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.<br>

脑机接口（brain-computer interfaces, BCIs）通过对脑活动进行编码与解码，为人类提供了一条全新的通信渠道。基于稳态视觉诱发电位（steady-state visual evoked potential, SSVEP）的脑机接口在众多BCI范式中脱颖而出，因其具备无创性、用户训练成本低以及信息传输速率（information transfer rate, ITR）高等优势。
然而，传统脑电图（electroencephalogram, EEG）采集方法中所使用的导电凝胶与笨重硬件，阻碍了基于SSVEP的BCI的实际应用。此外，长期使用过程中的持续视觉刺激会引发视觉疲劳，为该技术的实用化应用带来了新的挑战。
本研究发布了一套由可穿戴式SSVEP-BCI系统采集的开放数据集，该系统针对干湿电极开展了全面对比，并完成了多会话连续记录。该数据集涵盖102名健康受试者的8通道SSVEP数据，受试者在实验中使用基于12目标的SSVEP-BCI系统完成了线索引导式目标选择任务。
针对每名受试者，分别采用湿电极与干电极各完成10个连续实验区块的记录，整体实验时长约为两小时。
该数据集可用于评估干湿电极在基于SSVEP的BCI中的应用性能，同时也能为开发新型目标识别算法以提升可穿戴式SSVEP-BCI的整体性能提供充足的数据支撑。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于可穿戴SSVEP脑机接口研究的开放数据集，包含102名受试者的8通道脑电数据，特别比较了湿电极和干电极在12目标SSVEP任务中的性能。数据集支持电极性能评估和新算法的开发，适用于脑机接口和神经工程领域的研究。