Electrophysiological correlates of anxious rumination

Andersen, Søren Bo; Moore, Roger A.; Venables, Louise; Corr, Philip J.

doi:10.1016/j.ijpsycho.2008.09.004

Cited by 88 publications

(64 citation statements)

References 48 publications

(67 reference statements)

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“…[52] Similarly, a theta increase was also reported during anxious personal object rumination compared to non-anxious object rumination. [63] The alpha rhythm (8 to 13 Hz) is most prominent over parietal and occipital regions, especially when the eyes are closed, and decreases in response to visual, [64] auditory (tau-rhythm [65]) and tactile (central mu-rhythm [66]) stimulation or during mental tasks. The eventrelated desynchronization in the alpha band (decrease of alpha power) in response to stimulation is believed to represent increased sensory processing, and hence has been associated with an activation of task-relevant sensory cortical regions.…”

Section: Frequency-domain Correlatesmentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

238

159

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Affective states, moods and emotions, are an integral part of human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial expression, and physiological signals, primarily with the goal of enhancing human-computer interaction with an affective component. With the advent of brain-computer interface research, the idea of affective brain-computer interfaces (aBCI), enabling affect detection from brain signals, arose. In this article, we set out to survey the field of neurophysiology-based affect detection. We outline possible applications of aBCI in a general taxonomy of brain-computer interface approaches and introduce the core concepts of affect and their neurophysiological fundamentals. We show that there is a growing body of literature that evidences the capabilities, but also the limitations and challenges of affect detection from neurophysiological activity

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Section: Frequency-domain Correlatesmentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

238

159

View full text Add to dashboard Cite

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“…[1][2][3][4][5][6][7][8][9] These characteristics may have resulted in misconceptions that, in turn, led to theoretical disagreement, erroneous theoretical models and flawed interpretations of results. [3][4][5][6][7]17,20,21,[23][24][25] The analysis of studies in our final sample suggested the existence of causal links between neurophysiological and neuroanatomical findings and CR. 17,20,22 These findings are based on the existence of hypothetical cerebral systems that may be responsible for activating and inhibiting specific types of human behaviors (response styles).…”

Section: Methodsmentioning

confidence: 71%

“…[3][4][5][6][7]17,20,21,[23][24][25] The analysis of studies in our final sample suggested the existence of causal links between neurophysiological and neuroanatomical findings and CR. 17,20,22 These findings are based on the existence of hypothetical cerebral systems that may be responsible for activating and inhibiting specific types of human behaviors (response styles). Electroencephalographic findings showed hippocampus augmentation and neocortex activation during CR activity.…”

Section: Methodsmentioning

confidence: 71%

“…Electroencephalographic findings showed hippocampus augmentation and neocortex activation during CR activity. 17 Functional magnetic resonance results showed hyperactivity in anterior medial cortex in patients with CR activity when compared to controls. 20 Decreases of cortisol response levels were less frequently found among depressive patients.…”

Section: Methodsmentioning

confidence: 94%

“…[1][2][3][4][5][6][7][8][9][17][18][19][20][21][22][23][24][25][26] This approach reduces CR to an object of empirical investigation exclusively.…”

Section: Methodsmentioning

confidence: 99%

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Phenomenological aspects of the cognitive rumination construct

Meyer

Taborda

Costa

et al. 2015

Trends Psychiatry Psychother.

