Zhu F(1), Jiang L(2)(3), Dong G(1), Gao X(4), Wang Y(2)(3). Author information:
(1)State Key Laboratory of Reliability and Intelligence of Electrical Equipment,
Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health,
Hebei University of Technology, Tianjin 300132, China.
(2)State Key Laboratory on Integrated Optoelectronics, Institute of
Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.
(3)University of Chinese Academy of Sciences, Beijing 100049, China.
(4)School of Medicine, Tsinghua University, Beijing 100084, China.
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 challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.
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