The Time-Varying Networks in P300: A Task-Evoked EEG Study

Li, Fali; Chen, Bei; Li, He; Zhang, Tao; Wang, Fei; Jiang, Yi; Li, Peiyang; Ma, Teng; Zhang, Rui; Tian, Yin; Liu, Tiejun; Guo, Daqing; Yao, Dezhong; Xu, Peng

doi:10.1109/tnsre.2016.2523678

Cited by 107 publications

(71 citation statements)

References 58 publications

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“…Another point is that we selected only Ge from the four BN properties as a component of fusion feature for emotion recognition. According to the graph theory, Ge represents the information integration and the exchange efficiency of the whole network (Li et al, 2016). The statistical results showed that Ge was more sensitive than other BN properties in depicting the differences among the different emotional states.…”

Section: Superiority Of High Gamma Band Features In Emotion Recognitionmentioning

confidence: 99%

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High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network

Yang

Shu

et al. 2020

Front. Hum. Neurosci.

111

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High-frequency electroencephalography (EEG) signals play an important role in research on human emotions. However, the different network patterns under different emotional states in the high gamma band (50-80 Hz) remain unclear. In this paper, we investigate different emotional states using functional network analysis on various frequency bands. We constructed multiple functional networks on different frequency bands and performed functional network analysis and time-frequency analysis on these frequency bands to determine the significant features that represent different emotional states. Furthermore, we verified the effectiveness of these features by using them in emotion recognition. Our experimental results revealed that the network connections in the high gamma band with significant differences among the positive, neutral, and negative emotional states were much denser than the network connections in the other frequency bands. The connections mainly occurred in the left prefrontal, left temporal, parietal, and occipital regions. Moreover, long-distance connections with significant differences among the emotional states were observed in the high frequency bands, particularly in the high gamma band. Additionally, high gamma band fusion features derived from the global efficiency, network connections, and differential entropies achieved the highest classification accuracies for both our dataset and the public dataset. These results are consistent with literature and provide further evidence that high gamma band EEG signals are more sensitive and effective than the EEG signals in other frequency bands in studying human affective perception.

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Section: Superiority Of High Gamma Band Features In Emotion Recognitionmentioning

confidence: 99%

“…However, the network connections and information processing and propagation in these time windows remain unclear. In the future, we will construct time-varying BNs (Li et al, 2016) and investigate the neural mechanism of the brain in more precise time windows.…”

Section: Superiority Of High Gamma Band Features In Emotion Recognitionmentioning

confidence: 99%

High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network

Yang

Shu

et al. 2020

Front. Hum. Neurosci.

111

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“…The results of statistical tests denote that both cases are significantly distinct with each other in LP (p=0.049) and RP (p=0.007) in terms of their designated R values. Denser bilateral cortical activity is observed over parietal regions in TR case that corresponds to P300 response [105,115-118] and motor coordination for button pressing [76,119,120]. Thus, NTNR case has lower bilateral parietal activity as no P300 and motor responses are required for non-target stimuli.…”

Section: Resultsmentioning

confidence: 99%

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

Thee

Nisar

Yeap

et al. 2019

Preprint

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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...

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“…EEG was pre-processed by averaging, re-referencing and 0.5–30 Hz band-pass filtering prior to the time-varying network analysis. The time-varying network analysis usually requires several segmentations (i.e., trials in evoked EEG experiment) to enable the construction of a reliable network in order to capture the brain architectures and networks (Hu et al, 2012 ; Li et al, 2016 ). In this work, compared to the time-varying network analysis for the evoked EEG that usually has the definite stimulus labels, one drawback here is that no exact events were labeled for the inter-ictal discharging during the 24-h EEG monitoring.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we assume that the time-varying network patterns may provide useful information to uncover the abnormal information processing and propagation when inter-ictal discharging is observed. Consequently, the time-varying network analysis (i.e., adaptive directed transfer function) which can be applied to investigate the dynamic network patterns during certain task (Li et al, 2016 ) is vital in establishing the corresponding time-varying networks during inter-ictal discharging. Moreover, given the fact that the inter-ictal spiking activity presented here is similar to ictal activity (Wilke et al, 2011 ) and the inter-ictal spiking yet easier to be obtained than ictal data.…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone

Zhang

Liang

et al. 2017

Front. Comput. Neurosci.

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The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging.

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The Time-Varying Networks in P300: A Task-Evoked EEG Study

Cited by 107 publications

References 58 publications

High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network

High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network

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

Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone

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