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

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 4; p. 1256
• Main Authors: Zhu, Fangkun, Jiang, Lu, Dong, Guoya, Gao, Xiaorong, Wang, Yijun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.02.2021; MDPI
• Subjects: Algorithms; Brain research; brain-computer interface (BCI); Brain-Computer Interfaces; Datasets; dry electrode; Electrodes; electroencephalogram (EEG); Electroencephalography; Evoked Potentials, Visual; Experiments; Fatigue; Human-computer interface; Humans; Laboratories; open dataset; Performance enhancement; Photic Stimulation; Questionnaires; steady-state visual evoked potential (SSVEP); Target recognition; User experience; User training; wearable BCI; Wearable Electronic Devices; Wearable technology; Florida; United States > US; open dataset; wearable BCI; dry electrode; brain-computer interface (BCI); electroencephalogram (EEG); steady-state visual evoked potential (SSVEP)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21041256

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.