Article

Spectral Structure and Brain Mapping of Human Alpha Activities in Different Arousal States

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Abstract

In a study with 10 young, healthy subjects, alpha activities were studied in three different arousal states: eyes closed in relaxed wakefulness (EC), drowsiness (DR), and REM sleep. The alpha band was divided into three subdivisions (slow, middle, and fast) which were analyzed separately for each state. The results showed a different spectral composition of alpha band according to the physiological state of the subject. Slow alpha seemed to be independent of the arousal state, whereas middle alpha showed a difference between REM and the other states. The fast-alpha subdivision appears mainly as a waking EEG component because of the increased power displayed only in wakefulness and lower and highly stable values for DR and REM. Scalp distribution of alpha activity was slightly different in each state: from occipital to central regions in EC, this topography was extended to fronto-polar areas in DR, with a contribution from occipital to frontal regions in REM sleep. These results provide evidence for an alpha power modulation and a different scalp distribution according to the cerebral arousal state.

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... Dans la littérature, de nombreux travaux utilisent des entraînements visant surtoutà augmenter la proportion de ces ondes en région occipitale pour réduire l'anxiété -notamment chez des personnes souffrant de troubles de l'anxiété généralisée, de troubles obsessionnels compulsifs ou de troubles phobiques ou chez des individus soumisà de fortes pressions comme desétudiants ou des sportifs de haut niveau (Hardt et Kamiya, 1978;Rice et al., 1993;Moore, 2000;Singer, 2004;Sandhu et al., 2007). Frost et al. (1978) n'ontégalement pas trouvé de différence significative dans la pro- De plus, plusieursétudes ont mis en avant que les oscillations α basses fréquences sont plus liées a des processus d'attention alors que celles de hautes fréquences, sont, au contraire liéesà des processus cognitifs (Shaw, 2003) et sont en particulier amplifiées en région occipito-pariétales dans lesétats de relaxationéveillée (Cantero et al., 1999). Kasamatsu et Hirai (1969) (Baumeister et al., 2008;Pavlenko et al., 2009;Saeed et al., 2015). ...
... Or, les exercices de NF proposés par l'application melomind TM sont supposés améliorer la gestion du stress età augmenter l'état relaxé. Dans ce contexte, cette augmentation de la puissance α haut va donc dans le sens des résultats deCantero et al. (1999) etKasamatsu et Hirai (1969), indiquant une augmentation des ondes α haut durant unétat relaxé. En revanche, aucun effet court terme n'a puêtre détecté concernant les autres métriques EEG. ...
... De plus, la puissance α bas en phases de repos post-sessions a tendanceà etre plusélevée pour le groupe contrôle que le groupe NF. Ainsi, toujours avec la supposition que les exercices de NF proposés par melomind TM permettent unétat relaxé, ces résultats vont dans le sens deKasamatsu et Hirai (1969) et Cantero et al. (1999 indiquant une augmentation des ondes α haut dans unétat de relaxation. Nous pouvons donc suggérer que le groupe NF procèdeà une réorganisation cérébrale induisant unétat de plus en plus relaxé, contrairement au groupe contrôle, dont les participants peuvent ressentir une certaine frustration ou incompréhension duesà une décorrélation entre leur ressenti et l'indice de NF présenté. ...
Thesis
Cette thèse porte sur la conception, l’implémentation et l’évaluation d’un système de neurofeedback EEG portable, d’aide à la gestion du stress, à destination du grand public. Un tel système permet aux utilisateurs d’apprendre à moduler leurs états mentaux par des phénomènes de plasticité cérébrale. Cependant, plusieurs facteurs peuvent compliquer cet apprentissage, comme un plus faible rapport signal sur bruit de l'EEG acquis par des électrodes sèches, la contamination par des artefacts ou encore la définition de paramètres pertinents à partir des signaux EEG. Afin d’optimiser ce retour neuronal, ma thèse propose d’abord une méthode statistique permettant de s’assurer de la qualité des signaux EEG acquis, ainsi qu’une méthode corrective d’artefacts, afin de pouvoir extraire une mesure pertinente de l’activité EEG reflétant le niveau de stress ou de relaxation de l’individu. Le développement d’un indice de neurofeedback pertinent et adapté à l’utilisateur est également proposé. A la suite de la constitution algorithmique d’un tel système, les caractéristiques d'apprentissage par neurofeedback ont pu être étudiées. En particulier, je montre qu'un apprentissage intersession semble se mettre en place et que chez les sujets stressés, des changements cérébraux s'opèrent dans la bande alpha durant les phases de repos. Finalement, par ces aspects méthodologiques, d’intégration logicielle et d’analyse longitudinale, cette thèse constitue les briques fondamentales d’un système de recommandation automatique adapté à l’utilisateur. Un tel système permettrait un suivi personnel des utilisateurs afin de leur proposer une stratégie préventive pour la gestion du stress.
