Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach

The effects of different challenge-level balance tasks on stroke cortical responses and balance assessment using EEG

Previous studies have validated that different balance tasks induce different cortical responses, which are key indexes of balance assessment. Assessing balance is crucial for stroke survivors to prevent falls and improve rehabilitation outcomes. However, it was unclear whether these tasks may affect the balance assessment, particularly regarding the relationship between task difficulty and the corresponding cortical responses involved in balance control. Therefore, we sought to explore the effects of different challenge-level balance tasks on balance assessment. Eighteen participants with stroke and thirteen healthy individuals were recruited in this study. The EEG was collected during sitting, standing and perturbation tasks. The pairwise-derived Brain Symmetry Index (pdBSI), and Granger Causality (GC) were analyzed with a two-way (task ×group) RMANOVA. Finally, a multiple linear regression analysis was applied to predict the BBS score with the above parameters. We found a significant interaction effect on pdBSI and GC. In the frontal lobe, participants with stroke exhibited significantly higher pdBSI (standing: p=0.042, perturbation: p=0.013) and lower GC (standing: p<0.001, perturbation: p=0.028) compared to healthy controls. Similarly, in the parietal lobe, stroke survivors showed markedly higher pdBSI (standing: p = 0.006, perturbation: p=0.012) and lower GC (standing: p=0.030, perturbation: p=0.011). Finally, The Berg Balance Scale (BBS) scores could be reliably predicted using parietal BSI and frontal GC metrics recorded during standing (p<0.001, adjusted R²=0.938) and perturbation tasks (p=0.001, adjusted R²=0.644). It was discovered that the more challenging balance tasks better revealed the difference in the power distribution and the directional functional connection between groups. The pdBSI and GC during standing and perturbation tasks, could be used as biomarkers for stroke balance assessment.

Passive BCI Towards Health and Safety in Industry: Forecasting Human Vigilance 5.5 s Ahead

Brain-computer interfaces based on electroencephalography (EEG) recordings are gaining increasing interest in the industrial domain, aiming to enhance health, safety and performance by optimizing the cognitive load of industrial operators and facilitating human-robot interactions. This study introduces a novel experimental protocol and analysis pipeline for predicting vigilance degradation during repetitive tasks. A dataset was recorded from 10 volunteers who observed a robotic arm executing three distinct movements. The EEG power spectrum was analyzed over time using the continuous wavelet transform. Upon verifying the increased amplitude of EEG oscillations in the 8-12 Hz frequency band, we forecast its behaviour, comparing the vector autoregressive model with two deep learning recurrent architectures. The proposed encoder-decoder gated recurrent unit model obtained accurate forecasts (mean absolute error = 0.048, R2 = 0.726) up to 5.5 s into the future. The findings suggested the feasibility of vigilance monitoring in the Industry 5.0 framework, proposing a strategy to prevent human accidents and performance decline during monotonous activities.

Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.

A comprehensive survey of complex brain network representation

Recent years have shown great merits in utilizing neuroimaging data to understand brain structural and functional changes, as well as its relationship to different neurodegenerative diseases and other clinical phenotypes. Brain networks, derived from different neuroimaging modalities, have attracted increasing attention due to their potential to gain system-level insights to characterize brain dynamics and abnormalities in neurological conditions. Traditional methods aim to pre-define multiple topological features of brain networks and relate these features to different clinical measures or demographical variables. With the enormous successes in deep learning techniques, graph learning methods have played significant roles in brain network analysis. In this survey, we first provide a brief overview of neuroimaging-derived brain networks. Then, we focus on presenting a comprehensive overview of both traditional methods and state-of-the-art deep-learning methods for brain network mining. Major models, and objectives of these methods are reviewed within this paper. Finally, we discuss several promising research directions in this field.

Brain and brain-heart Granger causality during wakefulness and sleep

In this exploratory study we apply Granger Causality (GC) to investigate the brain-brain and brain-heart interactions during wakefulness and sleep. Our analysis includes electroencephalogram (EEG) and electrocardiogram (ECG) data during all-night polysomnographic recordings from volunteers with apnea, available from the Massachusetts General Hospital's Computational Clinical Neurophysiology Laboratory and the Clinical Data Animation Laboratory. The data is manually annotated by clinical staff at the MGH in 30 second contiguous intervals (wakefulness and sleep stages 1, 2, 3, and rapid eye movement (REM). We applied GC to 4-s non-overlapping segments of available EEG and ECG across all-night recordings of 50 randomly chosen patients. To identify differences in GC between the different sleep stages, the GC for each sleep stage was subtracted from the GC during wakefulness. Positive (negative) differences indicated that GC was greater (lower) during wakefulness compared to the specific sleep stage. The application of GC to study brain-brain and brain-heart bidirectional connections during wakefulness and sleep confirmed the importance of fronto-posterior connectivity during these two states, but has also revealed differences in ipsilateral and contralateral mechanisms of these connections. It has also confirmed the existence of bidirectional brain-heart connections that are more prominent in the direction from brain to heart. Our exploratory study has shown that GC can be successfully applied to sleep data analysis and captures the varying physiological mechanisms that are related to wakefulness and different sleep stages.

Directed Functional Coordination Analysis of Swallowing Muscles in Healthy and Dysphagic Subjects by Surface Electromyography

Swallowing is a complex sequence of highly regulated and coordinated skeletal and smooth muscle activity. Previous studies have attempted to determine the temporal relationship between the muscles to establish the activation sequence pattern, assessing functional muscle coordination with cross-correlation or coherence, which is seriously impaired by volume conduction. In the present work, we used conditional Granger causality from surface electromyography signals to analyse the directed functional coordination between different swallowing muscles in both healthy and dysphagic subjects ingesting saliva, water, and yoghurt boluses. In healthy individuals, both bilateral and ipsilateral muscles showed higher coupling strength than contralateral muscles. We also found a dominant downward direction in ipsilateral supra and infrahyoid muscles. In dysphagic subjects, we found a significantly higher right-to-left infrahyoid, right ipsilateral infra-to-suprahyoid, and left ipsilateral supra-to-infrahyoid interactions, in addition to significant differences in the left ipsilateral muscles between bolus types. Our results suggest that the functional coordination analysis of swallowing muscles contains relevant information on the swallowing process and possible dysfunctions associated with dysphagia, indicating that it could potentially be used to assess the progress of the disease or the effectiveness of rehabilitation therapies.