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

• Published in: Frontiers in neuroscience Vol. 14; p. 579469
• Main Authors: Zheng, Li, Sun, Sen, Zhao, Hongze, Pei, Weihua, Chen, Hongda, Gao, Xiaorong, Zhang, Lijian, Wang, Yijun
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 22.10.2020; Frontiers Media S.A
• Subjects: Algorithms; Brain; brain-computer interfaces (BCI); Classification; Collaboration; collaborative BCI; cross-session transfer; Data analysis; Datasets; EEG; electroencephalogram (EEG); Electroencephalography; event related potentials (ERP); Event-related potentials; Experiments; Interfaces; Neuroscience; Principal components analysis; rapid serial visual presentation (RSVP); Signal processing
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2020.579469

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 there is still a lack of datasets for a collaborative RSVP-based BCI. This paper presents a cross-session EEG dataset of a collaborative RSVP-based BCI system from 14 subjects, who were divided into 7 groups. In collaborative BCI experiments, two subjects did the same target image detection tasks synchronously. All subjects participated in the same experiment twice with an average interval of ~23 days. 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.