Liu Chang, You Jia, Wang Kun, Zhang Shanshan, Huang Yining, Xu Minpeng, Ming Dong
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
Front Neurosci. 2023 Aug 29;17:1180471. doi: 10.3389/fnins.2023.1180471. eCollection 2023.
In recent years, motor imagery-based brain-computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement.
Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm.
As a result, both the MRCP and ERD features showed the specific temporal-spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively.
This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.
近年来，基于运动想象的脑机接口（MI-BCIs）因其在神经康复方面的巨大潜力而迅速发展。然而，可控指令集限制了其在日常生活中的应用。为了扩展指令集，我们提出了一种基于顺序手指运动的新型运动意图编码范式。
10名受试者参与离线实验。实验过程中，他们被要求在1Hz的听觉提示下，以大约1秒的间隔，用左手或右手食指依次按键[即左→左（LL）、右→右（RR）、左→右（LR）和右→左（RL）]。利用与运动相关的皮层电位（MRCP）和事件相关去同步化（ERD）特征来研究顺序手指运动任务诱发的脑电图（EEG）变化。12名受试者参与在线实验以验证所提出范式的可行性。
结果表明，MRCP和ERD特征均显示出不同顺序手指运动任务的特定时空EEG模式。对于离线实验，四项任务的平均分类准确率为71.69%，最高准确率为79.26%。对于在线实验，LL对RR和LR对RL的平均准确率分别为83.33%和82.71%。
本文通过离线和在线实验证明了所提出的顺序手指运动范式的可行性。本研究将有助于优化与运动相关的EEG信息的编码方法，并为扩展基于运动意图的脑机接口的指令集提供一种有前景的方法。
