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A Benchmark Dataset for RSVP-Based Brain-Computer Interfaces

2020-11-05

Author(s): Zhang, SE (Zhang, Shangen); Wang, YJ (Wang, Yijun); Zhang, LJ (Zhang, Lijian); Gao, XR (Gao, Xiaorong)

Source: FRONTIERS IN NEUROSCIENCE Volume: 14 Article Number: 568000 DOI: 10.3389/fnins.2020.568000 Published: OCT 2 2020

Abstract: This paper reports on 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, horizontal ellipsis , sub64) while they performed a target image detection task. For each subject, the data contained two groups ("A" and "B"). Each group contained two blocks, and each block included 40 trials that corresponded to 40 stimulus sequences. Each sequence contained 100 images presented at 10 Hz (10 images per second). The stimulus 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 similar to 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.

Accession Number: WOS:000579314800001

PubMed ID: 33122990

eISSN: 1662-453X

Full Text: https://www.frontiersin.org/articles/10.3389/fnins.2020.568000/full



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