Author(s): Liu, BC (Liu, Bingchuan); Wang, YJ (Wang, Yijun); Gao, XR (Gao, Xiaorong); Chen, XG (Chen, Xiaogang)

Source: SCIENTIFIC DATA Volume: 9 Issue: 1 Article Number: 252 DOI: 10.1038/s41597-022-01372-9 Published: MAY 31 2022

Abstract: Global population aging poses an unprecedented challenge and calls for a rising effort in eldercare and healthcare. Steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) boasts its high transfer rate and shows great promise in real-world applications to support aging. Public database is critically important for designing the SSVEP-BCI systems. However, the SSVEP-BCI database tailored for the elder is scarce in existing studies. Therefore, in this study, we present a large eldercare-oriented BEnchmark database of SSVEP-BCI for The Aging population (eldBETA). The eldBETA database consisted of the 64-channel electroencephalogram (EEG) from 100 elder participants, each of whom performed seven blocks of 9-target SSVEP-BCI task. The quality and characteristics of the eldBETA database were validated by a series of analyses followed by a classification analysis of thirteen frequency recognition methods. We expect that the eldBETA database would provide a substrate for the design and optimization of the BCI systems intended for the elders.

Accession Number: WOS:000803898700001

PubMed ID: 35641547

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Liu, Bingchuan                  0000-0001-5988-6051

eISSN: 2052-4463

Full Text: https://www.nature.com/articles/s41597-022-01372-9