BETA: A Large Benchmark Database Toward SSVEP-BCI Application

Bingchuan Liu; Xiaoshan Huang; Yijun Wang; Xiaogang Chen; Xiaorong Gao

doi:10.3389/fnins.2020.00627

BETA: A Large Benchmark Database Toward SSVEP-BCI Application

Front Neurosci. 2020 Jun 23:14:627. doi: 10.3389/fnins.2020.00627. eCollection 2020.

Authors

Bingchuan Liu¹, Xiaoshan Huang¹, Yijun Wang², Xiaogang Chen³, Xiaorong Gao¹

Affiliations

¹ Department of Biomedical Engineering, Tsinghua University, Beijing, China.
² State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China.
³ Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China.

Abstract

The 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 (SSVEP) based BCI, marked improvement has been made in the frequency recognition method and data sharing. However, the number of pubic databases 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 of 70 subjects performing a 40-target cued-spelling task. The design and the acquisition of the BETA are 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 analyses and conduct the classification analysis of eleven frequency recognition methods on BETA. We recommend using the metric of wide-band signal-to-noise ratio (SNR) and BCI quotient to characterize the SSVEP at the single-trial and population levels, respectively. The BETA database can be downloaded from the following link http://bci.med.tsinghua.edu.cn/download.html.

Keywords: brain-computer interface (BCI); classification algorithms; electroencephalogram (EEG); frequency recognition; public database; signal-to-noise ratio (SNR); steady-state visual evoked potential (SSVEP).