BETA: A Large Benchmark Database Toward SSVEP-BCI Application

TL;DR

The paper introduces BETA, a comprehensive EEG database with 70 subjects for SSVEP-BCI research, facilitating real-world application development and benchmarking of frequency recognition methods.

Contribution

It provides a large, publicly available EEG database specifically designed for SSVEP-BCI applications, addressing the scarcity of such datasets.

Findings

Validated the database through analysis and classification experiments

Compared eleven frequency recognition methods on BETA

Recommended metrics for SSVEP characterization

Abstract

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 \textbf{BE}nchmark database \textbf{T}owards BCI \textbf{A}pplication (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.