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Evoked potentials and behavioral performance during different states of brain arousal
Background: Previous studies compared evoked potentials (EPs) between several sleep stages but only one uniform wake state. However, using electroencephalography (EEG), several arousal states can be distinguished before sleep onset. Recently, the Vigilance Algorithm Leipzig (VIGALL 2.0) has been developed, which automatically attributes one out of seven EEG-vigilance stages to each 1-s EEG segment, ranging from stage 0 (associated with cognitively active wakefulness), to stages A1, A2 and A3 (associated with relaxed wakefulness), to stages B1 and B2/3 (associated with drowsiness) up to stage C (indicating sleep onset). Applying VIGALL, we specified the effects of these finely differentiated EEG-vigilance stages (indicating arousal states) on EPs (P1, N1, P2, N300, MMN and P3) and behavioral performance. Subjects underwent an ignored and attended condition of a 2-h eyes-closed oddball-task. Final analysis included 43 subjects in the ignored and 51 subjects in the attended condition. First, the effect of brain arousal states on EPs and performance parameters were analyzed between EEG-vigilance stages A (i.e. A1, A2 and A3 combined), B1 and B2/3&C (i.e. B2/3 and C combined). Then, in a second step, the effects of the finely differentiated EEG-vigilance stages were further specified. Results: Comparing stages A versus B1 versus B2/3&C, a significant effect of EEG-vigilance stages on all behavio-ral parameters and all EPs, with exception of MMN and P3, was found. By applying VIGALL, a more detailed view of arousal effects on EP and performance was possible, such as the finding that the P2 showed no further significant increase in stages deeper than B1. Stage 0 did not differ from any of the A-stages. Within more fine-graded stages, such as the A-substages, EPs and performance only partially differed. However, these analyses were partly based on small sample sizes and future studies should take effort to get enough epochs of rare stages (such as A3 and C). Conclusions: A clear impact of arousal on EPs and behavioral performance was obtained, which emphasize the necessity to consider arousal effects when interpreting EPs.
Tonic and phasic EEG and behavioral changes induced by arousing feedback
NeuroImage, 2010
This study investigates brain dynamics and behavioral changes in response to arousing auditory signals presented to individuals experiencing momentary cognitive lapses during a sustained-attention task. Electroencephalographic (EEG) and behavioral data were simultaneously collected during virtual-reality (VR) based driving experiments, in which subjects were instructed to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel. 30-channel EEG data were analyzed by independent component analysis and the short-time Fourier transform. Across subjects and sessions, intermittent performance during drowsiness was accompanied by characteristic spectral augmentation or suppression in the alpha-and theta-band spectra of a bilateral occipital component, corresponding to brief periods of normal (wakeful) and hypnagogic (sleeping) awareness and behavior. Arousing auditory feedback was delivered to the subjects in half of the non-responded lane-deviation events, which immediately agitated subject's responses to the events. The improved behavioral performance was accompanied by concurrent spectral suppression in the theta-and alpha-bands of the bilateral occipital component. The effects of auditory feedback on spectral changes lasted 30 s or longer. The results of this study demonstrate the amount of cognitive state information that can be extracted from noninvasively recorded EEG data and the feasibility of online assessment and rectification of brain networks exhibiting characteristic dynamic patterns in response to momentary cognitive challenges.
