A Cross-Session Dataset for Collaborative Brain-Computer Interfaces Based on Rapid Serial Visual Presentation.

ABSTRACT: Brain-computer interfaces (BCIs) based on rapid serial visual presentation (RSVP) have been widely used to categorize target and non-target images. However, it is still a challenge to detect single-trial event related potentials (ERPs) from electroencephalography (EEG) signals. Besides, the variability of EEG signal over time may cause difficulties of calibration in long-term system use. Recently, collaborative BCIs have been proposed to improve the overall BCI performance by fusing brain activities acquired from multiple subjects. For both individual and collaborative BCIs, feature extraction and classification algorithms that can be transferred across sessions can significantly facilitate system calibration. Although open datasets are highly efficient for developing algorithms, currently

SUBMITTER: Zheng L 

PROVIDER: S-EPMC7642747 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature