Robinson Neethu, Guan Cuntai, Vinod A P
School of Computer Engineering, Nanyang Technological University, Singapore.
J Neural Eng. 2015 Dec;12(6):066019. doi: 10.1088/1741-2560/12/6/066019. Epub 2015 Oct 26.
The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings.
EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables.
The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p < 0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational time.
The proposed system provides a real time movement control system using EEG-BCI with control over movement speed and position. These results are higher and statistically significant compared to existing techniques in EEG based systems and thus promise the applicability of the proposed method for efficient estimation of movement parameters and for continuous motor control.
定义手部运动的各种参数，如轨迹、速度等，是由不同的大脑活动编码的。从神经生理学记录中解码这些信息是脑机接口（BCI）研究中一个较少被探索的领域。应用诸如脑电图（EEG）等非侵入性记录进行解码会使问题更具挑战性，因为这种编码被认为存在于大脑深处，头皮记录不易获取。
可以开发基于EEG的BCI系统来识别运动参数背后的神经特征，这些特征可进一步用于为BCI输出设备提供详细且明确的控制命令集。实时连续控制更适合实际的BCI系统，并且通过连续自适应重建运动轨迹比离散的大脑活动分类能够更好地实现。在这项工作中，我们从多通道EEG记录中自适应地重建/估计二维手部运动轨迹的参数，即运动速度和位置。用于分析的数据是通过进行一项实验收集的，该实验涉及以两种不同速度、随机顺序在四个不同方向上进行的从中心向外的右手运动任务。我们使用卡尔曼滤波器估计运动轨迹，该滤波器基于一组定义的预测变量对大脑活动与记录参数之间的关系进行建模。我们提出了一种定义这些预测变量的方法，该方法包括空间、频谱和时间局部化的神经信息，并选择最优信息变量。
所提出的方法在记录数据与估计数据之间产生了（0.60±0.07）的相关性。此外，纳入所提出的预测子集选择后，实现的相关性为（0.57±0.07，p<0.004），系统稳定性显著提高，预测变量数量大幅减少（76%），节省了计算时间。
所提出的系统提供了一种使用EEG-BCI的实时运动控制系统，可控制运动速度和位置。与基于EEG的系统中的现有技术相比，这些结果更高且具有统计学意义，因此有望使所提出的方法适用于高效估计运动参数和连续运动控制。