Personality and the EEG: Arousal and emotional arousability
Personality and Individual Differences, 1992
Eysenck's theory asserts that low cortical arousal accompanies extraversion (or the subcomponent impulsivity). In Gray's theory, impulsivity is associated with high sensitivity to signals of reward, and anxiety with high sensitivity to signals of punishment. These hypotheses were tested by recording EEG signs of arousal and phasic arousability in emotional imagery, using 17 EEG channels and frequency analysis by Fourier transform. Three conditions were used: a neutral control task, and two emotional conditions involving imagery about pleasant and unpleasant personal memories. Forty subjects participated (23 men, 17 women; median age 23 years). Orthogonal personality dimensions of impulsivity and anxiety were derived from a joint analysis of the EPI and Karolinska Scales of Personality (KSP) questionnaires. The results showed, as expected. lower arousal, defined by more posterior theta activity, in impulsive subjects than in non-impulsives. These differences extended across all conditions. The EEG responses to the emotional conditions, in relation to the neutral one, consisted primarily of a right-lateralized frontal theta increase and changes in temporal beta activity (an increase in the positive condition, and a decrease in the negative one). These responses were expected to be amplified for impulsive subjects in positive emotion, and for anxious subjects in negative emotion. The right-sided frontal theta activity was stronger in high-anxious subjects than in low-anxious ones across all conditions, suggesting higher overall emotionality. For the temporal beta activity, the expected amplification of the response to negative emotion in the high anxiety group was confirmed, but the corresponding prediction for impulsives and positive emotion was not upheld. It is concluded that anxiety is related to EEG signs of heightened emotionality, especially in negative affect, and that impulsivity is associated with lowered arousal.
Clinical EEG and Neuroscience 1 -7
In part 1 of this article, we describe an approach and methodology that bridges 2 worlds: the internal, subjective experience of emotions and thoughts, and the external world of brain electrical activity. Using a novel event-related brain activation imaging method, we demonstrate that within single trials, short-term mental processes, on the order of 100 ms, can be clearly related to observed brain activation in controlled experiments. We use an ipsative assessment validation process that combines selfreport with real-time EEG recordings to provide a combined picture of both the mental and the brain activity, during short-term reactions, emotions, and decisions regarding controlled information. Part 2 provides a detailed description of the emerging emotional decision-making model.
Neuroscience and Behavioral Physiology, 2004
The cognitive approach to studies of the emotional sphere in humans has developed the so-called dimensionality or component theory of emotions, based on results obtained by factor analysis of verbal assessments of emotional experiences (For example, [37, 40]). According to this theory, the nature of emotional experience is determined by two main dimensions-valence/sign (positive/negative, pleasant/unpleasant) and activation (calm/excited). Emotions of different signs can be accompanied by either high (joy, fear) or low (satisfaction, melancholy) activation. Current psychophysiological studies of the affective sphere in humans have take account of this demarcation [10, 11, 24, 30, 33, and others]. However, the cortical mechanisms of interactions between different aspects of activation and valence are to a large extent unknown, mainly because of the inadequate amount of study devoted to each of these dimensions. Data from EEG studies of emotions in humans have provided evidence that "affective" interhemisphere asymmetry is characterized by significant heterogeneity in the anteroposterior direction [1, 5, 8, 10, 11, 15, 33]. The anterior areas of the cortex of the left and right hemispheres are associated predominantly with the valence dimension of emotion [16], while the posterior (especially of the right hemisphere) are associated with the processes of emotional activation regardless of valence [22, 24, 30]. However, to date it remains unclear how the characteristics of the interhemisphere distribution of EEG frequencyamplitude characteristics depend on the activated dimensions of the emotional signal, and this also applies to the
Sleep problems and emotional correlates in the EEG
2020
The need for sleep differs among people, with the average healthy adult needing about 7-9 hours of sleep every night in order to function in his/her day to day life. Research has shown a link between sleep and negative perception of angry facial expressions, suggesting that sleep is essential for emotional processing. We examine the research question, how sleep affects emotional correlates in the electroencephalogram (EEG). Motivated by the importance of sleep to people's emotional stability, we examine the research question: How does sleep affect emotional correlates in the EEG. We hypothesize that sleep affects how people process emotional pictures. We expect that people who slept less than six hours will show a lower reactivity of the brain to pictures with emotional content. We recorded EEG from 119 participants, while they looked at emotional pictures from the OASIS database. The pictures were grouped into those of positive, neutral and negative valence. The BIS (Bergen Insomnia Scale) questionnaire was used to screen for potential sleep problems of the participants. We also asked how many hours the participants slept the night before. They answered six questions about their sleep habits and we calculated the BIS score. We defined participants who scored at least 3 on at least one of the first four questions of the BIS and at least 3 on at least one of the last two questions of the BIS as suffering from insomnia. We considered two groups; one with participants who slept 6 hours or less in the night before the testing and one where the participants slept 7 hours or more in the night before the testing. The Fast Fourier Transform (FFT) of the EEG was computed. We extracted band power in the alpha frequency range (8-12 Hz). The IBM SPSS statistics software was used for all statistical analysis. A generalized linear model for repeated measures was used with two within subject factors (for the variables "valence" and "region"), that each had 3 levels, and a between subject for the variable "hours of sleep" or "insomnia". For insomnia and sleep deprivation the main effect of region, and the main effect of valence were found significant, but there was no significant effect for hours of sleep or insomnia. There was an interaction between region and valence, but no evidence of interactions between region and hours of sleep or insomnia, valence and hours of sleep or insomnia, and valence and hours of sleep or insomnia, and region. We did not manage to confirm our hypothesis, but found Sleep and emotions ii that emotional valence differently activates diverse brain regions. The main limitations of this research were the following: • We only measured hours of sleep for one night. • The hours of sleep were subjectively reported. • The research took place during the sunnier months of the year in Iceland. We hope that this thesis will stimulate more thorough research on this topic to gain a better understanding of the interaction of sleep and emotional state.
