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

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 image...