An open dataset for human SSVEPs in the frequency range of 1-60 Hz

A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system relies on the photic driving response to effectively elicit characteristic electroencephalogram (EEG) signals. However, traditional visual stimuli mainly adopt high-contrast black-and-white flickering stimulat...

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Bibliographic Details
Published in:Scientific data 2024-02, Vol.11 (1), p.196-196, Article 196
Main Authors: Gu, Meng, Pei, Weihua, Gao, Xiaorong, Wang, Yijun
Format: Article
Language:English
Subjects:
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Algorithms
Brain
Brain research
Brain-Computer Interfaces
Computer applications
Data analysis
Data Descriptor
Datasets
EEG
Electroencephalography
Evoked Potentials, Visual
Healthy Volunteers
Humanities and Social Sciences
Humans
Implants
LCDs
Light emitting diodes
Liquid crystal displays
multidisciplinary
Paradigms
Photic Stimulation
Science
Science (multidisciplinary)
User experience
Visual evoked potentials
Visual stimuli
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Description
Summary:A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system relies on the photic driving response to effectively elicit characteristic electroencephalogram (EEG) signals. However, traditional visual stimuli mainly adopt high-contrast black-and-white flickering stimulations, which are easy to cause visual fatigue. This paper presents an SSVEP dataset acquired at a wide frequency range from 1 to 60 Hz with an interval of 1 Hz using flickering stimuli under two different modulation depths. This dataset contains 64-channel EEG data from 30 healthy subjects when they fixated on a single flickering stimulus. The stimulus was rendered on an LCD display with a refresh rate of 240 Hz. Initially, the dataset was rigorously validated through comprehensive data analysis to investigate SSVEP responses and user experiences. Subsequently, BCI performance was evaluated through offline simulations of frequency-coded and phase-coded BCI paradigms. This dataset provides comprehensive and high-quality data for studying and developing SSVEP-based BCI systems.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-03023-7