... Electroencephalography (EEG) signals have provided new visions to the neocortical dynamic functions at a macroscopic level. EEG is analyzed to different frequency band, including delta (0.5-4 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (>30 Hz) by signal processing methods. One of the favorite EEG frequency bands is the alpha, which is detected either by EEG or magnetoencephalography in the range of 8-13 Hz [1,2] and predominately comes from the occipital zone during wakeful relaxation with closed eyes. ...
... This study focused on characterization of alpha variability in terms of both amplitude and frequency in Psych-Insomnia that has been largely ignored in past studies except a study only in the frequency variability in insomnia generally. [6] Considering that the brain cognitive, behavioral, and physiological function change EEG amplitude and frequency, three hypotheses were adjusted H1: both the alpha amplitude and frequency oscillate around a state-dependent set point; H2: the set-point amplitude and frequency evoke the neurophysiological state; [20,21] and H3: the amplitude and the frequency variability may be different between Psych-Insomnia and normal sleepers. ...
... This means that the probability of a neurophysiological state is higher in Psych-Insomnia individuals. [20,21] In a study performed on posttraumatic stress disorder patients, alpha peak frequency was evaluated. The results revealed which frequency of alpha peak was higher. ...
Article
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Individuals with psychophysiological insomnia (Psych-Insomnia) would show raised cortical arousal through their initiating sleep. Frequent changes in the alpha activity can be indicative of visual cortical activation, even without visual stimulation or retinal input. Therefore, we aimed to investigate alpha-wave characteristics in Psych-Insomnia before and after sleep onset. In a case-control study, 11 individuals with Psych-Insomnia (age: 44.00 ± 13.27) and 11 age-, sex-, and body mass index-matched healthy individuals (age: 41.64 ± 15.89) were recruited for this study. An overnight polysomnography monitoring was performed. Alpha characteristics were calculated from wake before sleep onsets (WBSOs), wake after sleep onset, rapid eye movement, and nonrapid eye movement in the both groups. They include the alpha power and alpha frequency and their variability in the central region. In the WBSO, alpha activity and variability were higher in the Psych-Insomnia individuals compared to healthy individuals. In both groups, alpha frequency variability was observed at approximately 1 Hz. Alpha-wave synchronization in Psych-Insomnia individuals was higher than the group with normal sleep. Individuals with Psych-Insomnia have a lot of imagination in the wake before sleep, which can be caused by stress, everyday concerns, and daily concerns.
... Arousal levels in humans have generally been associated with EEG alpha activity (Bollimunta, Chen, Schroeder, & Ding, 2008;Bollimunta, Mo, Schroeder, & Ding, 2011;Cantero, Atienza, Gómez, & Salas, 1999a;Figueredo-Rodríguez, Del Río-Portilla, Sánchez-Romero, Pérez-Ortiz, & Corsi-Cabrera, 2009;Hasan & Broughton, 1994), which is defined as an EEG oscillation between 8.0 and 13.0 Hz with amplitude usually below 50 mV and localized over posterior regions of the head (Buzsaki, 2006;Niedermeyer, 2005;Shaw, 2003). Spontaneous alpha activity appears during wakefulness mainly with eyes closed and under conditions of relaxation and mental inactivity (Alvarez, Pascual-Marqui, & Valdes-Sosa, 1990;Brazier, 1968;Cantero et al., 1999a;Inouye, Shinosaki, Yagasaki, & Shimizu, 1986;Rodin & Rodin, 1995). ...
... Arousal levels in humans have generally been associated with EEG alpha activity (Bollimunta, Chen, Schroeder, & Ding, 2008;Bollimunta, Mo, Schroeder, & Ding, 2011;Cantero, Atienza, Gómez, & Salas, 1999a;Figueredo-Rodríguez, Del Río-Portilla, Sánchez-Romero, Pérez-Ortiz, & Corsi-Cabrera, 2009;Hasan & Broughton, 1994), which is defined as an EEG oscillation between 8.0 and 13.0 Hz with amplitude usually below 50 mV and localized over posterior regions of the head (Buzsaki, 2006;Niedermeyer, 2005;Shaw, 2003). Spontaneous alpha activity appears during wakefulness mainly with eyes closed and under conditions of relaxation and mental inactivity (Alvarez, Pascual-Marqui, & Valdes-Sosa, 1990;Brazier, 1968;Cantero et al., 1999a;Inouye, Shinosaki, Yagasaki, & Shimizu, 1986;Rodin & Rodin, 1995). However, there is evidence that increases in alpha band activity during sleep may be considered an indicator of arousal and cortical activation (American Sleep Disorders Association, 1992;Cantero, Atienza, Salas, & Gómez, 1999b;Figueredo-Rodríguez et al., 2009;Hasan & Broughton, 1994;Pivik & Harman, 1995;Tyson, Ogilvie, & Hunt, 1984). ...
... show high values during wakefulness that decrease during N-REMS and fall to minimum values during REMS (Cantero et al., 1999a(Cantero et al., , 1999b(Cantero et al., , 1999cCantero, Atienza, & Salas, 2000). Both spectral power and the h characteristics of alpha activity have been determined for humans, a primate species that sleeps in a horizontal posture and presents muscular atonia during REMS. ...
