Supporting data for "Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions"

Non-invasive brain-computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper-extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography (EEG), 7-channel electromyography (EMG), and 4-channel electrooculography (EOG) of 25 healthy subjects collected over 3-day sessions for a total of 82,500 trials across all the subjects. We validated our dataset via neuro physiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery (MI), respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method. The dataset includes the data of multiple recording sessions, various classes within the single upper-extremity, and multimodal signals. This work can be used to i) compare the brain activities associated with real-movement and imagination, ii) improve the decoding performance, and iii) analyze the differences among recording sessions. Hence, this study, as a data note, has focused on collecting data required for further advances in the BCI technology.

非侵入式脑机接口（Non-invasive Brain-Computer Interfaces, BCIs）已被开发用于实现用户与外部机器人系统之间的自然双向交互。然而，通过人工匹配实现用户与脑机接口系统的通信仍是一项关键难题。近年来，脑机接口已转向采用直观解码技术——这正是解决类别数量有限、手动匹配脑机接口指令与设备控制等诸多问题的核心所在。遗憾的是，由于缺乏大规模且标准化的数据集，该领域的进展始终较为缓慢。本研究构建了一项针对11种不同上肢运动任务的大规模直观数据集，数据采集于多轮记录会话。该数据集包含25名健康受试者在为期3天的记录会话中采集的60通道脑电图（electroencephalography, EEG）、7通道肌电图（electromyography, EMG）以及4通道眼电图（electrooculography, EOG）信号，所有受试者累计共完成82500次试次。我们通过神经生理学分析对该数据集进行了验证：观察到与真实运动及运动想象（Motor Imagery, MI）分别相关的清晰感觉运动去激活/激活模式及其空间分布。此外，我们通过使用基线机器学习方法对每一轮记录会话的分类性能进行评估，证实了该数据集的一致性。该数据集涵盖多轮记录会话的数据、单任务内的多种上肢运动类别以及多模态信号。本研究可用于：i）对比与真实运动及运动想象相关的脑活动；ii）提升解码性能；iii）分析不同记录会话之间的差异。因此，作为一份数据笔记，本研究聚焦于采集脑机接口技术进一步发展所需的相关数据。