International Journal of Psychophysiology, 1996
In previous papers we proposed that an increase in delta EEG activity during mental tasks might be related to an increase in subjects' attention to internal processing, In this paper we have made a narrow band analysis to detect those EBG frequencies that change selectively during the performance of a mental task that requires attention to internal processing. Two different experiments were performed: (1) a difficult mental calculation task and a control stimulus with the same physical characteristics as the arithmetical symbols were presented in random order; (2) the, Stemberg paradigm for the analysis of short term memory using a memory set of 5 or 3 digits was also presented in random order. Referential recordings to linked ears were obtained in all leads of the lo/20 system. In the ,first experiment, the increase of power from 1.56 to 5.46 Hz was observed only during the performance of the task and not during the control condition. In the Stemberg paradigm, the increase of power from 1.56 to 3.90 Hz was greater during the difficult than during the easy condition. l!hese results support our hypothesis that an increase in delta activity may be related to attention to internal processing during the performance of a mental task. postulated the existence of two kinds, of' behavioral inhibition, both represented by slow waves in the EEG. 'Class I inhibition' would refer to a gross inactivation of an entire excitatory process; resulting in a relaxed, less active state, as in sleep. 'Class II inhibition' would selectively suppress inappropriate or non-relevant neural activity during the performance of a mental task.
Spectrum-weighted EEG frequency ("brain-rate") as a quantitative indicator of mental arousal
PubMed, 2005
A concept of brain-rate is introduced, defining it as the weighted mean frequency of the EEG spectrum. In analogue to the blood pressure, heart-rate and temperature, used as standard preliminary indicators of corresponding general bodily activations, it is proposed to use the brain-rate as a preliminary indicator of general mental activation (mental arousal) level. In addition, along with the more specific few-band biofeedback parameters (theta-beta ratio, relative beta ratio, etc.), the brain-rate could be effectively used as a general multiband biofeedback parameter.
Neuroscience & Biobehavioral Reviews, 2011
At present there is no direct brain measure of basic emotional dynamics from the human brain. EEG provides non-invasive approaches for monitoring brain electrical activity to emotional stimuli. Event-related desynchronization/synchronization (ERD/ERS) analysis, based on power shifts in specific frequency bands, has some potential as a method for differentiating responses to basic emotions as measured during brief presentations of affective stimuli. Although there appears to be fairly consistent theta ERS in frontal regions of the brain during the earliest phases of processing affective auditory stimuli, the patterns do not readily distinguish between specific emotions. To date it has not been possible to consistently differentiate brain responses to emotion-specific affective states or stimuli, and some evidence to suggests the theta ERS more likely measures general arousal processes rather than yielding veridical indices of specific emotional states. Perhaps cortical EEG patterns will never be able to be used to distinguish discrete emotional states from the surface of the brain. The implications and limitations of such approaches for understanding human emotions are discussed.