REVELIO: Cognitive Gaming Public Set

Multimodal physiological and behavioral recordings for task load estimation during an n-back test and two commercial video games (Hogwarts Legacy, Overcooked! 2). This record holds the Public Set: 30 of the 45 REVELIO gaming sessions.

How to cite

Please cite this dataset record and both papers: ADABase introduced the acquisition setup, and REVELIO extends it with the gaming cohort.

@article{oppelt2023adabase,
  author  = {Oppelt, Maximilian P. and Foltyn, Andreas and Deuschel, Jessica and Lang, Nadine R.
             and Holzer, Nina and Eskofier, Bjoern M. and Yang, Seung Hee},
  title   = {{ADABase}: A Multimodal Dataset for Cognitive Load Estimation},
  journal = {Sensors},
  year    = {2023},
  volume  = {23},
  number  = {1},
  pages   = {340},
  doi     = {10.3390/s23010340}
}

@article{oppelt2026revelio,
  author  = {Oppelt, Maximilian P. and Foltyn, Andreas and Lang-Richter, Nadine R. and Eskofier, Bjoern M.},
  title   = {{REVELIO}: Multimodal Task Load Estimation for Cross-Domain Generalization},
  journal = {International Journal of Human--Computer Interaction},
  publisher = {Taylor \& Francis},
  year    = {2026},
  doi     = {10.48550/arXiv.2509.01642},
  note    = {In press. DOI refers to the arXiv preprint arXiv:2509.01642}
}

Dataset overview

Participants. ADABase and REVELIO together comprise 96 sessions from 88 subjects (51 in the driving study, 37 in the gaming study). Five people took part in both studies, so the subjects are 83 different individuals. The sessions are split into four Zenodo records:

The REVELIO gaming cohort comprises 45 sessions from 37 participants.

Tasks. Each session contains an n-back test and two commercial video games, Hogwarts Legacy and Overcooked! 2, each with baseline/training and task segments. Participants rated their task load with the NASA-TLX after each task segment. A session lasted about 2–3 hours. See the REVELIO paper for the protocol.

Modalities. The sensors, recording equipment and n-back protocol are the same as in ADABase.

• Physiological signals (BIOPAC MP160, recorded at 2000 Hz, stored at 1 kHz): ECG, PPG, EDA, EMG (trapezius), respiration, skin temperature
• Eye tracking (Tobii Pro Fusion, 250 Hz): gaze and pupil diameter
• Behavioral features extracted from facial video (camera at 25 Hz): facial action units, head pose, body landmarks. No raw video or images are released.
• Performance measures, NASA-TLX ratings, and participant meta-information (e.g. personality traits, gaming experience)

Public/private split. The 45 sessions are split into two Zenodo records:

• Public Set: 30 sessions, sampled at random, access on request.
• Private Set: 15 sessions, retained as a hidden test set.

Data collection was approved by the ethics committee of Friedrich-Alexander-Universität Erlangen-Nürnberg (no. 129_21 B, 21 April 2021). All participants gave written informed consent, and only participants who consented to data sharing are included.

Files

This record contains 30 HDF5 files (25.02 GB in total, see file list), plus checksums.md5.

File naming scheme: <NNNN>-<CODE>.h5

• NNNN: four-digit participant ID. IDs are unique across ADABase and REVELIO, so the same NNNN always denotes the same person. IDs 0051–0082 are new in REVELIO. IDs 0009, 0013, 0029, 0031 and 0045 belong to participants who also took part in ADABase.
• CODE: pseudonym of the session. Participants with two sessions have two files with the same NNNN and different CODEs.

Example: 0013-SKR11.h5 and 0013-YAF19.h5 are the two gaming sessions of participant 0013.

0009-WMS15.h5, 0013-SKR11.h5, 0013-YAF19.h5, 0029-GPW07.h5, 0031-GBR27.h5, 0045-GGZ24.h5, 0045-LJT10.h5, 0051-CEN35.h5, 0052-COA12.h5, 0053-DBE20.h5, 0054-DFI08.h5, 0055-EAB20.h5, 0056-EEE10.h5, 0056-YWK22.h5, 0057-FHC08.h5, 0057-SGF31.h5, 0058-FIL28.h5, 0059-IUS14.h5, 0059-KNC19.h5, 0060-KBG08.h5, 0061-KOB08.h5, 0062-LMI20.h5, 0063-QGP09.h5, 0064-QIK35.h5, 0065-SKW18.h5, 0065-ZJC18.h5, 0066-VYM28.h5, 0067-ZAT15.h5, 0068-ZAU14.h5, 0069-ZYP27.h5

HDF5 structure

Each file is written with pandas (PyTables) and holds four tables, which pandas.read_hdf reads directly. Driving (ADABase) and gaming (REVELIO) files share this layout.

