Department of Electrical and Electronics, Birla Institute of Technology & Science, Pilani 333031, India.
Graduate School, Duy Tan University, Da Nang 550000, Vietnam.
Sensors (Basel). 2020 Jun 27;20(13):3620. doi: 10.3390/s20133620.
A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.
脑机接口（BCI）是一种使用脑信号来控制外部设备的通信机制。这种信号的产生有时是独立于神经系统的，如在被动 BCI 中。这对于那些患有严重运动障碍的人来说是非常有益的。传统的 BCI 系统仅依赖于使用脑电图（EEG）记录的脑信号，并使用基于规则的翻译算法来生成控制命令。然而，最近使用多传感器数据融合和基于机器学习的翻译算法提高了这些系统的准确性。本文讨论了各种 BCI 应用，如远程呈现、物体抓取、导航等，这些应用使用多传感器融合和机器学习来控制人形机器人执行所需的任务。本文还包括对所讨论应用中使用的方法和系统设计的回顾。
