A large dataset for VEP based brain-computer interfaces employing narrow-band code modulation and frequency-phase modulation

Objective Brain-computer interfaces (BCIs) realize the information transmission between the brain and the external world. Visual evoked potential (VEP) based BCIs have gained widespread attention in the field of multi-instruction interaction attributed to their high information transfer rate (ITR)....

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Bibliographic Details
Published in:Brain-apparatus communication 2024-12, Vol.3 (1)
Main Authors: Zheng, Li, Tian, Sen, Dong, Yida, Pei, Weihua, Gao, Xiaorong, Wang, Yijun
Format: Article
Language:English
Subjects:
Brain-computer interface
code-modulated visual evoked potential
large dataset
narrow-band random sequences
steady-state visual evoked potential
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Summary:Objective Brain-computer interfaces (BCIs) realize the information transmission between the brain and the external world. Visual evoked potential (VEP) based BCIs have gained widespread attention in the field of multi-instruction interaction attributed to their high information transfer rate (ITR). However, improving the IT R and the practicability is challenging for existing VEP based BCIs due to factors such as the encoding efficiency and the calibration time. To address this issue, this study proposed a new encoding method employing narrow-band random sequences and provided a large dataset for VEP based brain-computer interfaces.Methods Narrow-band random sequences are random sequences with a specific frequency band. The dataset encompasses three paradigms that employ three kinds of encoding sequences: narrow-band sequences with a frequency band of 15 ∼ 25 Hz (NBRS-15), narrow-band random sequences with a frequency band of 8 ∼ 16 Hz (NBRS-8), and sequences utilizing joint frequency-phase modulation method with a frequency range of 8–15.8 Hz (JFP M-8).Results The dataset includes 59-channel electroencephalogram (EEG) data for 100 subjects, and the quality of the dataset is validated through quantitative analyses on EEG characteristics and classification performance.Conclusion The proposed large dataset includes various paradigms, and the data quality has been validated, which can contribute to the development of VEP based BCIs. The dataset is available from https://doi.org/10.6084/m9.figshare.24864243.
ISSN:2770-6710
2770-6710
DOI:10.1080/27706710.2024.2383860