EEG-Based Sleep Staging Analysis with Functional Connectivity

Huang, Hui; Zhang, Jianhai; Zhu, Li; Tang, Jiajia; Lin, Guang; Kong, Wanzeng; Lei, Xu; Zhu, Lei

doi:10.3390/s21061988

Cited by 31 publications

(15 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An effect of sleep on functional network properties was reported in the literature, and might be expected to have affected our analysis. Namely, sleep is reportedly associated with a reorganization of the network towards a small-world topology (Ferri et al, 2008;Vecchio et al, 2017) and stronger connectivity in delta and alpha bands (Huang et al, 2021).…”

Section: Limitationsmentioning

confidence: 99%

EEG reveals brain network alterations in chronic aphasia during natural speech listening

Mehraram

Kries

Clercq

et al. 2023

Preprint

View full text Add to dashboard Cite

Aphasia is a common consequence of a stroke which affects language processing. In search of an objective biomarker for aphasia, we used EEG to investigate how functional network patterns in the cortex are affected in persons with post-stroke chronic aphasia (PWA) compared to healthy controls (HC) while they are listening to a story. EEG was recorded from 22 HC and 27 PWA while they listened to a 25-min-long story. Functional connectivity between scalp regions was measured with the weighted phase lag index. The Network-Based Statistics toolbox was used to detect altered network patterns and to investigate correlations with behavioural tests within the aphasia group. Differences in network geometry were assessed by means of graph theory and a targeted node-attack approach. Group-classification accuracy was obtained with a support vector machine classifier. PWA showed stronger inter-hemispheric connectivity compared to HC in the theta-band (4.5-7 Hz), whilst a weaker subnetwork emerged in the low-gamma band (30.5-49 Hz). Two subnetworks correlated with semantic fluency in PWA respectively in delta- (1-4 Hz) and low-gamma-bands. In the theta-band network, graph alterations in PWA emerged at both local and global level, whilst only local changes were found in the low-gamma-band network. As assessed with the targeted node-attack, PWA exhibit a more scale-free network compared to HC. Network metrics effectively discriminated PWA and HC (AUC = 83%). Overall, we showed for that EEG-network metrics are effective biomarkers to assess natural speech processing in chronic aphasia. We hypothesize that the detected alterations reflect compensatory mechanisms associated with recovery.

show abstract

Section: Limitationsmentioning

confidence: 99%

EEG reveals brain network alterations in chronic aphasia during natural speech listening

Mehraram

Kries

Clercq

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…As such, the CPCC measure could be used for various neurology related studies. Such studies include the EEG-based brain mechanism of sleep stages, which is important for sleep quality assessment and disease diagnosis [33]. By averaging over the trial set, the proposed measures could also be used as a solution to improve the prediction results of the phases of the synchronization and desynchronization tasks [34].…”

Section: Discussionmentioning

confidence: 99%

Complex Pearson Correlation Coefficient for EEG Connectivity Analysis

Šverko

Vrankić

Vlahinić

et al. 2022

Sensors

View full text Add to dashboard Cite

In the background of all human thinking—acting and reacting are sets of connections between different neurons or groups of neurons. We studied and evaluated these connections using electroencephalography (EEG) brain signals. In this paper, we propose the use of the complex Pearson correlation coefficient (CPCC), which provides information on connectivity with and without consideration of the volume conduction effect. Although the Pearson correlation coefficient is a widely accepted measure of the statistical relationships between random variables and the relationships between signals, it is not being used for EEG data analysis. Its meaning for EEG is not straightforward and rarely well understood. In this work, we compare it to the most commonly used undirected connectivity analysis methods, which are phase locking value (PLV) and weighted phase lag index (wPLI). First, the relationship between the measures is shown analytically. Then, it is illustrated by a practical comparison using synthetic and real EEG data. The relationships between the observed connectivity measures are described in terms of the correlation values between them, which are, for the absolute values of CPCC and PLV, not lower that 0.97, and for the imaginary component of CPCC and wPLI—not lower than 0.92, for all observed frequency bands. Results show that the CPCC includes information of both other measures balanced in a single complex-numbered index.

show abstract

“…Therefore, the characteristic path length and clustering coefficient metrics of the OMST-based brain networks are larger than the brain networks based on the manual threshold method shown in Tables 7 , 8 . Manual thresholding-based brain network analysis is a potential feature selection method for EEG classification tasks (Kong et al, 2018 ; Huang et al, 2021 ). In future study, the comparison and evaluation of different topological filtering methods are worth deeply being studied.…”

Section: Discussionmentioning

confidence: 99%

Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke

Ren

Yang

Zhu

et al. 2022

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500–800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.

show abstract

EEG-Based Sleep Staging Analysis with Functional Connectivity

Cited by 31 publications

References 55 publications

EEG reveals brain network alterations in chronic aphasia during natural speech listening

EEG reveals brain network alterations in chronic aphasia during natural speech listening

Complex Pearson Correlation Coefficient for EEG Connectivity Analysis

Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke

Contact Info

Product

Resources

About