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

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. To cope with these challenges, collaborative BCIs can be used to improve the detection efficiency by
fusing brain activities acquired from multiple subjects. More effective feature
extraction and classification algorithms can also contribute to enhance the detection
accuracy and cross-session performance. To evaluate the performance of data
fusion methods and feature extraction and classification algorithms, datasets
are required. However, there is still a lack of cross-session collaborative
RSVP-based BCI dataset. This paper presents a cross-session EEG dataset of a collaborative RSVP-based BCI system from 14 subjects, who were divided
into 7 groups. All subjects participated in the same experiment twice with an
average interval of ~23 days. The experiment consisted of 3 blocks, each
containing 14 trials. Each trial presented 100 street scene images at 10Hz (10
images per second), including 4 target images with human and 96 non-target
images without human. In the collaborative BCI experiment, two subjects were asked
to watch the same RSVP sequences synchronously and make a key-press immediately
when they detected a target. The results in data evaluation indicate that adequate signal processing algorithms can greatly enhance the cross-session BCI performance in both individual and
collaborative conditions. Besides, compared with individual BCIs, the collaborative
methods that fuse information from multiple subjects obtain significantly
improved BCI performance. This dataset can be used for developing more
efficient algorithms to enhance performance and practicality of a collaborative
RSVP-based BCI system.