# BETA: A Large Benchmark Database Toward SSVEP-BCI Application

arXiv: https://arxiv.org/abs/1911.13045 · HTML: https://mlsanity.com/paper/1911.13045/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2019.

## Built on by (most cited first, 15 of 15)

- [A Transformer-based deep neural network model for SSVEP classification](https://mlsanity.com/paper/2210.04172/) (2022)
- [OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models](https://mlsanity.com/paper/2606.00815/) (2026)
- [A Survey of Few-Shot Learning for Biomedical Time Series](https://mlsanity.com/paper/2405.02485/) (2024)
- [Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training](https://mlsanity.com/paper/2209.01511/) (2022)
- [FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces](https://mlsanity.com/paper/2109.02165/) (2021)
- [Estimation of Correlation Matrices from Limited time series Data using Machine Learning](https://mlsanity.com/paper/2209.01198/) (2022)
- [Information Transfer Rate in BCIs: Towards Tightly Integrated Symbiosis](https://mlsanity.com/paper/2301.00488/) (2023)
- [Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces](https://mlsanity.com/paper/2305.17403/) (2023)
- [iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System](https://mlsanity.com/paper/2410.12267/) (2024)
- [Short-length SSVEP data extension by a novel generative adversarial networks based framework](https://mlsanity.com/paper/2301.05599/) (2023)
- [Minimally Informed Linear Discriminant Analysis: training an LDA model with unlabelled data](https://mlsanity.com/paper/2310.11110/) (2023)
- [ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals](https://mlsanity.com/paper/2409.07326/) (2024)
- [Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring](https://mlsanity.com/paper/2506.13909/) (2025)
- [Rethinking Self-Training Based Cross-Subject Domain Adaptation for SSVEP Classification](https://mlsanity.com/paper/2601.21203/) (2026)
- [HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning](https://mlsanity.com/paper/2610.16589/) (2026)

## Abstract (arXiv)

Brain-computer interface (BCI) provides an alternative means to communicate and it has sparked growing interest in the past two decades. Specifically, for Steady-State Visual Evoked Potential based BCI, marked improvement has been made in the frequency recognition method and data sharing. However, the number of pubic database is still limited in this field. Therefore, we present a BEnchmark database Towards BCI Application (BETA) in the study. The BETA database is composed of 64-channel Electroencephalogram (EEG) data from 70 subjects performing a 40-target cued-spelling task. The design and acquisition of BETA is in pursuit of meeting the demand from real-world applications and it can be used as a test-bed for these scenarios. We validate the database by a series of analysis and conduct the classification analysis of eleven frequency recognition methods on BETA. We recommend to use the metric of wide-band SNR and BCI quotient to characterize the SSVEP at the single-trial and population level, respectively. The BETA database can be downloaded from the website http://bci.med.tsinghua.edu.cn/download.html.