Channels in SIGNALS.

The full channel list is printed by the example in "Loading the data in Python".

Sampling rates. The biosignals are stored at 1 kHz. Streams with a lower native rate (eye tracker 250 Hz, video features 25 Hz) are NaN between their samples. Calling .dropna() on a column recovers the native samples. The row number equals the time in ms, so the start and stop arguments of pandas.read_hdf select a time range without reading the whole file. A file has about 9 million rows, so read only the columns and time range you need.

Annotation. Every sample in SIGNALS carries three labels:

• STUDY: the task. Time outside all tasks is n/a.
• PHASE: baseline, train (practice), test, or n/a between phases.
• LEVEL: numbers the conditions within a study and phase. In the gaming files, STUDY and PHASE are stored as pandas categories, which pandas.read_hdf decodes automatically.

Loading the data in Python

import pandas as pd

path = "0009-WMS15.h5"

# Tables and channels
with pd.HDFStore(path, "r") as store:
    print(store.keys())  # SIGNALS, SUBJECTIVE, PERFORMANCE, META
    channels = list(store.get_storer("SIGNALS").non_index_axes[0][1])
print(len(channels), channels[:10])

# Small tables
subjective = pd.read_hdf(path, "SUBJECTIVE")
performance = pd.read_hdf(path, "PERFORMANCE").dropna(axis=1, how="all")
meta = pd.read_hdf(path, "META")

# NASA-TLX: unweighted mean (RTLX) and weighted score (TLX)
dims = ["MENTAL", "PHYSICAL", "TEMPORAL", "PERFORMANCE", "EFFORT", "FRUSTRATION"]
weights = subjective[[f"WEIGHT {d}" for d in dims]].to_numpy()
subjective["RTLX"] = subjective[dims].mean(axis=1)
subjective["TLX"] = (subjective[dims].to_numpy() * weights).sum(axis=1) / weights.sum(axis=1)

# Annotation of the whole recording (reads all rows, about 30 s)
annotation = pd.read_hdf(path, "SIGNALS", columns=["STUDY", "PHASE", "LEVEL"])
test_3 = annotation.index[
    (annotation["STUDY"] == "n-back") & (annotation["PHASE"] == "test") & (annotation["LEVEL"] == 3)
]

# Selected channels during n-back test 3 (index = time in ms = row number)
signals = pd.read_hdf(
    path,
    "SIGNALS",
    columns=["RAW_ECG_I", "ECG_I_PEAKS", "EYE_LEFT_PUPIL_DIAMETER"],
    start=test_3[0],
    stop=test_3[-1] + 1,
)
pupil = signals["EYE_LEFT_PUPIL_DIAMETER"].dropna()  # native 250 Hz samples

Checksums

Each file's MD5 checksum is shown on the Zenodo record page. The file checksums.md5 in this record lists the same checksums. After downloading, verify all files with:

md5sum -c checksums.md5          # Linux
# macOS: sudo port install coreutils, then gmd5sum -c checksums.md5

Related records and publications

Supplement to the REVELIO paper. Counterpart of the REVELIO Private Set. Continues the ADABase Public and Private Sets.

Publications:

• Oppelt, M. P., Foltyn, A., Deuschel, J., Lang, N. R., Holzer, N., Eskofier, B. M., & Yang, S. H. (2023). ADABase: A Multimodal Dataset for Cognitive Load Estimation. Sensors, 23(1), 340. https://doi.org/10.3390/s23010340
• Oppelt, M. P., Foltyn, A., Lang-Richter, N. R., & Eskofier, B. M. (2026). REVELIO: Multimodal Task Load Estimation for Cross-Domain Generalization. International Journal of Human–Computer Interaction, in press. Preprint: https://doi.org/10.48550/arXiv.2509.01642

License, access and contact

The files of this record are restricted. Researchers with a permanent position at an academic institution can request access with the Request access button on this Zenodo record. Access is free of charge and limited to non-commercial academic research. In the request, state your name, position, affiliation, postal address, email, up to five additional researchers (with affiliation and email), and a short description of the intended research use.

Use of the data is governed by the End User License Agreement (EULA): https://raw.githubusercontent.com/revelio-dataset/revelio-dataset.github.io/main/LICENSE.md. By requesting access, you accept it.

Copyright (C) 2026 The Authors.

Contact: adabase@iis.fraunhofer.de