A Benchmark Dataset for SSVEP-Based Brain–Computer Interfaces

By Xiaogang Chen, Xiaorong Gao, Shangkai Gao. IEEE transactions on neural systems and rehabilitation engineering, 2017

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This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain– computer interface (BCI) speller. The dataset consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed a cue-guided target selecting task. The virtual keyboard of the speller was composed of 40 visual flickers, which were coded using a joint frequency and phase modulation (JFPM) approach. The stimulation frequencies ranged from 8 Hz to 15.8 Hz with an interval of 0.2 Hz. The phase difference between two adjacent frequencies was $0.5\pi $ . For each subject, the data included six blocks of 40 trials corresponding to all 40 flickers indicated by a visual cue in a random order. The stimulation duration in each trial was five seconds. The dataset can be used as a benchmark dataset to compare the methods for stimulus coding and target identification in SSVEP-based BCIs. Through offline simulation, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data. The dataset also provides high-quality data for computational modeling of SSVEPs. The dataset is freely available from http://bci.med.tsinghua.edu.cn/download.html.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/baa175c7c7d56057191866b6fe837a967c140d0e

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