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Objective: To evaluate the importance of phenomenological aspects of the cognitive rumination (CR) construct in current empirical psychiatric research. Method: We searched SciELO, Scopus, ScienceDirect, MEDLINE, OneFile (GALE), SpringerLink, Cambridge Journals and Web of Science between February and March of 2014 for studies whose title and topic included the following keywords: cognitive rumination; rumination response scale; and self-reflection. The inclusion criteria were: empirical clinical study; CR as the main object of investigation; and study that included a conceptual definition of CR. The studies selected were published in English in biomedical journals in the last 10 years. Our phenomenological analysis was based on Karl Jaspers' General Psychopathology. Results: Most current empirical studies adopt phenomenological cognitive elements in conceptual definitions. However, these elements do not seem to be carefully examined and are indistinctly understood as objective empirical factors that may be measured, which may contribute to misunderstandings about CR, erroneous interpretations of results and problematic theoretical models. Conclusion:Empirical studies fail when evaluating phenomenological aspects of the cognitive elements of the CR construct. Psychopathology and phenomenology may help define the characteristics of CR elements and may contribute to their understanding and hierarchical organization as a construct. A review of the psychopathology principles established by Jasper may clarify some of these issues. Keywords: Psychopathology, phenomenology, cognitive rumination. ResumoObjetivo: Verificar a importância de aspectos fenomenológicos relacionados ao construto de ruminação cognitiva (RC) nas pesquisas empíricas psiquiátricas atuais. Método: Foram pesquisadas as bases de dados SciELO, Scopus, ScienceDirect, MEDLINE, OneFile (GALE), SpringerLink, Cambridge Journals e Web of Science, entre fevereiro e março de 2014, buscando artigos cujo tópico ou título contivessem os seguintes termos-chave: ruminação cognitiva; escala de resposta ruminativa; e autorreflexão. Os critérios de inclusão foram: estudos clínicos empíricos; RC como principal objeto de pesquisa; e estudos que incluíssem uma definição conceitual de RC. Foram considerados apenas artigos em inglês publicados em periódicos biomédicos nos últimos 10 anos. Nossa análise fenomenológica se fundamentou na Psicopatologia Geral de Jaspers. Resultados: Os conceitos de RC atualmente encontrados nas pesquisas empíricas utilizam majoritariamente elementos fenomenológicos em suas definições. Entretanto, esses elementos cognitivos são indistintamente entendidos como elementos objetivos (empíricos), passíveis de mensuração, e não parecem ser cuidadosamente examinados. Este fato pode contribuir para uma compreensão enganosa sobre RC, além de favorecer a interpretação errônea de resultados e a elaboração de paradoxos teóricos problemáticos. Conclusão: As pesquisas empíricas atuais sobre RC falham ao avaliar os aspectos fenomenológicos inerentes ao...

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Toward Affective Brain–Computer Interface: Fundamentals and Analysis of EEG‐Based Emotion Classification

Lin

Jung

Wang

et al. 2015

Emotion Recognition

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Emotion classification from non-invasively measured electroencephalographic (EEG) data has been a growing research topic because of its potential application to affective brain-computer interfaces (ABCI), such as brain-inspired multimedia interaction and clinical assessment. A crucial component in ABCI is to reliably and accurately characterize individuals' brain dynamics into distinct affective states by employing advanced methods of pattern recognition. This chapter explores principles for translating neuroscientific findings into a practical ABCI. It will cover not only an overview of state-of-the-art EEG-based emotion recognition techniques, but also the basic research exploring neurophysiological EEG dynamics associated with affective responses. Although previous studies have demonstrated the use of EEG spectral dynamics for emotion classification, most of them achieved high classification accuracy by using an affective framework involving all available channels as well as Emotion Recognition: A Pattern Analysis Approach, First Edition. Edited by Amit Konar and Aruna Chakraborty. 316TOWARD AFFECTIVE BRAIN-COMPUTER INTERFACE frequency bands. The issue of feature and electrode reduction/selection has not typically been a primary goal in ABCI research. However, this chapter aims at resolving EEG feature selection and electrode reduction issues by the generalization of subjectindependent feature/electrode set extraction techniques that we have proposed in our series of emotion classification studies [1][2][3][4][5][6][7]. Furthermore, this study addresses several practical issues and potential challenges for ABCIs as well. We believe a userfriendly EEG cap with a small number of electrodes can efficiently detect affective states, and therefore significantly promote practical ABCI applications in daily life. INTRODUCTION Brain-Computer InterfaceBrain-computer interfaces (BCIs) translate human intentions into control signals to establish a direct communication channel between the human brain and output devices [8]. During the past two decades, BCI technology has become a hot research topic in the areas of neuroscience, neural engineering, medicine, and rehabilitation [9,10]. To detect human brain activities in real time, researchers have used different neuroimaging modalities such as electroencephalogram (EEG), magenetoencephalogram (MEG), electrocorticogram (ECoG), functional magnetic resonance imaging (fMRI), and functional near infrared spectroscopy (fNIRS) to build practical BCI systems [8]. EEG is a measurement of the electrical potential at a particular point on the scalp relative to another point on the head. It is believed that the EEG detected at scalp electrodes is generated by patches of cortical neurons with synchronous activity. A synchronous patch of cortex creates an electrical dipole that alternates in polarity as synaptic depolarization of the apical dendrites alternates with the consequent depolarization of the neuronal cell body. Each end of the dipole (dendrites and cell body) automatically rep...

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Electrophysiological correlates of anxious rumination

Cited by 88 publications

References 48 publications

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Phenomenological aspects of the cognitive rumination construct

Toward Affective Brain–Computer Interface: Fundamentals and Analysis of EEG‐Based Emotion Classification

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