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 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 seven 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.
Highlights
Brain-computer interfaces (BCIs) establish a communication channel between human brain and the external world (Wolpaw et al, 2002; Gao et al, 2014)
Computer vision (CV) has become a major method to deal with the image recognition problem recently, it consumes a large amount of resource to get a good performance, and is still lack of generalization ability
This study presents a cross-session dataset of a collaborative rapid serial visual presentation (RSVP)-based BCI
Summary
Brain-computer interfaces (BCIs) establish a communication channel between human brain and the external world (Wolpaw et al, 2002; Gao et al, 2014). A series of studies have demonstrated collaborative BCIs for target detection and decision making (Wang et al, 2011; Yuan et al, 2012; Matran-Fernandez et al, 2013; Cecotti and Rivet, 2014; Poli et al, 2014; Touyama, 2014; Valeriani et al, 2015, 2016, 2017; Bhattacharyya et al, 2019) For both individual and collaborative RSVP-based BCIs, system calibration remains another big challenge in practical applications. This paper presents a cross-session dataset for collaborative RSVP-based BCIs. The dataset has the following characteristics: (1) EEG data from two subjects were recorded simultaneously with a collaborative BCI where two subjects performed the same target detection tasks synchronously, (2) two separate sessions were recorded for each of seven groups (14 subjects) on two different days with an average interval of ∼23 days, and (3) whole-head 62-channel EEG data were recorded and the raw data were provided without further processing.
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