Between-Frequency Topographical and Dynamic High-Order Functional Connectivity for Driving Drowsiness Assessment

Abstract

Previous studies exploring driving drowsiness utilized spectral power and functional connectivity without considering between-frequency and more complex synchronizations. To complement such lacks, we explored interregional synchronizations based on the topographical and dynamic properties between frequency bands using high-order functional connectivity (HOFC) and envelope correlation. We proposed the dynamic interactions of HOFC, associated-HOFC, and a global metric measuring the aggregated effect of the functional connectivity. The EEG dataset was collected from 30 healthy subjects, undergoing two driving sessions. The two-session setting was employed for evaluating the metric reliability across sessions. Based on the results, we observed reliably significant metric changes, mainly involving the alpha band. In HOFC θα , HOFC αβ , associated-HOFC θα , and associated-HOFC αβ , the connection-level metrics in frontal-central, central-central, and central-parietal/occipitalareas were significantly increased, indicating a dominance in the central region. Similar results were also obtained in the HOFC θαβ and aHOFC θαβ. For dynamic-low-order-FC and dynamic-HOFC, the global metrics revealed a reliably significant increment in the alpha, theta-alpha, and alpha-beta bands. Modularity indexes of associated-HOFC α and associated-HOFC θα also exhibited reliably significant differences. This paper demonstrated that within-band and betweenfrequency topographical and dynamic FC can provide complementary information to the traditional individual-band LOFC for assessing driving drowsiness.

Regression-Based Continuous Driving Fatigue Estimation: Toward Practical Implementation

“…Other features, such as functional connectivity [40,41] and entropy [20,42], could be used in the proposed framework as these features have been proven to be of discriminative power in the differentiation between alertness and fatigue. Most recently, high-order functional connectivity in both static and dynamic representations was found to have complementary information to low-order functional connectivity in fatigue detection [43]. These diverse kinds of features can be fused to improve fatigue classification [44].…”

EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis

“…The studies found that when mental fatigue level increases due to vigilance tasks, the functional coupling decreased, specifically over the parietal-to-frontal areas in individual theta, alpha and beta frequency bands. However, for the simulated driving task, studies have reported an increase in the connectivity network in the frontal-central, and central-parietal/occipital areas at the end of driving sessions [39], [40], [41]. Besides, a recent study has reported both; decrease and increase in the connectivity networks in driving fatigue [42].…”

Progresses and prospects of brain electromagnetic imaging

“…[36,37] 、相位同步(phase synchronization) [38] 、同步 似然(synchronization likelihood) [39,40] 、Granger因 果 [41,42] 、自适应直接传递函数(adaptive directed transfer function, ADTF) [43] 、跨频耦合(cross-frequency coupling) [44] 以及一些其他方法 [45~47] . 网络分析方法, 主要 有基于图论的分析方法 [48,49] 、时变脑网络分析 [50,51] 、 网络动态分析 [52~54] 、网络重组 [55,56] 、多层网络 [57,58] 、 高阶网络 [59] 等. 最近出现的网络分析处理算法则有Lp 范数Granger因果有向网络分析方法和Lp(p≤1)范数偏 有向相干(partial directed coherence)网络分析方法, 这 些方法能在噪声情况下比较准确地估计出网络连接模 式 [60,61] .…”