Graph Theoretical Analysis of Functional Brain Networks in Healthy Subjects: Visual Oddball Paradigm

Thee, Kang Wei; Nisar, Humaira; Soh, Chit Siang

doi:10.1109/access.2018.2877035

Cited by 15 publications

(10 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, in θ and δ waves, pSTh exposes an enhanced connectivity between the medial frontal cortex and other cortical regions (including the parietal) during attention and surprise/novelty processing; this conclusion is suggested also in Gulbinaite et al (2014). In the case of visual tasks, parieto-central, parieto-temporal, and occipitotemporal and occipito-parietal links are observed with enhanced relevance as discussed in Thee et al (2018), associating all these links with object detection and visual processing. With regards to the piecewise interpretation of target responses, all the abovereferenced findings become more distinctly seen when applying the variable window.…”

Section: Topoplot Brain Mapping Of Group-level Connectivity Analysismentioning

confidence: 64%

“…To overcome this issue, the quasi-stationary activity of large neuronal populations has been considered by extracting synchronization estimates from a set of previously segmented time intervals, which are statistically verified (Pereda et al, 2018) or within the stationary assumption holds (Kaplan et al, 2005). In the latter approach, non-overlapping segments are used with the purpose of dividing the grand-average ERP into time-windows to evaluate the functional network changes (Thee et al, 2018). Thus, there are two main approaches for piecewise segmenting within the estimates of FC measures are extracted from EEG recordings: a fixed time window and variable window along the ERP response.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks

et al. 2020

View full text Add to dashboard Cite

Section: Topoplot Brain Mapping Of Group-level Connectivity Analysismentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks

et al. 2020

View full text Add to dashboard Cite

“…The TR case is significantly different (p=0.002) from NTNR case in terms of average LI values. Thus, we conclude that TR case triggered denser networks as compared to NTNR case [50,99]. Moreover, WT/PCA is sufficient for PLV to use as a connectivity estimation modality for visual oddball paradigm.…”

Section: Resultsmentioning

confidence: 85%

“…The linear approaches include cross-correlation [46] and coherence [47], whereas non-linear methods include mutual information [48], phase synchronization [49] etc. Linear and non-linear methods are widely used for FC estimations in the sensor space [50-52] as well as source space [53-56].…”

Section: Introductionmentioning

confidence: 99%

“…1) To evaluate the differences in terms of functional connectivity among oddball cases in the source space. Previously the evaluation was done in the sensor space [50,51,64], 2) To assess the joint capacity of the time-frequency decomposition algorithms / scout functions applied to reconstructed EEG sources to evaluate brain FC elicited by our oddball task; as the joint capacity has not been evaluated by other studies. In this study, we also assess the impact of the scout functions and time-frequency decomposition algorithms on FC estimation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of EEG source reconstructed functional networks in healthy subjects elicited during visual oddball task

Thee

Nisar

Yeap

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

30In this paper we have reconstructed electroencephalography (EEG) sources using weighted 31 Minimum Norm Estimator (wMNE) for visual oddball experiment to estimate brain functional 32 networks. Secondly we have evaluated the impact of time-frequency decomposition algorithms 33 and scout functions on brain functional networks estimation using phase-locked value (PLV). 34 Lastly, we compared the difference between target stimuli with response (TR) and non-target 35 with no response (NTNR) cases in terms of brain functional connectivity (FC). We acquired 36 the EEG data from 20 healthy participants using 129 channels EEG sensor array for visual 37 oddball experiment. Three scout functions: i) MEAN, ii) MAX and iii) PCA were used to 38 extract the regional time series signals. We transformed the regional time series signals into 39 complex form using two methods: i) Wavelet Transform (WT) and ii) Hilbert Transform (HT). 40The instantaneous phases were extracted from the complex form of the regional time series 41 signals. The FC was estimated using PLV. The joint capacity of the time-frequency 42 decomposition algorithms/scout functions applied to reconstructed EEG sources was evaluated 43 using two criteria: i) localization index (LI) and ii) R. The difference in FC between TR and 44 NTNR cases was evaluated using these two criteria. Our results show that the WT has higher 45 impact on LI values and it is better than HT in terms of consistency of the results as the standard 46 deviation (SD) of WT is lower. In addition, WT/PCA pair is better than other pairs in terms of 47 consistency as the SD of the pair is lower. This pair is able to estimate the connectivity within 48 parietal region which corresponds to P300 response; although WT/MEAN is also able to do 49 that, However, WT/PCA has lower SD than WT/MEAN. Lastly, the differences in connectivity 50 between TR and NTNR cases over parietal, central, right temporal and limbic regions which 51 correspond to target detection, P300 response and motor response were observed. Therefore, 52 we conclude that the output of the connectivity estimation might be affected by time-frequency 53 decomposition algorithms/scout functions pairs. Among the pairs, WT/PCA yields best results 54 for the visual oddball task. Moreover, TR and NTNR cases are different in terms of estimated 55 functional networks. 56 57 and non-linear methods are widely used for FC estimations in the sensor space [50-52] as well 102 as source space [53-56]. 103 Based on the literature review it is observed that the estimated FC depends on the 104 algorithms to solve the EEG ill-posed inverse problem and the methods for connectivity 105 estimation. Hassan et al. reported that the use of wMNE in conjunction with the phase-locking 106 value (PLV) provides better results as compared to the other combinations in the sensor space 107 [26,57]. This combination has been adapted for FC analysis in the source space [25,58-60]. 108 In this research, we planned to utilized wMNE to reconstruct the dipolar so...

show abstract

Performance Enhancement of Motor Imagery EEG Signals Using Feature Extraction and Classification with Time Domain Statistical Parameters of Brain–Computer Interface

Thanigaivelu¹,

Sridhar²,

Sulthana³

2022

International Conference on Innovative Computing and Communications

View full text Add to dashboard Cite

Graph Theoretical Analysis of Functional Brain Networks in Healthy Subjects: Visual Oddball Paradigm

Cited by 15 publications

References 68 publications

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks

Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks

Comparison of EEG source reconstructed functional networks in healthy subjects elicited during visual oddball task

Performance Enhancement of Motor Imagery EEG Signals Using Feature Extraction and Classification with Time Domain Statistical Parameters of Brain–Computer Interface

Contact Info

Product

Resources

About