Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, Colombia.
Sensors (Basel). 2021 Apr 13;21(8):2750. doi: 10.3390/s21082750.
Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.
运动学习与功能大脑可塑性有关，涉及神经网络中特定的功能连接变化。然而，个体之间学习新运动技能的程度存在差异，这主要是由于脑电图（EEG）记录中捕获的脑结构和功能的个体间变异性。在这里，我们提出了一种基于核的功能连接度量方法来处理运动相关任务中的个体间/个体内变异性。为此，我们从时空频率模式中通过 EEG 通道的高斯核互谱分布提取功能连接。此外，我们通过基于稀疏的ℓ2 范数特征选择框架优化光谱组合权重，该框架与执行维度减少的提取连接特征的运动相关标签相匹配。从具有运动想象和运动执行任务的三个数据库的验证结果中，我们得出结论，单次试验高斯功能连接度量提供了非常有竞争力的分类器性能值，受特征提取参数（如滑动时间窗口）的影响较小，并避免使用先验线性空间滤波。我们还为聚类功能连接模式提供了可解释性，并假设所提出的基于核的度量方法对于评估运动技能很有前景。
