WBCIC-SHU Motor Imagery Dataset

<br>Brain-computer interfaces (BCIs) provide an effective means for users to control external software applications and devices solely by decoding their brain activity, without the need for muscle engagement. A large-scale, high-quality BCI dataset can stimulate researchers from related fields to develop advanced deep learning algorithms, thereby enriching the BCI domain. Therefore, creating an EEG dataset that supports the development and research of BCI systems is crucial. This dataset, derived from the World Robot Conference Contest-BCI Robot Contest MI, focuses on upper-limb or upper-and-lower-limb motor imagery (MI) tasks across three recording sessions. Sixty-two healthy, right-handed participants (ages 17–30, 18 females) with no prior BCI experience took part in this experiment. Of these, 52 subjects completed the two-class MI experiment, while 11 subjects participated in the three-class MI experiment. This dataset offers significant potential for a wide range of BCI-related research, including the analysis of inter-session variability for individual subjects and enhancing decoding algorithm performance.If the download of this version is slow, you can choose to download Version 3 in batches.<br>

脑机接口（Brain-computer interfaces, BCIs）为用户提供了一种无需肌肉参与、仅通过解码大脑活动即可控制外部软件应用与设备的有效途径。大规模高质量的脑机接口数据集能够推动相关领域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内涵。因此，构建一套可支撑脑机接口系统研发与相关研究的脑电图（Electroencephalogram, EEG）数据集具有重要意义。本数据集源自世界机器人大会竞赛-脑机接口机器人竞赛运动想象赛项，涵盖三次采集会话中的上肢或上下肢运动想象（Motor Imagery, MI）任务。本次实验共有62名健康的右利手受试者参与，年龄区间为17至30岁，其中女性18名，所有受试者均无脑机接口相关实验经验。其中52名受试者完成了二分类运动想象实验，另有11名受试者参与了三分类运动想象实验。本数据集可广泛应用于各类脑机接口相关研究，例如分析个体受试者的会话间变异性、提升解码算法的性能表现。若当前版本下载速度较慢，您可选择分批次下载版本3。