Article
There is evidence that some animal species have developed physiological and behavioral mechanisms to monitor potential predatory threats during rapid eye movement sleep (REMS). Nevertheless, it has not been reported in arboreal primates. The present study analyzed the sleeping postures, as well as the electromyographic and electroencephalographic (EEG) activities during three conditions: REMS, non-REMS (N-REMS), and wakefulness in spider monkeys. The study included six monkeys, whose EEGs were recorded at the O1-O2, C3, C4, F3, and F4 derivations to analyze relative power (RP) and interhemispheric, intrahemispheric, frontoposterior, and central-posterior coherence of frequency bands, which has been considered an index of arousal states. The bands analyzed were theta (4.0-7.0 Hz), alpha1 (8.0-10.5 Hz), alpha2 (11.0-13.5 Hz), and beta (14.0-30.0 Hz). Spider monkeys adopt a vertical posture during sleep, and in REMS a lack of muscular atonia was observed. The RP of the alpha bands at O1-O2 was higher during REMS than that during wakefulness, N-REMS1, and N-REMS2. At the C3 derivation, the RP of alpha1 was higher during REMS than that during N-REMS2. The RP of both alpha bands at the F4 derivation was higher during REMS than that during wakefulness, whereas REMS was characterized by a higher coherence between the F3 and O1-O2 derivations of the alpha2 band. These prevalences and the higher coherence of alpha bands during REMS could represent a correlate of behavioral traits and activated cortical areas related to a possible arousal state in spider monkeys while sleeping.
... Currently, the α band heterogeneity is beyond doubt. It was demonstrated by many authors [6][7][8][9][10][11][12][13][14][15][16][17]. ...
... As a result, some authors distinguish between two components of the α rhythm and demonstrate different reactions of the lowand high-frequency subranges [2,15,16]. Other authors consider three subranges to be independent: the low-, medium-, and high-frequency α rhythm [12,17]. When the whole α range was divided into eight equal parts, the functional differentiation of the α activity in narrow generalized bands was revealed. ...
... Our recent studies also revealed a high heterogeneity and individual specificity of the fine structure of the EEG spectrum in α range but only under conditions of sensorimotor behavior [6] or intermittent sensory stimulation with varying frequency [8,10]. At the same time, it is known that the fine spectral structure of the α rhythm largely depends on the functional state of a subject [13,17]. Moreover, contradictions can be explained by the fact that authors use different frequency resolutions of EEG spectra. ...
Article
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In order to study the fine structure of the EEG frequency band in the resting state, two-minute segments of baseline EEG derived from symmetrical frontal, central, parietal, and occipital areas of 16 male subjects were analyzed by Fast Fourier Transform by successively increasing the analysis epoch from 2 to 20 s with 0.5-s steps. It was shown that the EEG band at rest is a heterogeneous complex of two independent components that differ in the frequency, on average, by 0.9 0.2 Hz. These components can exist in the Fourier spectrum either simultaneously or independently of each other, which is determined by experimentally-induced and natural shifts in the functional state of the system. Doublets of these components can be located on the frequency axis in any part of the range. This finding does not allow the a prioridivision of the band into the classical 1, 2, etc. subbands.
... Since the ARAS projects both to the cortex and the thalamus, we hypothesized that ARAS-mediated inputs would interfere with the cortico-thalamic feedback loop to support changes in oscillatory neural activity across both cortical and thalamic populations. We assumed that this tuning mediated by ARAS -and hence arousal -would occur when eyes are opening/closing, through increase in attention (Bollimunta et al. 2011) or during drowsiness or REM-sleep, as seen experimentally (Cantero et al. 1999). Since the ARAS projects to supragranular cortical layers (Koval'zon 2016), we follow the line of thought of Pisarchik et al. 2019 and hypothesise that cortical γ−activity is noise-induced and the ARAS contributes to this cortical noise. ...
... This limit cycle exists for low arousal (eyes closed) and vanishes for high arousal (eyes open). This is consistent with an experimental study on the impact of arousal on the power and frequency of occipital α−activity (Cantero et al. 1999). ...
Article
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Arousal results in widespread activation of brain areas to increase their response in task and behavior relevant ways. Mediated by the Ascending Reticular Arousal System (ARAS), arousal-dependent inputs interact with neural circuitry to shape their dynamics. In the occipital cortex, such inputs may trigger shifts between dominant oscillations, where α\alpha activity is replaced by γ\gamma activity, or vice versa. A salient example of this are spectral power alternations observed while eyes are opened and/or closed. These transitions closely follow fluctuations in arousal, suggesting a common origin. To better understand the mechanisms at play, we developed and analyzed a computational model composed of two modules: a thalamocortical feedback circuit coupled with a superficial cortical network. Upon activation by noise-like inputs originating from the ARAS, our model is able to demonstrate that noise-driven non-linear interactions mediate transitions in dominant peak frequency, resulting in the simultaneous suppression of α\alpha limit cycle activity and the emergence of γ\gamma oscillations through coherence resonance. Reduction in input provoked the reverse effect - leading to anticorrelated transitions between α\alpha and γ\gamma power. Taken together, these results shed a new light on how arousal shapes oscillatory brain activity.
