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A 40-Class SSVEP Speller Dataset: Beta Range Stimulation for Low-Fatigue BCI Applications
Heegyu Kim1, Kyungho Won2, Minkyu Ahn3
1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.
Scientific Data
|November 5, 2025
Summary
This study introduces a new electroencephalography (EEG) dataset for brain-computer interface (BCI) research. Beta-frequency visual stimulation effectively minimizes visual fatigue, enhancing BCI performance and data consistency.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Electroencephalography (EEG) signal non-stationarity requires large, consistent datasets for reliable brain-computer interface (BCI) research.
- Visual fatigue from prolonged steady-state visual evoked potential (SSVEP) stimulation alters EEG patterns, degrading BCI performance.
Purpose of the Study:
- To mitigate fatigue-induced variability in SSVEP-based BCI research.
- To present a comprehensive 40-class SSVEP speller dataset using beta-frequency (14-22 Hz) visual stimulation.
- To validate the effectiveness of beta-frequency stimulation in reducing visual fatigue.
Main Methods:
- Acquired a 40-class SSVEP speller dataset from 40 participants using 31 central-to-occipital EEG channels.
- Subjects completed six sessions of the SSVEP speller task with pre- and post-experiment resting-state recordings (eyes-open/closed).
- Utilized subjective fatigue ratings and EEG band power analyses to assess fatigue effects.
Main Results:
- Beta-frequency (14-22 Hz) visual stimulation significantly minimized subjective and objective measures of visual fatigue.
- EEG band power analysis confirmed reduced fatigue effects with beta-range stimulation.
- High classification accuracy was achieved using calibration-based algorithms, demonstrating dataset suitability.
Conclusions:
- Beta-frequency stimulation is a viable strategy to reduce visual fatigue in SSVEP BCI paradigms.
- The presented dataset is robust and well-suited for training and evaluating advanced SSVEP-based BCI systems.
- This research contributes to developing more reliable and high-performance BCIs by addressing fatigue-related challenges.
