A dynamical graph-based feature extraction approach to enhance mental task classification in brain-computer interfaces
A dynamical graph-based feature extraction approach to enhance mental task classification in brain-computer interfaces
复制标题
DOI：
10.2139/ssrn.4170113
复制
发表时间：
2022-12
影响因子：
7.7
通讯作者：
Shaotong Zhu;S. Hosni;Xiaofei Huang;Michael Wan;S. B. Borgheai;J. McLinden;Y. Shahriari;S. Ostadabbas
中科院分区：
文献类型：
--
作者：
Shaotong Zhu;S. Hosni;Xiaofei Huang;Michael Wan;S. B. Borgheai;J. McLinden;Y. Shahriari;S. Ostadabbas
图论方法在分析大脑活动的时空动力学研究不足，但可能是非常有前途的方向，在开发有效的脑机接口（BCI）。现有的许多脑机接口系统使用脑电信号来非侵入性地记录和解码人类的神经活动。然而，通常情况下，从EEG信号中提取的特征忽略了隐藏在EEG时间动态中的拓扑信息。此外，现有的图论方法主要用于揭示基于来自不同空间区域的信号之间的同步的脑功能网络的拓扑模式，而不是在不同时间戳的状态之间的相互依赖性。在这项研究中，我们提出了一个强大的折叠式超参数优化框架，利用一系列传统的基于图的测量结合频谱图特征，并调查其区分性能的分类设计的心理任务在6名参与者肌萎缩侧索硬化症（ALS）。在我们所有的参与者中，通过结合全局基于图的测量和谱图特征，我们达到了71.1%±4.5%的心理任务分类的平均准确率，高于传统的非基于图的特征性能（67.1%±7.5%）。与使用任一图形特征（特征值为66.3%±6.5%，全局图形特征为65.9%±5.2%）相比，我们的特征组合策略在准确性和鲁棒性性能方面都有相当大的提高。我们的研究结果表明，所提出的折叠式优化框架的可行性和优势，利用基于图形的功能在BCI系统针对最终用户。
Graph theoretic approaches in analyzing spatiotemporal dynamics of brain activities are under-studied but could be very promising directions in developing effective brain-computer interfaces (BCIs). Many existing BCI systems use electroencephalogram (EEG) signals to record and decode human neural activities noninvasively. Often, however, the features extracted from the EEG signals ignore the topological information hidden in the EEG temporal dynamics. Moreover, existing graph theoretic approaches are mostly used to reveal the topological patterns of brain functional networks based on synchronization between signals from distinctive spatial regions, instead of interdependence between states at different timestamps. In this study, we present a robust fold-wise hyperparameter optimization framework utilizing a series of conventional graph-based measurements combined with spectral graph features and investigate its discriminative performance on classification of a designed mental task in 6 participants with amyotrophic lateral sclerosis (ALS). Across all of our participants, we reached an average accuracy of 71.1%±4.5% for mental task classification by combining the global graph-based measurements and the spectral graph features, higher than the conventional non-graph based feature performance (67.1%±7.5%). Compared to using either one of the graphic features (66.3%±6.5% for the eigenvalues and 65.9%±5.2% for the global graph features), our feature combination strategy shows considerable improvement in both accuracy and robustness performance. Our results indicate the feasibility and advantage of the presented fold-wise optimization framework utilizing graph-based features in BCI systems targeted at end-users.