... In a separate stream of research founded by Hans Berger in the 1920s [12,13], a rhythmic brain wave at about 10 Hz was shown to transpire from the scalp of human participants and reacted to such events as eye opening, involuntary attention to sudden startle from gunshot sound, other auditory, visual, olfactive, tactile, and pain stimuli, voluntary concentration, anesthesia, medications, and a variety of clinical conditions [12][13][14][15][16][17]. This brain wave is a dominant activity in human waking electroencephalogram (EEG) [18]. After the controversy of its cerebral origin was finally settled [15], what came to be known as the alpha wave [14] rose as one of the most studied electrophysiological phenomenon, with firmly established correlation to attentive processes [19][20][21][22]. ...
... This hypothesis retained the idea of a unitary alpha that equally suppresses exogenous and endogenous distractors, but firm evidence of complete anatomical and dynamical equivalence remains unfulfilled. Second, studies of oscillatory power that paid close attention to the timing of oscillatory processes have suggested a complex temporal organization of posterior 10 Hz activities with different sub-bands of alpha playing a role at different moments [18,24,[49][50][51], see also [31]. These studies beg for finer-grained models for the relation between local rhythmic activity and attentional processes. ...
Article
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With their salient power distribution and privileged timescale for cognition and behavior, brainwaves within the 10 Hz band are special in human waking electroencephalography (EEG). From the inception of electroencephalographic technology, the contribution of alpha rhythm to attention is well-known: Its amplitude increases when visual attention wanes or visual input is removed. However, alpha is not alone in the 10 Hz frequency band. A number of other 10 Hz neuromarkers have function and topography clearly distinct from alpha. In small pilot studies, an activity that we named xi was found over left centroparietal scalp regions when subjects held their attention to spatially peripheral locations while maintaining their gaze centrally ("looking from the corner of the eyes"). I outline several potential functions for xi as a putative neuromarker of covert attention distinct from alpha. I review methodological aids to test and validate their functional role. They emphasize high spectral resolution, sufficient spatial resolution to provide topographical separation, and an acute attention to dynamics that caters to neuromarkers' transiency.
... Alpha activity is a meaningful constituent of REM sleep. Its widespread distribution during tonic periods (i.e., in the absence of rapid eye movements) is reduced during phasic periods (i.e., upon bursts of rapid eye movements), mainly in posterior recording areas (Cantero, Atienza, Gomez, & Salas, 1999;Cantero, Atienza, & Salas, 2000). This suppression of Alpha activity has been interpreted as reflecting the processing of dream content (Cantero et al., 1999(Cantero et al., , 2000Esposito et al., 2004;Jouny, Chapoto, & Merica, 2000). ...
... Its widespread distribution during tonic periods (i.e., in the absence of rapid eye movements) is reduced during phasic periods (i.e., upon bursts of rapid eye movements), mainly in posterior recording areas (Cantero, Atienza, Gomez, & Salas, 1999;Cantero, Atienza, & Salas, 2000). This suppression of Alpha activity has been interpreted as reflecting the processing of dream content (Cantero et al., 1999(Cantero et al., , 2000Esposito et al., 2004;Jouny, Chapoto, & Merica, 2000). In a single-case study of a typical 36-year-old male, Hong et al. (1996) showed that Alpha suppression is also apparent in nonposterior recording sites during dreaming, as they reported a negative correlation between expressive/receptive language in dream reports and Alpha activity over C3 and P3 electrodes during intermingled periods of tonic and phasic REM sleep. ...
Article
Functional interregional neural coupling was measured as EEG coherence during REM sleep, a state of endogenous cortical activation, in 9 adult autistic individuals (21.174.0 years) and 13 typically developed controls (21.574.3 years) monitored for two consecutive nights in a sleep laboratory. Spectral analysis was performed on 60 s of artefact-free EEG samples distributed equally throughout the first four REM sleep periods of the second night. EEG coherence was calculated for six frequency bands (delta, theta, alpha, sigma, beta, and total spectrum) using a 22-electrode montage. The magnitude of coherence function was computed for intra-and interhemispheric pairs of recording sites. Results were compared by Multivariate Analysis of Variance (MANOVA). Each time the autistic group showed a greater EEG coherence than the controls; it involved intrahemispheric communication among the left visual cortex (O1) and other regions either close to or distant from the occipital cortex. In contrast, lower coherence values involved frontal electrodes in the right hemisphere. No significant differences between groups were found for interhemispheric EEG coherence. These results show that the analysis of EEG coherence during REM sleep can disclose patterns of cortical connectivity that can be reduced or increased in adults with autism compared to typically developed individuals, depending of the cortical areas studied. Superior coherence involving visual perceptual areas in autism is consistent with an enhanced role of perception in autistic brain organization.
