A Benchmark Dataset for RSVP-Based Brain–Computer Interfaces

• Published in: Frontiers in neuroscience Vol. 14; p. 568000
• Main Authors: Zhang, Shangen, Wang, Yijun, Zhang, Lijian, Gao, Xiaorong
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 02.10.2020; Frontiers Media S.A
• Subjects: Algorithms; Artificial intelligence; Brain; brain–computer interface; Computer applications; Datasets; EEG; Electrodes; electroencephalogram; Electroencephalography; event-related potential; Event-related potentials; Experiments; Implants; Interfaces; Machine learning; Neuroscience; public dataset; rapid serial visual presentation; Signal processing; target detection; Visual evoked potentials
• DOI: 10.3389/fnins.2020.568000
• ISSN: 1662-453X; 1662-4548

This paper reports a benchmark dataset acquired with a brain-computer interface (BCI) system based on the rapid serial visual presentation (RSVP) paradigm. The dataset consists of 64-channel Electroencephalogram (EEG) data from 64 healthy subjects (sub1, …, sub64) while they performed a target images detection task. For each subject, the data contained 2 groups (‘A’ and ‘B’). Each group contained 2 blocks and each block included 40 trials which corresponded to 40 stimuli sequences. Each sequence contained 100 images presented at 10 Hz (10 images per second). The stimuli images were street view images of two categories: target images with human and non-target images without human. Target images were presented randomly in the stimulus sequence with a probability of 1~4%. During the stimulus presentation, subjects were asked to search for the target images and ignore the non-target images in a subjective manner. To keep all original information, the dataset was the raw continuous data without any processing. On one hand, the dataset can be used as a benchmark dataset to compare the algorithms for target identification in RSVP-based BCIs. On the other hand, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data through offline simulation. Furthermore, the dataset also provides high-quality data for characterizing and modeling event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEPs) in RSVP-based BCIs. The dataset is freely available from http://bci.med.tsinghua.edu.cn/download.html.