Brain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheel-chairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 different motor-imagery-based BCI tasks. The current dataset presents one of the largest and most complex SMR-BCI datasets publicly available to date and should be useful for the development of improved algorithms for BCI control.

脑机接口 (BCI) 是一种有价值的工具，可以绕过传统的神经肌肉通路来扩展交流的本质。基于感觉运动节律 (SMR) 的 BCI 的非侵入性、直观性和连续性使个人能够通过解码脑电图 (EEG) 的运动想象来控制计算机、机械臂、轮椅，甚至无人机。需要大型且统一的数据集来设计、评估和改进 BCI 算法。在这项工作中，我们发布了一个大型纵向数据集，该数据集是在一项研究中收集的，该研究调查了个人如何学习控制 SMR-BCI。该数据集包含 600 多个小时的脑电图记录，这些记录是在在线和连续 BCI 控制期间从 62 名健康成人（（大部分）右手主导参与者）收集的，每个参与者（最多）11 个培训课程。数据记录包括 598 个记录会话，以及 4 种不同的基于运动想象的 BCI 任务的超过 250,000 次试验。当前的数据集是迄今为止公开可用的最大和最复杂的 SMR-BCI 数据集之一，应该有助于开发改进的 BCI 控制算法。