... In contrast, durations to sleep onset in multiple sleep latency tests follow distributions with exponential tails (Erkki Kronholm, personal communication, 18 July 2015;Kronholm et al., 1995). Moreover, Cantero et al. (1999) found the spectral composition of the alpha band to be state-dependent when comparing states of wake, drowsiness and alpha bursts in rapid eye movement (REM) sleep (for review, see Cantero et al., 2002). ...
... This analysis focused on properties of alpha frequency and its variability that have been largely disregarded in previous studies (except Jin et al., 2006). Based on the assumption that a rhythm-generation process actively controls the frequency of alpha-wave activity in the brain, three hypotheses were formulated: (H1) the alpha frequency fluctuates around a state-dependent set point; (H2) the set-point frequency reflects the neurophysiological state (Cantero et al., 1999;Nunez et al., 1978); (H3) the frequency variability indicates pathological processes. ...
Article
Appearances of alpha waves in the sleep electrencephalogram indicate physiological, brief states of awakening that lie in between wakefulness and sleep. These microstates may also cause the loss in sleep quality experienced by individuals suffering from insomnia. To distinguish such pathological awakenings from physiological ones, differences in alpha-wave characteristics between transient awakening and wakefulness observed before the onset of sleep were studied. In polysomnographic datasets of sleep-healthy participants (n = 18) and patients with insomnia (n = 10), alpha waves were extracted from the relaxed, wake state before sleep onset, wake after sleep-onset periods and arousals of sleep. In these, alpha frequency and variability were determined as the median and standard deviation of inverse peak-to-peak intervals. Before sleep onset, patients with insomnia showed a decreased alpha variability compared with healthy participants (P < 0.05). After sleep onset, both groups showed patterns of decreased alpha frequency that was lower for wake after sleep-onset periods of shorter duration. For patients with insomnia, alpha variability increased for short wake after sleep-onset periods. Major differences between the two groups were encountered during arousal. In particular, the alpha frequency in patients with insomnia rebounded to wake levels, while the frequency in healthy participants remained at the reduced level of short wake after sleep-onset periods. Reductions in alpha frequency during wake after sleep-onset periods may be related to the microstate between sleep and wakefulness that was described for such brief awakenings. Reduced alpha variability before sleep may indicate a dysfunction of the alpha generation mechanism in insomnia. Alpha characteristics may also prove valuable in the study of other sleep and attention disorders.
... Alpha power has been shown to be related to attention\arousal in various studies with greater alpha power indicating lower levels of arousal (Cantero et al., 1999). The split plot ANOVA revealed a significant interaction of pre-stimulus alpha power averaged over electrodes Oz, O1, O2, POz, P3, P4 between perspective and task instruction [F (1, 28) = 4.79, p = 0.037] with the contrast Bird > Ego, meaning lower power in the alpha band for the ego-compared to the bird perspective, being significant only for the morally aversive "shoot"-instruction [t (14) = −2.99, ...
... The post-response differences likely have altered the participants' cognitive state toward a greater expectancy of aversive stimuli in the "shoot" task, where the animation of the target falling down was seen either from close range or from the distance, depending on perspective condition. An abundance of evidence suggests that attention and arousal have a crucial influence on alpha power (Shaw, 1996;Cantero et al., 1999;Keil et al., 2001;Simons et al., 2003). Likewise, the wording of the task instruction might have diminished the morally aversive content of the task and influenced pre-stimulus alpha power. ...
Article
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In a shooting video game we investigated whether increased distance reduces moral conflict. We measured and analyzed the event related potential (ERP), including the N2 component, which has previously been linked to cognitive conflict from competing decision tendencies. In a modified Go/No-go task designed to trigger moral conflict participants had to shoot suddenly appearing human like avatars in a virtual reality scene. The scene was seen either from an ego perspective with targets appearing directly in front of the participant or from a bird's view, where targets were seen from above and more distant. To control for low level visual features, we added a visually identical control condition, where the instruction to “shoot” was replaced by an instruction to “detect.” ERP waveforms showed differences between the two tasks as early as in the N1 time-range, with higher N1 amplitudes for the close perspective in the “shoot” task. Additionally, we found that pre-stimulus alpha power was significantly decreased in the ego, compared to the bird's view only for the “shoot” but not for the “detect” task. In the N2 time window, we observed main amplitude effects for response (No-go > Go) and distance (ego > bird perspective) but no interaction with task type (shoot vs. detect). We argue that the pre-stimulus and N1 effects can be explained by reduced attention and arousal in the distance condition when people are instructed to “shoot.” These results indicate a reduced moral engagement for increased distance. The lack of interaction in the N2 across tasks suggests that at that time point response execution dominates. We discuss potential implications for real life shooting situations, especially considering recent developments in drone shootings which are per definition of a distant view.
