Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States

Al-Shargie, Fares; Tariq, Usman; Hassanin, Omnia; Mir, Hasan; Babiloni, Fabio

doi:10.3390/brainsci9120363

Cited by 39 publications

(26 citation statements)

References 86 publications

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“…Meanwhile, the accuracy of detecting the SCWT stimuli decreased by -10%. In line with our previous observations [64], the significant increase in the RT, suggesting a genuine reduction in the accuracy for timely responding as opposed a speed-accuracy tradeoff [65]. The increase in RT indicated that participants lose their interest in performing the task or found it stressful.…”

Section: Summary and Discussionsupporting

confidence: 90%

“…The GTA measurements including nodal degree, clustering coefficient, global efficiency and transitivity significantly decreased with vigilance decrement indicating a loss of information exchange between brain regions as demonstrated in FIGURE 6 to FIGURE 8. Consistent with this observation, we have previously reported a vigilance-related significant decrease of cluster coefficient and node degree over a 60-min SCWT in one frequency band at 0.1-30 Hz [64]. Previous studies have investigated various mental tasks in the single frequency bands [36], [40] and there is lack of research in essential properties in different bands.…”

Section: Summary and Discussionsupporting

confidence: 80%

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

et al. 2020

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This paper proposes two novel methods to classify semantic vigilance levels by utilizing EEG directed connectivity patterns with their corresponding graphical network measures. We estimate the directed connectivity using relative wavelet transform entropy (RWTE) and partial directed coherence (PDC) and the graphical network measures by graph theory analysis (GTA) at four frequency bands. The RWTE and PDC quantify the strength and directionality of information flow between EEG nodes. On the other hand, the GTA of the complex network measures summarizes the topological structure of the network. We then evaluate the proposed methods using machine learning classifiers. We carried out an experiment on nine subjects performing semantic vigilance task (Stroop color word test (SCWT)) for approximately 45 minutes. Behaviorally, all subjects demonstrated vigilance decrement as reflected by the significant increase in response time and reduced accuracy. The strength and directionality of information flow in the connectivity network by RWTE/PDC and the GTA measures significantly decrease with vigilance decrement, p<0.05. The classification results show that the proposed methods outperform other related and competitive methods available in the literature and achieve 100% accuracy in subject-dependent and above 89% in subject-independent level in each of the four frequency bands. The overall results indicate that the proposed methods of directed connectivity patterns and GTA provide a complementary aspect of functional connectivity. Our study suggests directed functional connectivity with GTA as informative features and highlight Support Vector Machine as the suitable classifier for classifying semantic vigilance levels.

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Section: Summary and Discussionsupporting

confidence: 90%

Section: Summary and Discussionsupporting

confidence: 80%

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

et al. 2020

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“…Previous studies have suggested that increased entropy was found to be related to performance improvement [75], [76]. The increased entropy value under vigilance enhancement is also consistent with the increased connectivity network measures reported in our previous vigilance studies [60], [61].…”

Section: B Vigilance Level and Entropy Measurementssupporting

confidence: 91%

“…We simultaneously recorded the electrical brain activity (EEG signals), self-reports and behavioral responses of the participants while performing the SCWT under vigilance decrement (VD) and vigilance enhancement (VE) states. In line with our previous studies [60], [61], and with the results of the behavioral responses of the participants we defined the VD state as the state in which participants performed the SCWT continuously for 30 min. Meanwhile, the VE state was defined as the state in which participants performed the same SCWT while listening to the PT audio stimulation of 250 Hz for 30 min.…”

Section: Discussionmentioning

confidence: 77%

Cognitive Vigilance Enhancement Using Audio Stimulation of Pure Tone at 250 Hz

et al. 2021

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In this paper, we propose a novel vigilance enhancement method based on audio stimulation of pure tone at 250 Hz. We induced two different levels of vigilance state; vigilance decrement (VD) and vigilance enhancement (VE). The VD state was induced by performing a modified version of the Stroop Color-Word Task (SCWT) for approximately 45 minutes. Likewise, the VE state was induced by incorporating audio stimulation of 250 Hz into the SCWT for 45 minutes. We assessed the levels of vigilance on 20 healthy subjects by utilizing Electroencephalogram (EEG) signals and machine learning. The EEG signals were analyzed using four types of entropies; Approximate Entropy (AE), Sample Entropy (SE), Fuzzy Entropy (FE), and Differential Entropy (DE). We then quantified vigilance levels using statistical analysis and support vector machines (SVM) classifier. We found that the proposed VE method has significantly reduced the reaction time (RT) by 44% and improved the accuracy of target detection by 25%, (p<0.001) compared to VD state. Besides, we found that 30 min of audio stimulation has reduced the RT by 32% from the beginning to the end of VE phase of the experiment. The entropy measures show that the temporal profile of the EEG signals has significantly increased with VE. The classification results showed that SVM technique with DE features across all frequency bands can detect VE levels with accuracy varying between (92.10±02.24)% to (98.32±01.14)%, sensitivity of (92.50±02.33)% to (98.66±01.00)%, and specificity of (91.70±02.32)% to (97.99±01.05)%. Results also showed that the classification performance using DE has outperformed the other entropy measures by an average of +8.07%. Our results demonstrate the effectiveness of the proposed 250 Hz audio stimulation method in improving vigilance level and suggest using it for future cognitive enhancement studies.

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“…In the present study, we investigated the relations between directed connectivity features and attention indexes. Based on [15][29] [30], we present the following hypotheses:…”

Section: Introductionmentioning

confidence: 99%

Directed EEG Functional Connectivity Features to Reveal Different Attention Indexes Using Hierarchical Clustering

Iddas

Iramina

2021

IEEE Access

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Functional connectivity related to familiarity has recently been investigated in the context of various stimuli (e.g., words, faces, pictures, music, and video). However, the directed functional connectivity patterns with different attention indexes as a response to familiar/unfamiliar stimuli remain unclear. In the current study, we employed the Directed Transfer Function (DTF) to estimate the information flow between brain areas. This method was reported to be practically robust to volume conduction. Furthermore, the hierarchical clustering approach was utilized to group subjects based on the attention index, i.e., the alpha/theta ratio of fronto-central (frontal to central and central to frontal) features. Three major findings were revealed from this study. First, all subjects had different attention indexes when they watched familiar/unfamiliar videos. Then, subjects were sorted into three groups: low index (LI), middle index (MI), and high index (HI). Second, a competition between two states (familiar/unfamiliar) showed that the information flows of familiar stimuli were greater than unfamiliar stimuli, which involved significant effects in the frontal, temporal, and parietal areas. Third, comparison between groups (LI/MI/HI) demonstrated that the frontal and central regions were the primary sources that distributed information flows to almost the whole brain, particularly during familiar conditions. This result indicates that these two regions may play an important role in attentional processing.INDEX TERMS attention index, Directed Transfer Function (DTF), Dunn Index, electroencephalograph (EEG), familiarity, fronto-central, hierarchical clustering.

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Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States

Cited by 39 publications

References 86 publications

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

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

Cognitive Vigilance Enhancement Using Audio Stimulation of Pure Tone at 250 Hz

Directed EEG Functional Connectivity Features to Reveal Different Attention Indexes Using Hierarchical Clustering

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