An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces

The
brain-computer interfaces (BCIs) provide humans a new communication channel by
encoding and decoding brain activities. Steady-state visual evoked potential
(SSVEP)-based BCI stands out among many BCI paradigms because of its
non-invasiveness, little user training, and high information transfer rate
(ITR). However, the use of conductive gel and bulky hardware in the traditional
Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs.
Besides, continuous visual stimulation in long time use will lead to visual
fatigue and pose a new challenges to the practical application. This study
presents an open dataset collected with a wearable SSVEP-based BCI system that
compared wet and dry electrodes comprehensively with continuous recording of
multiple sessions. The dataset consists of 8-channel SSVEP data from 102
healthy subjects while they performed a cue-guided target selecting task with a
12-target SSVEP-based BCI. For each subject, wet and dry electrodes were used
to record 10 consecutive blocks respectively in an overall duration of around
two hours. The dataset can be used to evaluate the performance of wet and dry
electrodes in SSVEP-based BCIs. The dataset also provide sufficient data for
developing new target identification algorithms to improve the performance of
wearable SSVEP-based BCIs.