... Alpha waves contribute to RISE in different states of vigilance. Anterior and posterior alpha patterns of the characteristic waking-alpha rhythm are seemingly interconnected via corticocortical synapses and modulated thalamic inputs [3]; this could explain the large extension of synchronization peaks in RISE with waking-alpha activity as well as the results of EEG coherence studies [32,[52][53][54]. REM-specific alpha waves might be due to a distinct pattern of corticocortical coupling [3,52], but they contribute to RISE the same way as alpha waves in waking and N1. ...
... Anterior and posterior alpha patterns of the characteristic waking-alpha rhythm are seemingly interconnected via corticocortical synapses and modulated thalamic inputs [3]; this could explain the large extension of synchronization peaks in RISE with waking-alpha activity as well as the results of EEG coherence studies [32,[52][53][54]. REM-specific alpha waves might be due to a distinct pattern of corticocortical coupling [3,52], but they contribute to RISE the same way as alpha waves in waking and N1. ...
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Pointwise transinformation (PTI) provides a quantitative nonlinear approach to spatiotemporal synchronization patterns of the rhythms of coupled cortical oscillators. We applied PTI to the waking and sleep EEGs of 21 healthy sleepers; we calculated the mean levels and distances of synchronized episodes and estimated the dominant frequency shift from unsynchronized to synchronized EEG segments by spectral analysis. Recurrent EEG synchronization appeared and ceased abruptly in the anterior, central, and temporal derivations; in the posterior derivations it appeared more fluctuating. This temporal dynamics of synchronization remained stable throughout all states of vigilance, while the dominant frequencies of synchronized phases changed markedly. Mean synchronization had high frontal and occipital levels and low central and midtemporal levels. Thus, a fundamental coupling pattern with recurrent increases of synchronization in the EEG ("RISE") seems to exist during the brain's resting state. The generators of RISE could be coupled corticocortical neuronal assemblies which might be modulated by subcortical structures. RISE designates the recurrence of transiently synchronized cortical microstates that are independent of specific EEG waves, the spectral content of the EEG, and especially the current state of vigilance. Therefore, it might be suited for EEG analysis in clinical situations without stable vigilance.
... Alpha oscillations during REM sleep might reflect relatively short periods of sleep instability (micro-arousals) that facilitate the connection between the sleeping brain and the external environment (Cantero, Atienza, & Salas, 2000;Halász, 1998;Halász, Terzano, Parrino, & Bódizs, 2004). Alpha oscillations were shown to be modulated differently in the different states of alertness (Cantero et al., 2002): while higher alpha components are dominant during wakefulness, REM sleep is characterized by the preponderance of slower (7.5-10.5 Hz) alpha oscillations in healthy subjects (Cantero, Atienza, Gómez, & Salas, 1999). Moreover, there are clear differences in the topographical distribution of the alpha activity between wakefulness and REM sleep. ...
... Moreover, there are clear differences in the topographical distribution of the alpha activity between wakefulness and REM sleep. Posterior dominance of alpha power is characteristic of relaxed wakefulness, whereas in REM the distribution seems to be more homogeneous (Cantero et al., 1999(Cantero et al., , 2002. Therefore, the increased high alpha activity peaking at posterior locations in the REM periods of NMs may reflect a "hybrid state" with the occurrence of wake-type alpha oscillations during REM sleep. ...
Article
Although a growing body of research indicates that frequent nightmares are related to impaired sleep regulation, the pathophysiology of nightmare disorder is far from being fully understood. We examined the relative spectral power values for NREM and REM sleep separately in 19 individuals with nightmare disorder and 21 healthy controls, based on polysomnographic recordings of the second nights' laboratory sleep. Nightmare subjects compared to controls exhibited increased relative high alpha (10-14.5Hz) and fronto-central increases in high delta (3-4Hz) power during REM sleep, and a trend of increased fronto-central low alpha (7.75-9Hz) power in NREM sleep. These differences were independent of the confounding effects of waking emotional distress. High REM alpha and low NREM alpha powers were strongly related in nightmare but not in control subjects. The topographical distribution and spectral components of REM alpha activity suggest that nightmare disordered subjects are characterized by wake-like electroencephalographic features during REM sleep.
... In fact, the presence of a prominent alpha activity is the most common factor that leads to a classification of REM sleep epochs as wakefulness or stage 1. In a previous series of studies, we established the electrophysiological features of wakefulness alpha rhythm, drowsiness alpha activity, and REM-alpha bursts (Cantero, Atienza, Gómez, & Salas, 1999;Cantero et al., 1999a,b). These studies tested whether brain activation state modulates the generation of alpha in human subjects. ...
... Drowsiness-alpha activity showed a higher power than REM-alpha bursts only for the component ranges from 9.7 to 10.9 Hz (four spectral bins), whereas the higher power in REM-alpha bursts with respect to the drowsiness alpha activity was observed for spectral components between 7.8 and 8.6 Hz (three spectral bins). However, this distinction could not be detected when broader subdivisions of alpha band (slow, middle, and fast) were taken into account (Cantero et al., 1999). Consequently, from these results one can conclude that both drowsiness alpha at sleep onset and REM-alpha bursts have a different spectral microstructure and, probably, a different neuronal configuration, and/or firing rate may be involved in their generation. ...
