Non-linear dynamic complexity of the human EEG during evoked emotions

Афтанас, Л. И.; Lotova, Natalia V; Koshkarov, Vladimir I.; Makhnev, Victor P; Mordvintsev, Yuri N.; Popov, S. P.

doi:10.1016/s0167-8760(97)00067-6

Cited by 80 publications

(59 citation statements)

References 42 publications

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“…The more speci®c assumption that the left hemisphere would produce more gamma activity when positive emotional material is to be processed, however, can clearly be rejected on the basis of the present results. The few studies which analyzed higher frequency components showed ± in line with the present ®ndings ± higher activity for positive valence at right hemisphere temporal electrodes (Tucker, 1981;Tucker and Dawson, 1984;Ray and Cole, 1985;Aftanas et al, 1998). As discussed in the introduction, few studies exist which have analyzed the behavior in higher EEG frequency bands.…”

Section: Discussionsupporting

confidence: 88%

Processing of affective pictures modulates right-hemispheric gamma band EEG activity

Müller

Keil

Gruber

et al. 1999

Clinical Neurophysiology

252

132

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The present study was designed to test differential hemispheric activation induced by emotional stimuli in the gamma band range (30±90 Hz). Subjects viewed slides with differing emotional content (from the International Affective Picture System). A signi®cant valence by hemisphere interaction emerged in the gamma band from 30±50 Hz. Other bands, including alpha and beta, did not show such an interaction. Previous hypotheses suggested that the left hemisphere is more involved in positive affective processing as compared to the right hemisphere, while the latter dominates during negative emotions. Contrary to this expectation, the 30±50 Hz band showed relatively more power for negative valence over the left temporal region as compared to the right and a laterality shift towards the right hemisphere for positive valence. In addition, emotional processing enhanced gamma band power at right frontal electrodes regardless of the particular valence as compared to processing neutral pictures. The extended distribution of speci®c activity in the gamma band may be the signature of cell assemblies with members in limbic, temporal and frontal neocortical structures that differ in spatial distribution depending on the particular type of emotional processing. q

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Section: Discussionsupporting

confidence: 88%

Processing of affective pictures modulates right-hemispheric gamma band EEG activity

Müller

Keil

Gruber

et al. 1999

Clinical Neurophysiology

252

132

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“…However, the abundance of studies showing the association of brain rhythms with affective responses suggests that aBCI can make use of time-domain, but especially frequencydomain, characteristics of EEG signals to detect affective states. Moreover, further neurophysiological indicators of affective states have been explored, such as functional connectivity between brain regions [98][99][100] or measures of signal complexity [101] that may help to better describe and differentiate affective states. In the next 72 C. Mühl et al…”

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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“…It is a nonparametric method, which uses no prior information about the model used to generate the time series [14]. Applied to a brain recordings, NLF may quantify the loss of memory of past brain states [15]. This measure has been used to study the behaviour of brain signals during different psychophysiological experiments [16].…”

Section: Nonlinear Forecasting (Nlf)mentioning

confidence: 99%

“…Aftanas et al. [15] tested the EEG predictability during evoked emotions, finding that negative evoked emotions showed better predictability than neutral or positive ones. Additionally, EEG recordings from subjects with depression disorders [18] and with Creutzfeldt-Jakob disease [17] have been analyzed with NLF.…”

Section: Nonlinear Forecasting (Nlf)mentioning

confidence: 99%

“…Additionally, EEG recordings from subjects with depression disorders [18] and with Creutzfeldt-Jakob disease [17] have been analyzed with NLF. The NLF algorithm is as follows [14,15]: 1) The data set (with size n) is divided into two parts of equal size. The first one (y 1 ,y 2 ,…,y n/2 ) is used as a learning set to build a model of the system dynamic, and the second one (x 1 ,x 2 ,…,x n/2 ) is a test set which is compared to the predicted series [16].…”

Section: Nonlinear Forecasting (Nlf)mentioning

confidence: 99%

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Nonlinear forecasting measurement of magnetoencephalogram recordings from Alzheimer's disease patients

Gómez

Hornero

Mediavilla

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The goal of this study was to analyze the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using a nonlinear forecasting measure. It is a nonparametric method to quantify the predictability of time series. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 15 patients with probable AD and 15 elderly control subjects. Stationary epochs of 5 seconds (848 points, sample frequency of 169.55 Hz) were selected. Our results showed that AD patients' MEGs were more predictable than controls' recordings. Additionally, an accuracy of 76.7% (80.0% sensitivity; 73.3% specificity) was reached using a receiver operating characteristic curve. These preliminary results suggest the usefulness of nonlinear forecasting to gain a better understanding of dynamical processes underlying the MEG recording.

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Non-linear dynamic complexity of the human EEG during evoked emotions

Cited by 80 publications

References 42 publications

Processing of affective pictures modulates right-hemispheric gamma band EEG activity

Processing of affective pictures modulates right-hemispheric gamma band EEG activity

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

Nonlinear forecasting measurement of magnetoencephalogram recordings from Alzheimer's disease patients

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