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.
State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.
Sensors (Basel). 2021 Feb 10;21(4):1256. 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.
脑-机接口（BCI）通过对脑活动进行编码和解码，为人类提供了一种新的通信渠道。稳态视觉诱发电位（SSVEP）为基础的脑-机接口在众多脑-机接口范式中脱颖而出，因为它具有非侵入性、用户培训少和高信息传输率（ITR）。然而，传统脑电图（EEG）方法中使用导电凝胶和笨重的硬件阻碍了 SSVEP 为基础的脑-机接口的应用。此外，长时间连续的视觉刺激会导致视觉疲劳，这对实际应用构成了新的挑战。本研究提供了一个开放数据集，该数据集是基于可穿戴 SSVEP 为基础的脑-机接口系统收集的，并全面比较了湿电极和干电极获得的 SSVEP 数据。该数据集包含 102 名健康受试者执行 12 目标 SSVEP 为基础的脑-机接口任务的 8 通道 EEG 数据。对于每个受试者，分别使用湿电极和干电极记录了 10 个连续的块。该数据集可用于研究湿电极和干电极在 SSVEP 为基础的脑-机接口中的性能。此外，该数据集为开发新的目标识别算法提供了足够的数据，以提高可穿戴 SSVEP 为基础的脑-机接口的性能。
