WBCIC-SHU Motor Imagery Dataset

Brain-computer interface (BCI) is an effective approach for users to control external software applications and devices only by decoding their brain activities and without engaging any muscles. The availability of a BCI dataset which are large-scale and high quality can stimulate the researchers from neighbouring research areas develop advanced deep learning algorithms to enrich the field of BCI. Therefore, it is necessary to build an EEG dataset with availability for the development and research of BCI system. This is a large and high-performance intuitive dataset from World Robot Conference Contest-BCI Robot Contest MI of upper limbs or upper and lower limbs with three recording sessions. Sixty-three healthy participants (all right-handed, aged 17-30, 18 females) were naive BCI users participated in this experiment, of which fifty-two subjects participated in the two-class MI experiment and eleven subjects participated in the three-class MI experiment. This dataset can be utilized for a wide range of BCI-related research questions: analysing the differences among recording sessions of the same subject, improving the decoding performance of algorithms.

脑机接口（Brain-computer Interface, BCI）是一种无需调动肌肉、仅通过解码大脑活动即可操控外部软件应用与设备的有效途径。大规模且高质量的脑机接口数据集，能够吸引邻近研究领域的研究者开发先进深度学习算法，进而丰富脑机接口领域的研究生态。因此，构建可用于脑机接口系统研发的脑电图（Electroencephalogram, EEG）数据集具有重要价值。本数据集源自世界机器人大会赛事——脑机接口机器人竞赛中的上肢或上下肢运动想象（Motor Imagery, MI）赛道，共包含三次数据采集会话，是一套大规模、高性能且直观易用的数据集。本次实验共招募63名健康受试者（均为右利手，年龄17~30岁，其中女性18名），所有受试者均为脑机接口新手用户；其中52名受试者参与了二分类运动想象实验，11名受试者参与了三分类运动想象实验。该数据集可应用于诸多脑机接口相关研究场景，例如分析同一受试者不同采集会话间的信号差异、提升算法的解码性能等。

搜集汇总

背景与挑战

背景概述

WBCIC-SHU Motor Imagery Dataset是一个大规模、高性能的脑机接口数据集，包含63名健康参与者的运动想象实验数据，支持两分类和三分类任务。该数据集可用于脑机接口系统的开发和研究，特别是分析不同记录会话间的差异和提升算法解码性能。