Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA.
Sci Data. 2024 Nov 20;11(1):1256. doi: 10.1038/s41597-024-04090-6.
This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor Imagery (MI) to control a cursor to follow a randomly moving target, instead of a single stationary target used in other traditional BCI tasks. DL methods have recently achieved promising performance in traditional BCI tasks, but most studies investigate offline data analysis using DL algorithms. This dataset consists of ~168 hours of EEG recordings from complex CP BCI experiments, collected from 28 unique human subjects over multiple sessions each, with an online DL-based decoder. The large amount of subject specific data from multiple sessions may be useful for developing new BCI decoders, especially DL methods that require large amounts of training data. By providing this dataset to the public, we hope to help facilitate the development of new or improved BCI decoding algorithms for the complex CP paradigm for continuous object control, bringing EEG-based BCIs closer to real-world applications.
这个数据集来自一个 EEG 脑机接口 (BCI) 研究，旨在探讨深度学习 (DL) 在在线连续追踪 (CP) BCI 中的应用。在这个任务中，被试者使用运动想象 (MI) 来控制光标追踪一个随机移动的目标，而不是像其他传统 BCI 任务中使用的单个固定目标。DL 方法最近在传统 BCI 任务中取得了有前景的性能，但大多数研究都使用 DL 算法对离线数据进行分析。该数据集由约 168 小时的 EEG 记录组成，这些记录来自 28 位独特的人类被试者在多个会话中的实验，每个会话都使用基于 DL 的在线解码器。来自多个会话的大量特定于主体的数据可能对开发新的 BCI 解码器有用，特别是对需要大量训练数据的 DL 方法。通过向公众提供这个数据集，我们希望有助于促进用于连续对象控制的复杂 CP 范式的新的或改进的 BCI 解码算法的发展，使基于 EEG 的 BCI 更接近实际应用。
