Brain-computer interfaces (BCIs) use brain signals such as electroencephalography to reflect user intention and enable two-way communication between computers and users. BCI technology has recently received much attention in healthcare applications, such as neurorehabilitation and diagnosis. BCI applications can also control external devices using only brain activity, which can help people with physical or mental disabilities, especially those suffering from neurological and neuromuscular diseases such as stroke and amyotrophic lateral sclerosis. Motor imagery (MI) has been widely used for BCI-based device control, but we adopted intuitive visual motion imagery to overcome the weakness of MI. In this study, we developed a three-dimensional (3D) BCI training platform to induce users to imagine upper-limb movements used in real-life activities (picking up a cell phone, pouring water, opening a door, and eating food). We collected intuitive visual motion imagery data and proposed a deep learning network based on functional connectivity as a mind-reading technique. As a result, the proposed network recorded a high classification performance on average (71.05%). Furthermore, we applied the leave-one-subject-out approach to confirm the possibility of improvements in subject-independent classification performance. This study will contribute to the development of BCI-based healthcare applications for rehabilitation, such as robotic arms and wheelchairs, or assist daily life.

翻译：脑机接口（BCI）利用脑电图等脑信号反映用户意图，实现计算机与用户间的双向通信。近年来，BCI技术在神经康复、疾病诊断等医疗健康领域受到广泛关注。BCI应用还能仅通过脑活动控制外部设备，为肢体或精神障碍患者（特别是中风、肌萎缩侧索硬化症等神经与神经肌肉疾病患者）提供帮助。运动想象（MI）已被广泛用于基于BCI的设备控制，但本研究采用更直观的视觉运动想象以克服MI的局限性。我们开发了一个三维（3D）BCI训练平台，诱导用户想象日常生活中上肢动作（如接听手机、倒水、开门、进食）。通过采集直观的视觉运动想象数据，提出基于功能连接的深度学习网络作为读心技术。实验结果显示，该网络平均分类性能达到71.05%。此外，我们采用留一被试交叉验证方法，验证了提升跨被试分类性能的可能性。本研究将促进基于BCI的康复医疗应用开发（如机械臂、轮椅等）及日常生活辅助。