Time-dependent cortical asymmetries induced by emotional arousal: EEG analysis of event-related synchronization and desynchronization in individually defined frequency bands

Афтанас, Л. И.; Varlamov, Anton; Павлов, С. В.; Makhnev, V P; Reva, N.V.

doi:10.1016/s0167-8760(01)00194-5

Cited by 190 publications

(144 citation statements)

References 49 publications

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“…[56,57] For emotionally arousing stimuli, increases in theta band power have been reported over frontal [53,62] and parietal regions. [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.…”

Section: Frequency-domain Correlatesmentioning

confidence: 87%

“…[51] Congruously, increases of delta band power have been reported in response to more arousing stimuli. [52][53][54] The theta rhythm (4 to 8 Hz) has been observed during a number of cognitive processes, and its most prominent form, fronto-medial theta, is believed to originate from limbic and associated structures in the medial prefrontal cortex. [55][56][57][58] These theta oscillations subserve central executive function, integrating affective and cognitive sources of information, as necessary in working memory tasks [59,60] as well as in action monitoring.…”

Section: Frequency-domain Correlatesmentioning

confidence: 99%

See 1 more Smart Citation

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: 87%

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

show abstract

“…A common method had been used to evoke the distinct emotions from subjects by presenting the emotional pictures with corresponding content [17][18][19][20]. The whole experiment was designed to induct emotion within the valence and arousal space shown in Figure 3.…”

Section: Experiments Settingmentioning

confidence: 99%

“…It is established through previous studies [9][10][11][12] that the desired results from original frequency are difficult to achieve due to some noise artifacts and existence of other unknown signal patterns. We considered five frequency bands in our research, such as delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-50 Hz), also denoted as , , ,…”

Section: Introductionmentioning

confidence: 99%

Exploration of Prominent Frequency Wave in EEG Signals from Brain Sensors Network

Mehmood

Lee

2015

International Journal of Distributed Sensor Networks

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We investigated the signals regularity of electroencephalography (EEG) channels separately and determined the energy of selected frequency waves, such as, , , , , and . The goal of this research is to identify the prominent frequency band from selected frequencies. We recorded the EEG signal data of 30 controlled subjects with 18 EEG channels. These subjects are all males with an average age of 24 years. Emotional stimuli related to different emotions were presented to each of selected candidates. EEG data were extracted and further processed for artifact removal, filtering, epoch selection and averaging of the signals. We designed and tested our method for exploring the frequency waves of all EEG channels. We also employed the Hjorth parameters to measure the signal regularity in time and frequency domain. The detailed physiological response of human subjects is also presented in this paper. Our results showed that the energy level of delta wave is mostly high in all cases.

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“…Decreases in the CC in low-frequency ranges are probably related to activation of the emotiogenic cerebral zones and motivation-related cerebral structures in the course of such a motor act. The functional asymmetry of the hemispheres rather clearly increases under the influence of a few psychoemotional factors, and the effect of such factors is reflected to a great extent in changes of exactly relatively low-frequency components of the EEG spectrum [15,[26][27][28][29][30].…”

Section: Discussionmentioning

confidence: 99%

Modifications of EEG Related to Repeated Static Grasp Efforts Developed by the Hand in Humans

et al. 2005

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The characteristics of EEG recorded before, in the course, and after three sequential static grasp efforts developed by the right hand were analyzed in 14 healthy volunteers of both sexes, 19 to 56 years old. The grasps were 3 min long, and intervals between the sequential trials were 5 min long. The mean intensity was, as a rule, the greatest in the first trial and decreased, because of fatigue, in the second and third trials. In the course of the grasp effort, significant (P ≤ 0.05) or close to significant decreases in the spectral power were observed in all trials within the alpha2 subrange in some frontal, central, and central/temporal leads. This reaction of desynchronization of alpha2 oscillations probably reflects activation of the sensorimotor cortical zones. Within the above period, the power of alpha2 oscillations increased in the occipital leads. In a few leads of the frontal group, the powers of delta, theta1, theta2, and alpha1 oscillations increased significantly (P < 0.05) in the course of the first trial. Such reaction was not observed in the second and third trials, while the powers of delta and alpha1 ranges increased in the occipital sites (P < 0.05). The powers of delta and theta oscillations increased, as compared with the control values, after the second and third grasps. Such specific EEG modifications in sequential trials can be related to the actions of a novelty factor in the first trial and a fatigue factor in the subsequent trials. The coherence coefficient (CC) of alpha and beta oscillations for symmetric leads usually increased in the course of the grasp. This effect reached a significance level (P < 0.05) for alpha2 oscillations in central, posterotemporal, parietal, and occipital pairs. The CC for beta2 oscillations increased in both temporal lead pairs. A drop in the interhemisphere coherence was more typical of the delta and theta1 ranges. Therefore, changes in the CC values show that the structure of interhemisphere interaction undergoes modifications with the development of the grasp static efforts.

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Time-dependent cortical asymmetries induced by emotional arousal: EEG analysis of event-related synchronization and desynchronization in individually defined frequency bands

Cited by 190 publications

References 49 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

Exploration of Prominent Frequency Wave in EEG Signals from Brain Sensors Network

Modifications of EEG Related to Repeated Static Grasp Efforts Developed by the Hand in Humans

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