论文标题
使用功能连通性分析，对脑机互化的theta-和beta波段中的手机图像进行初步评估
Preliminary Assessment of hands motor imagery in theta- and beta-bands for Brain-Machine-Interfaces using functional connectivity analysis
论文作者
论文摘要
事实证明，基于时间和频率的功能在大脑计算机界面（BCIS）中进行了精神任务的过程已被证明有效。尽管如此，这些方法中的大多数几乎没有关于潜在的大脑活动和功能的见解。因此，必须更好地理解大脑活动的机制和动力学，以便获得BCIS的有用和信息性特征。在本研究中，目的是通过部分定向连贯性（PDC）分析研究两个运动成像任务的功能连通性差异，该分析是一个频域度量指标，可提供有关在不同通道记录的信号之间相互作用的方向性的信息。四个健康的受试者参与了这项研究，评估了两项心理任务：右手或左手运动的想象力。我们通过两种不同的方法对这些任务进行了区分：一方面，基于光谱力的传统方法；另一方面，一种基于PDC的方法。结果表明，基于EEG的PDC分析提供了其他信息，它可能会在Beta频段中改善特征选择。
The use of time- and frequency-based features has proven effective in the process of classifying mental tasks in Brain Computer Interfaces (BCIs). Still, most of those methods provide little insight about the underlying brain activity and functions. Thus, a better understanding of the mechanisms and dynamics of brain activity, is necessary in order to obtain useful and informative features for BCIs. In the present study, the objective is to investigate the differences in functional connectivity of two motor imagery tasks, through a partial directed coherence (PDC) analysis, which is a frequency-domain metric that provides information about directionality in the interaction between signals recorded at different channels. Four healthy subjects participated in this study, two mental tasks were evaluated: Imagination of the movement of the right hand or left hand. We carry out the differentiation of these tasks through two different approaches: on one hand, the traditional one based on spectral power; on the other hand, an approach based on PDC. The results showed that EEG-based PDC analysis provides additional information and it can potentially improve the feature selection mainly in the beta frequency band.