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High-resolution frequency methods were used to describe the spectral and topographic microstructure of human spontaneous alpha activity in the drowsiness (DR) period at sleep onset and during REM sleep. EEG, electrooculograph (EOGH), and EMG/H measurements were obtained during sleep in 10 healthy volunteer Ss (aged 19–25 yrs). Spectral microstructure of alpha activity during DR showed a significant maximum power with respect to REM-alpha bursts for the components in the 9.7–10.9 Hz range, whereas REM-alpha bursts reached their maximum statistical differentiation from the sleep onset alpha activity at the components between 7.8 and 8.6 Hz. Furthermore, the maximum energy over occipital regions appeared in a different spectral component in each brain activation state, namely, 10.1 Hz in drowsiness and 8.6 Hz in REM sleep. These results provide quantitative information for differentiating the drowsiness alpha activity and REM-alpha by studying their microstructural properties. On the other hand, these data suggest that the spectral microstructure of alpha activity during sleep onset and REM sleep could be a useful index to implement in automatic classification algorithms in order to improve the differentiation between the 2 brain states. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
... Furthermore, abnormalities in α oscillations in the frontal cortex during WAKE and NREM sleep may result from abnormal transmission of neural signals within corticobasal ganglia circuits. 49 However, the mechanism of power changes in different frequency bands of cortical neurons remains unclear. It is anticipated that applying novel imaging techniques and multichannel EEG recordings will provide a better understanding of the intricate interplay between typical oscillatory and synchronized activities within the cortico-basal ganglia motor circuit and muscle. ...
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Aims Sleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time‐consuming and labor‐intensive. In this study, we performed an automatic sleep–wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats. Methods The study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep–wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD. Results Features extracted from occipital ECoG signals were more suitable for constructing sleep–wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep–wake states of rats (average Cohen's kappa: 0.79). Conclusion This study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep–wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.
... A fast Fourier transform was then used to calculate the EEG power spectra with a frequency resolution of 0.5 Hz using the Welch method. The frequency band was divided into alpha1 (8-10 Hz) and alpha2 (10)(11)(12). The P3/4 and O1/2 electrodes were used for analysis because the 8-12 Hz signals are prominent in the parieto-occipital region 48 . ...
Preprint
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Although researchers have widely explored the relationship between EEG and heart rate variability (HRV), the results are not always consistent mainly due to the variety of tasks. In particular, several factors, such as mental fatigue and sleepiness, can affect the alpha power, which makes it difficult to obtain a direct relationship between alpha and heart rate activities. This study investigates the brain–heart interplay that is consistently observed in various mental states: listening to music and resting. To eliminate the indirect effects of mental states on alpha power, subjective fatigue and sleepiness in the resting condition and their emotional valence and arousal in the music condition were measured. A partial correlation analysis in the music condition, which excluded the indirect effects of emotional valence and arousal level, showed a positive correlation between the power of the occipital alpha2 component (10-12 Hz) and nHF, a measure of parasympathetic activity. In a similar vein, a partial correlation analysis in the resting condition, excluding subjective fatigue and sleepiness effects, showed a positive correlation between the occipital alpha2 component and nHF. These results indicate a brain–heart interplay that is frequently observed in various subjective states and that still exists after eliminating the effects of other variables.
... Besides (a) being expressed along a health-pathology continuum and (b) being transdiagnostic, tonic vigilance is reflected in the characteristics of the qEEG phenotype [157,523,[540][541][542][543][544][545][546][547]. Additionally, vigilance-relevant qEEG characteristics are ~80% heritable [548]. ...
Article
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Many practicing clinicians are time-poor and are unaware of the accumulated neuroscience developments. Additionally, given the conservative nature of their field, key insights and findings trickle through into the mainstream clinical zeitgeist rather slowly. Over many decades, clinical, systemic, and cognitive neuroscience have produced a large and diverse body of evidence for the potential utility of brain activity (measured by electroencephalogram—EEG) for neurology and psychiatry. Unfortunately, these data are enormous and essential information often gets buried, leaving many researchers stuck with outdated paradigms. Additionally, the lack of a conceptual and unifying theoretical framework, which can bind diverse facts and relate them in a meaningful way, makes the whole situation even more complex. To contribute to the systematization of essential data (from the authors’ point of view), we present an overview of important findings in the fields of electrophysiology and clinical, systemic, and cognitive neuroscience and provide a general theoretical–conceptual framework that is important for any application of EEG signal analysis in neuropsychopathology. In this context, we intentionally omit detailed descriptions of EEG characteristics associated with neuropsychopathology as irrelevant to this theoretical–conceptual review.
... There is known sleep EEG activity in these frequency ranges. In addition to general observations of alpha incursion into NREM (e.g., arousals, alpha-delta sleep), which is typically viewed as pathological [87][88][89][90][91][92][93], alpha has been observed in healthy controls across all stages of sleep [87]. In particular, peri-REM alpha transient bursting has been shown to occur at lower alpha frequencies in controls [40,94], and alpha microstates have been observed during NREM [95][96][97]. ...
