Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
Comput Biol Med. 2019 Jul;110:254-264. doi: 10.1016/j.compbiomed.2019.05.024. Epub 2019 Jun 1.
Noninvasive brain-computer interfaces (BCI) for movement control via an electroencephalogram (EEG) have been extensively investigated. However, most previous studies decoded user intention for movement directions based on sensorimotor rhythms during motor imagery. BCI systems based on mapping imagery movement of body parts (e.g., left or right hands) to movement directions (left or right directional movement of a machine or cursor) are less intuitive and less convenient due to the complex training procedures. Thus, direct decoding methods for detecting user intention about movement directions are urgently needed.
Here, we describe a novel direct decoding method for user intention about the movement directions using the echo state network and Gaussian readouts. Importantly parameters in the network were optimized using the genetic algorithm method to achieve better decoding performance. We tested the decoding performance of this method with four healthy subjects and an inexpensive wireless EEG system containing 14 channels and then compared the performance outcome with that of a conventional machine learning method.
We showed that this decoding method successfully classified eight directions of intended movement (approximately 95% of an accuracy).
We suggest that the echo state network and Gaussian readouts can be a useful decoding method to directly read user intention of movement directions even using an inexpensive and portable EEG system.
基于脑电图（EEG）的非侵入式脑-机接口（BCI）已经被广泛研究，用于运动控制。然而，大多数先前的研究都是基于运动想象期间的感觉运动节律来解码用户的运动方向意图。基于将身体部位的想象运动（例如，左手或右手）映射到运动方向（机器或光标向左或向右的方向运动）的 BCI 系统，由于复杂的训练过程，不太直观且不太方便。因此，迫切需要直接解码方法来检测用户对运动方向的意图。
在这里，我们描述了一种使用回声状态网络和高斯读取的用于检测运动方向用户意图的新的直接解码方法。重要的是，使用遗传算法方法优化了网络中的参数，以达到更好的解码性能。我们使用四名健康受试者和一个包含 14 个通道的廉价无线 EEG 系统测试了该方法的解码性能，然后将性能结果与传统的机器学习方法进行了比较。
我们表明，这种解码方法可以成功地对八个预期运动方向进行分类（准确率约为 95%）。
我们建议，回声状态网络和高斯读取可以作为一种有用的解码方法，即使使用廉价且便携的 EEG 系统，也可以直接读取用户的运动方向意图。
