Electroencephalogram (EEG), as a principal signal in detecting brain activities, assumes a dominant position in the current research for the anesthetic depth monitoring.
脑电图作为检测大脑皮层活动的最主要信号，在目前麻醉深度监测研究中处于主导地位。
This platform applies the patients motor imagery electroencephalogram (EEG) as the control signal in order to combine motor imagery with recovery training of motion function.
该平台采用患者在想象运动时的脑电信号作为虚拟人运动的控制信号，从而把想象运动与运动功能恢复训练结合在一起。
To develop effective learning algorithms for fast and accurate continuous prediction using Electroencephalogram (EEG) signal is a key issue in BrainComputer Interface (BCI).
设计有效的学习算法快速准确地对脑电信号进行连续预测是脑机接口研究的关键之一。
To develop effective learning algorithms for fast and accurate continuous prediction using Electroencephalogram (EEG) signal is a key issue in BrainComputer Interface (BCI).
设计有效的学习算法快速准确地对脑电信号进行连续预测是脑机接口研究的关键之一。
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