Neural substrates of emotional interference: A quantitative EEG study

Batabyal, T.; Muthukrishnan, Suriya Prakash; Sharma, Ratna; Tayade, Prashant; Kaur, Simran

doi:10.1016/j.neulet.2018.07.019

Cited by 13 publications

(8 citation statements)

References 43 publications

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“…Similar findings were reported, in an earlier study by Zhu, Zhang, Wu, Luo, and Luo [58] who found that when participants respond to words and ignore faces, the incongruent condition evokes a less negative N170 component (i.e., a marker of face encoding) than the congruent condition. Moreover, when participants respond to emotional faces while ignoring emotional words (and vice versa, they respond to words and ignore faces), EEG evidence shows that trials with affectively incongruent target and distractor are associated with a disengagement of fronto-parietal attentional network [59]. Finally, recent evidence from an fMRI study using the word-face interference task with adolescents provides additional insight on the involvement of the fronto-parietal network in emotional conflict elicited by emotional stimuli.…”

Section: Introductionmentioning

confidence: 96%

Asymmetric Contributions of the Fronto-Parietal Network to Emotional Conflict in the Word–Face Interference Task

et al. 2020

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The fronto-parietal network is involved in top-down and bottom-up processes necessary to achieve cognitive control. We investigated the role of asymmetric enhancement of the left dorsolateral prefrontal cortex (lDLPFC) and right posterior parietal cortex (rPPC) in cognitive control under conditions of emotional conflict arising from emotional distractors. The effects of anodal tDCS over the lDLPFC/cathodal over the rPPC and the effects of anodal tDCS over the rPPC/cathodal over the lDLPFC were compared to sham tDCS in a double-blind design. The findings showed that anodal stimulation over the lDLPFC reduced interference from emotional distractors, but only when participants had already gained experience with the task. In contrast, having already performed the task only eliminated facilitation effects for positive stimuli. Importantly, anodal stimulation of the rPPC did not affect distractors’ interference. Therefore, the present findings indicate that the lDLPFC plays a crucial role in implementing top-down control to resolve emotional conflict, but that experience with the task is necessary to reveal this role.

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Section: Introductionmentioning

confidence: 96%

Asymmetric Contributions of the Fronto-Parietal Network to Emotional Conflict in the Word–Face Interference Task

et al. 2020

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“…In this study, the database of facial emotion images (POFA) 15 , which includes 110 black and white images of facial expressions, was used as emotional stimuli. The brain areas involved during emotional interference conditions have been investigated in 16 . The brain source activities were computed using sLORETA.…”

Section: Introductionmentioning

confidence: 99%

“…The brain source activities were computed using sLORETA. Considerable decreased activity [p < 0.05/66] with respect to baseline are observed in Eighteen gyri in face-word interference and fifty-four gyri in word-face interference, respectively 16 . To detect EEG emotions, a dynamic convolutional neural network (DGCNN) is presented in 17 .…”

Section: Introductionmentioning

confidence: 99%

Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes

Asadzadeh

Rezaii

Beheshti

et al. 2022

Sci Rep

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Due to the effect of emotions on interactions, interpretations, and decisions, automatic detection and analysis of human emotions based on EEG signals has an important role in the treatment of psychiatric diseases. However, the low spatial resolution of EEG recorders poses a challenge. In order to overcome this problem, in this paper we model each emotion by mapping from scalp sensors to brain sources using Bernoulli–Laplace-based Bayesian model. The standard low-resolution electromagnetic tomography (sLORETA) method is used to initialize the source signals in this algorithm. Finally, a dynamic graph convolutional neural network (DGCNN) is used to classify emotional EEG in which the sources of the proposed localization model are considered as the underlying graph nodes. In the proposed method, the relationships between the EEG source signals are encoded in the DGCNN adjacency matrix. Experiments on our EEG dataset recorded at the Brain-Computer Interface Research Laboratory, University of Tabriz as well as publicly available SEED and DEAP datasets show that brain source modeling by the proposed algorithm significantly improves the accuracy of emotion recognition, such that it achieve a classification accuracy of 99.25% during the classification of the two classes of positive and negative emotions. These results represent an absolute 1–2% improvement in terms of classification accuracy over subject-dependent and subject-independent scenarios over the existing approaches.

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“…Some deep neural networks applied to electroencephalography data claim greater than 90% accuracy in terms of predicting mental health symptoms [15][16][17]. Also, traditionally, several studies relate EEG neural markers to cognitive functions, such as attention [18][19][20], inhibitory control [21][22][23], emotion processing [18,24], working memory, and cognitive load [25][26][27][28]. Yet, specific mental health symptoms have not been predicted from the standpoint of multiple neural markers underlying essential cognitive processes.…”

Section: Introductionmentioning

confidence: 99%

Utility of Cognitive Neural Features for Predicting Mental Health Behaviors

Kato

Balasubramani

Ramanathan

et al. 2022

Sensors

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Cognitive dysfunction underlies common mental health behavioral symptoms including depression, anxiety, inattention, and hyperactivity. In this study of 97 healthy adults, we aimed to classify healthy vs. mild-to-moderate self-reported symptoms of each disorder using cognitive neural markers measured with an electroencephalography (EEG). We analyzed source-reconstructed EEG data for event-related spectral perturbations in the theta, alpha, and beta frequency bands in five tasks, a selective attention and response inhibition task, a visuospatial working memory task, a Flanker interference processing task, and an emotion interference task. From the cortical source activation features, we derived augmented features involving co-activations between any two sources. Logistic regression on the augmented feature set, but not the original feature set, predicted the presence of psychiatric symptoms, particularly for anxiety and inattention with >80% sensitivity and specificity. We also computed current flow closeness and betweenness centralities to identify the “hub” source signal predictors. We found that the Flanker interference processing task was the most useful for assessing the connectivity hubs in general, followed by the inhibitory control go-nogo paradigm. Overall, these interpretable machine learning analyses suggest that EEG biomarkers collected on a rapid suite of cognitive assessments may have utility in classifying diverse self-reported mental health symptoms.

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Neural substrates of emotional interference: A quantitative EEG study

Cited by 13 publications

References 43 publications

Asymmetric Contributions of the Fronto-Parietal Network to Emotional Conflict in the Word–Face Interference Task

Asymmetric Contributions of the Fronto-Parietal Network to Emotional Conflict in the Word–Face Interference Task

Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes

Utility of Cognitive Neural Features for Predicting Mental Health Behaviors

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