A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior
A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior
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DOI：
10.1088/1741-2560/5/1/003
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发表时间：
2008-03-01
影响因子：
4
通讯作者：
Hallett, Mark
中科院分区：
文献类型：
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作者：
Bai, Ou;Lin, Peter;Hallett, Mark
使用与人类自然运动行为相关的非侵入性脑电图信号来探索高性能脑机接口的可靠性不需要大量的训练。我们提出了一种新的脑机接口方法，用户执行维持或停止运动任务，并将时间锁定到预定义的时间窗口。9名健康志愿者、1名患有右侧偏瘫的中风幸存者和1名患有肌萎缩侧索硬化症（ALS）的患者参加了这项研究。受试者在参加本研究前未接受脑机接口训练。我们研究了身体运动和运动想象的任务。利用曲面拉普拉斯导数增强脑电空间分辨率。采用无模型阈值设置方法对运动意图进行分类。通过在线顺序二进制光标控制游戏验证了所提BCI在二维光标移动中的性能。当受试者持续或停止运动执行或运动想象时，观察到事件相关的去同步和同步。特征分析表明，感觉运动区脑电β带活动具有最大的区别。通过对单个电极的β波段脑电图活动进行简单的无模型分类（表面拉普拉斯推导），6名健康志愿者的运动执行/运动想象的脑电图活动在线分类为：>90%/相似于80%，>80%/相似于80%，脑卒中患者-90%/相似于80%，ALS患者-90%/相似于80%。其余3名健康志愿者的脑电图活动不可分类。与人类自然运动行为相关的EEG感觉运动节律可用于健康受试者和神经系统疾病患者的可靠和高性能脑机接口。意义：提出的新的无创脑机接口方法突出了临床应用的实用性，其中用户不需要广泛的培训。
To explore the reliability of a high performance brain-computer interface (130) using non-invasive EEG signals associated with human natural motor behavior does not require extensive training. We propose a new BCI method, where users perform either sustaining or stopping a motor task with time locking to a predefined time window. Nine healthy volunteers, one stroke survivor with right-sided hemiparesis and one patient with amyotrophic lateral sclerosis (ALS) participated in this study. Subjects did not receive BCI training before participating in this study. We investigated tasks of both physical movement and motor imagery. The surface Laplacian derivation was used for enhancing EEG spatial resolution. A model-free threshold setting method was used for the classification of motor intentions. The performance of the proposed BCI was validated by an online sequential binary-cursor-control game for two-dimensional cursor movement. Event-related desynchronization and synchronization were observed when subjects sustained or stopped either motor execution or motor imagery. Feature analysis showed that EEG beta band activity over sensorimotor area provided the largest discrimination. With simple model-free classification of beta band EEG activity from a single electrode (with surface Laplacian derivation), the online classifications of the EEG activity with motor execution/motor imagery were: >90%/similar to 80% for six healthy volunteers, >80%/similar to 80% for the stroke patient and -90%/similar to 80% for the ALS patient. The EEG activities of the other three healthy volunteers were not classifiable. The sensorimotor beta rhythm of EEG associated with human natural motor behavior can be used for a reliable and high performance BCI for both healthy subjects and patients with neurological disorders. Significance: The proposed new non-invasive BCI method highlights a practical 130 for clinical applications, where the user does not require extensive training.