","paper":"/paper/phyaat-physiology-of-auditory-attention-to","metrics":{"Accuracy":"81"},"code_links":[{"title":"Nikeshbajaj/phyaat","url":"https://github.com/Nikeshbajaj/phyaat"},{"title":"Nikeshbajaj/Notebooks","url":"https://github.com/Nikeshbajaj/Notebooks/tree/master/PhyAAt_Notebooks"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/noise-level-prediction-on-phyaat","task":"Noise Level Prediction","dataset_variant":"PhyAAt","rows":1,"metrics":["MAE"],"first_row_in_archive_order":{"model":"SVM","paper":"/paper/phyaat-physiology-of-auditory-attention-to","metrics":{"MAE":"4.75"},"code_links":[{"title":"Nikeshbajaj/phyaat","url":"https://github.com/Nikeshbajaj/phyaat"},{"title":"Nikeshbajaj/Notebooks","url":"https://github.com/Nikeshbajaj/Notebooks/tree/master/PhyAAt_Notebooks"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semanticity-prediction-on-phyaat","task":"Semanticity prediction","dataset_variant":"PhyAAt","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"SVM","paper":"/paper/phyaat-physiology-of-auditory-attention-to","metrics":{"Accuracy":"56"},"code_links":[{"title":"Nikeshbajaj/phyaat","url":"https://github.com/Nikeshbajaj/phyaat"},{"title":"Nikeshbajaj/Notebooks","url":"https://github.com/Nikeshbajaj/Notebooks/tree/master/PhyAAt_Notebooks"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/phyaat-physiology-of-auditory-attention-to","title":"PhyAAt: Physiology of Auditory Attention to Speech Dataset","date":"2020-05-23","rows_on_this_dataset":4,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}