Abstract
An open electroencephalography (EEG) dataset is presented for evaluating consumer-grade EEG devices in comparison with a research-grade EEG device. Datasets were collected from 30 participants using four consumer-grade EEG devices (BrainLink Pro, NeuroNicle FX2, MindWave Mobile 2, Muse 2), including two single-channel headsets (BrainLink Pro and MindWave Mobile 2), and one research-grade EEG device (DSI-24). Each participant completed four experimental paradigms: eye blinks, jaw clenching, head movements with eyes open, and head movements with eyes closed, with the resting-state EEG data recorded before and after each task. The dataset enables the validation of device signal quality, the assessment of neural features such as alpha-band activity, and the examination of robustness to movement-induced artifacts. The dataset is publicly available on Figshare.
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Code availability
The code for basic data visualization and spectral analysis is available alongside the data in the same public repository. These codes allow users to reproduce figures similar to those presented in this article.
Data availability
The dataset supporting this article is available at figshare: https://doi.org/10.6084/m9.figshare.30162868.
References
Grummett, T. S. et al. Measurement of neural signals from inexpensive, wireless and dry EEG systems. Physiol Meas 36, 1469–1484 (2015).
Dadebayev, D., Goh, W. W. & Tan, E. X. EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques. Journal of King Saud University - Computer and Information Sciences 34, 4385–4401 (2022).
Anthony, J. R. A Comparison of Electroencephalography Signals Acquired from Conventional and Mobile Systems. Journal of Neuroscience and Neuroengineering 3, 10–20 (2014).
Radüntz, T. Signal quality evaluation of emerging EEG devices. Front. Physiol. 9, 98 (2018).
Liu, D. et al. A study on quality assessment of the surface EEG signal based on fuzzy comprehensive evaluation method. Computer Assisted Surgery 24, 167–173 (2019).
Sawangjai, P., Hompoonsup, S., Leelaarporn, P., Kongwudhikunakorn, S. & Wilaiprasitporn, T. Consumer grade EEG measuring sensors as research tools: a review. IEEE Sensors Journal 20, 3996–4024 (2020).
Maskeliunas, R., Damasevicius, R., Martisius, I. & Vasiljevas, M. Consumer-grade EEG devices: are they usable for control tasks? PeerJ 4, e1746 (2016).
David Hairston, W. et al. Usability of four commercially-oriented EEG systems. J. Neural Eng. 11, 046018 (2014).
Zhu, F., Jiang, L., Dong, G., Gao, X. & Wang, Y. An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces. Sensors 21, 1256 (2021).
Zhang, H. et al. EEGdenoiseNet: A benchmark dataset for deep learning solutions of EEG denoising. J. Neural Eng. 18, 056057 (2021).
Brooke, J. SUS: A quick and dirty usability scale. in Usability Evaluation in Industry (eds. Jordan, P. W., Thomas, B., Weerdmeester, B. A. & McClelland, I. L.) 189–194 (Taylor & Francis, 1996).
Lee, Y. & Ahn, M. EEG dataset of consumer- and research-grade systems for device evaluation. figshare https://doi.org/10.6084/m9.figshare.30162868 (2025).
Acknowledgements
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW) supervised by the IITP(Institute of Information & Communications Technology Planning & Evaluation) in 2025 (2023-0-00055), the Korea Institute of Oriental Medicine grant (No. KSN2324022), the National Research Council of Science and Technology (NST) Aging Convergence Research Center (CRC22015-500) and also by the National Research Foundation of Korea (NRF) grant (No.2021R1I1A3060828, No.RS-2025-25412061).
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Data acquisition: Y.L., D.G., K.K., S.P., S.S., M.U.C. Data curation: Y.L., D.G., K.K., S.P., S.S., M.U.C. Validation: Y.L., D.G., M.A. Formal analysis: Y.L. Investigation: Y.L., D.G., M.A. Visualization: Y.L., D.G., M.A. Writing: Y.L., D.G., M.A.
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Lee, Y., Gwon, D., Kim, K. et al. EEG dataset of consumer- and research-grade systems. Sci Data (2026). https://doi.org/10.1038/s41597-026-06962-5
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DOI: https://doi.org/10.1038/s41597-026-06962-5
