基于多层脑功能网络特征的动作意图识别

基于脑电信号完成对步态特征的解码分析并就动作意图做出可靠识别和预测，是基于脑机接口的人机混合康复训练系统和智能助行机器人中的核心问题。为实现对站立、坐下以及静止状态这些最基本步态过程的分类识别，提出了基于多层脑功能网络分析的特征表示方法，结合对各类脑功能网络特征的统计分析，确定对不同动作敏感的网络特征量，并结合支持向量机、线性判别分析、逻辑回归以及朴素贝叶斯算法完成对不同动作过程的分类识别。实验结果表明，所提出的方法可较好地实现对上述动作意图的识别，针对13名被试者对站立、坐下和静止状态的识别准确率均高于71%，最高达到77%。对多层动态脑功能网络的分析结果表明，下肢运动过程的发生会弱化脑区间的相互依赖关系，导致网络拓扑连接结构变得逐渐稀疏。研究结果对理解下肢运动过程中大脑认知过程变化，开展基于脑机接口的下肢康复策略研究和康复系统开发具有一定的参考价值。

Action Intention Recognition Based on Multi-layer Functional Brain Network

The decoding analysis of gait features based on electroencephalogram (EEG) and the reliable recognition and prediction of motion intention are the core problems of brain-computer interface (BCI) based human-machine hybrid rehabilitation training system and intelligent walking robot. In order to realize the recognition of the most basic gait processes such as standing, sitting and resting states, this study proposes a feature representation method based on multi-layer functional brain network using EEG. Combined with the statistical analysis of various network features, these parameters sensitive to different movements are determined, and support vector machine, linear discriminant analysis, logistic regression and naive bayes algorithms are applied to complete the classification of different actions. Experiment results show the proposed method can realize the recognition of the three actions, and the recognition accuracy of standing, sitting and resting state is higher than 71% and the highest accuracy is 77% for 13 subjects. Multi-layer brain network analysis shows the motion action of lower limb can weaken the interdependence between brain regions, resulting the sparsification of the topology structure. This study has certain reference value for understanding the changes of brain cognitive process during lower limb movement, carrying out BCI based rehabilitation strategies, and developing corresponding rehabilitation systems.