The Influence of Negative Emotion on the Simon Effect as Reflected by P300

Ma, Qingguo; Shang, Qian

doi:10.1155/2013/516906

Cited by 3 publications

(2 citation statements)

References 33 publications

(52 reference statements)

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“…left vs. right), but the computerized testing with EEG provide objective measures of the compromised oscillatory component behind interference processing. Future studies can incorporate different interference factors, such as the Simon effect 45 . Finally, more participants are female than male, both for CH-NATs and CH-PATs, which may reflect greater altruism or greater resilience in females for aging.…”

Section: Limitations Clinical Implications and Future Studiesmentioning

confidence: 99%

A study of alpha desynchronization, heart rate, and MRI during stroop testing unmasks pre‐symptomatic Alzheimer’s disease

Arakaki

Hung

Wei

et al. 2020

Alzheimer's & Dementia

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Background Alzheimer’s disease (AD) studies suggested the need to detect pre‐symptomatic stage, when cognitive challenges reveal changes of alpha power (brain hyperactivity). Heart rate (HR) regulates brain oxygen supply, is regulated by the brain (eg. hippocampus and amygdala), and correlated with resting state alpha power. We aimed to compare alpha power, HR, and hippocampal and amygdala volume between pre‐symptomatic AD and normal, aging individuals. Method We employed quantitative electroencephalography (qEEG) to monitor brain activity during resting and during Stroop interference testing. Cognitively healthy (CH) study participants (demographically matched) were recruited from the local community, consisting of two subgroups based on cerebrospinal fluid (CSF) proteins: with normal amyloid/tau ratio (CH‐NAT, n=20) or pathological amyloid/tau ratio (CH‐PAT, equals pre‐symptomatic AD, n=21). Cognition was assessed using Montreal Cognitive Assessment (MoCA) and Mini‐Mental State Examination‐7 (MMSE‐7). Participants were presented a series of colored words and asked to respond to each word for the color of the ink, including low load (congruent trials, when color matches the word) and high load (incongruent trials, when color does not match the word). Comparisons between two groups include: alpha desynchronization and alpha spectral entropy (SE), as well as the relationships of alpha desynchronization, HR after Stroop testing, and hippocampal and amygdala volumes (1.5T MRI, demographically balanced subgroups). Result No alpha differences were found during the resting state. Occipital alpha desynchronization of CH‐PATs was more negative than in that of CH‐NATs (p=0.024) during the congruent trials (Figure 1), indicating hyperactivity during low load. CH‐PATs had higher alpha SE during congruent trials (p=0.042 frontal, p=0.039 occipital), and lower frontal SE change from congruent to incongruent trials (p=0.012) (Figure 2), supporting reduced functional reserve. Alpha desynchronization positively correlated with HR in CH‐PATs but not CH‐NATs; alpha desynchronization correlated with hippocampal or amygdala differently between CH‐NATs and CH‐PATs (Figure 4). Multiple spectral frequencies revealed correlations in MMSE‐7 & MoCA that differed between CH‐NATs and CH‐PATs. Conclusion These results suggest that hyper‐excitability during low load challenge and limited brain reserve is manifest with increasing interference load in pre‐symptomatic AD. We also find brain‐heart coupling is altered in pre‐symptomatic AD.

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Section: Limitations Clinical Implications and Future Studiesmentioning

confidence: 99%

A study of alpha desynchronization, heart rate, and MRI during stroop testing unmasks pre‐symptomatic Alzheimer’s disease

Arakaki

Hung

Wei

et al. 2020

Alzheimer's & Dementia

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“…The different pattern of QRS complex will determine the patient heart syndrome. Additionally, the peak detection for the EEG signal has been widely used to detect P300 response [ 4 , 5 ] and epilepsy response [ 6 ]. P300 is a brain response measured by electrodes covering the parietal lobe in the presence of visual and auditory stimuli.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

Adam

Shapiai

Tumari

et al. 2014

The Scientific World Journal

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Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

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Alpha desynchronization during Stroop test unmasks cognitively healthy individuals with abnormal CSF Amyloid/Tau

Arakaki

Hung

Rochart

et al. 2022

Neurobiology of Aging

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The Influence of Negative Emotion on the Simon Effect as Reflected by P300

Cited by 3 publications

References 33 publications

A study of alpha desynchronization, heart rate, and MRI during stroop testing unmasks pre‐symptomatic Alzheimer’s disease

A study of alpha desynchronization, heart rate, and MRI during stroop testing unmasks pre‐symptomatic Alzheimer’s disease

Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

Alpha desynchronization during Stroop test unmasks cognitively healthy individuals with abnormal CSF Amyloid/Tau

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