Article
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Transient oscillatory events in the sleep electroencephalogram represent short-term coordinated network activity. Of particular importance, sleep spindles are transient oscillatory events associated with memory consolidation, which are altered in aging and in several psychiatric and neurodegenerative disorders. Spindle identification, however, currently contains implicit assumptions derived from what waveforms were historically easiest to discern by eye, and has recently been shown to select only a high-amplitude subset of transient events. Moreover, spindle activity is typically averaged across a sleep stage, collapsing continuous dynamics into discrete states. What information can be gained by expanding our view of transient oscillatory events and their dynamics? In this paper, we develop a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events across a wide frequency range using continuous dynamics. We demonstrate that the complex temporal evolution of transient events during sleep is highly stereotyped when viewed as a function of slow oscillation power (an objective, continuous metric of depth-of-sleep) and phase (a correlate of cortical up/down states). This two-fold power-phase representation has large intersubject variability—even within healthy controls—yet strong night-to-night stability for individuals, suggesting a robust basis for phenotyping. As a clinical application, we then analyze patients with schizophrenia, confirming established spindle (12-15 Hz) deficits as well as identifying novel differences in transient NREM events in low alpha (7-10Hz) and theta (4-6Hz) ranges. Overall, these results offer an expanded view of transient activity, describing a broad class of events with properties varying continuously across spatial, temporal, and phase-coupling dimensions.
... There have been suggestions that alpha might serve as a feedback signal from the cortex that could modulate the neural excitability in the thalamo-recipient layer [34]. While the fluctuation in alpha power could be driven by top-down task demands, they can also occur due to multiple factors such as changes in arousal [74]. ...
Article
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Background Anomalous phantom visual perceptions coupled to an aversion and discomfort to some visual patterns (especially grating in mid-range spatial frequency) have been associated with the hyperresponsiveness in migraine patients. Previous literature has found fluctuations of alpha oscillation (8-14 Hz) over the visual cortex to be associated with the gating of the visual stream. In the current study, we examined whether alpha activity was differentially modulated in migraineurs in anticipation of an upcoming stimulus as well as post-stimulus periods. Methods We used EEG to examine the brain activity in a group of 28 migraineurs (17 with aura /11 without) and 29 non-migraineurs and compared their alpha power in the pre/post-stimulus period relative to the onset of stripped gratings. Results Overall, we found that migraineurs had significantly less alpha power prior to the onset of the stimulus relative to controls. Moreover, migraineurs had significantly greater post-stimulus alpha suppression (i.e event-related desynchronization) induced by the grating in 3 cycles per degree at the 2nd half of the experiment. Conclusions These findings, taken together, provide strong support for the presence of the hyperresponsiveness of the visual cortex of migraine sufferers. We speculate that it could be the consequence of impaired perceptual learning driven by the dysfunction of GABAergic inhibitory mechanism.
... Concerning the spatial distribution of alpha-band ERpow, the increase of ERpow was dominantly mapped in the occipitoparietal areas relevant to the cortical areas of DMN. Changes in alpha rhythmic activity related to the arousal level were dominant in the occipito-parietal areas (Ota et al., 1996;Cantero et al., 1999). In occipital-parietal areas, the alpha power increased to levels similar to a relaxed state, when a task required isolation from the external world so that the attentional direction was kept internalized (Magosso et al., 2019). ...
Article
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Dysfunctional attentional control is observed in patients with mental disorders. However, there is no established neurophysiological method to assess attention in such patients. We showed a discrepancy in alpha-band power in the tasks that evoked internal and external attention event-related alpha-band power changes in healthy subjects during self-reflection (SR) and working memory (WM) tasks in a preliminary study. In this study, we aimed at elucidating event-related alpha-band power changes in healthy subjects during the tasks, addressing the shortcomings of the previous study. Sixteen healthy volunteers were examined for the event-related power (ERpow) change during the tasks. The results demonstrated the discrepancy of alpha-band ERpow at 8, 10, and 12 Hz in the parieto-occipital area between the WM and SR tasks for a period between a target stimulus and a command stimulus, where a participant switched to internal attention from external attention according to the SR task and remained at external attention according to the WM task. The results suggest that alpha-band ERpow in this area is associated with the direction of attention in response to cognitive stimuli, indicating that the findings of ERpow during the two tasks would potentially aid in the clarification of the pathophysiology of the dysfunctional change in attention in patients with psychiatric disorders.
... For example, a-band activity has been associated with memory (Bonnefond and Jensen, 2012;Klimesch, 1999;Palva and Palva, 2007), attention (Benwell et al., 2017Foxe and Snyder, 2011), and arousal Cantero et al., 1999;Sadaghiani et al., 2010), whereas b-activity is believed to play a role in sensorimotor functions (Pfurtscheller et al., 1996) and the maintenance of top-down attention (Buschman and Miller, 2007;Engel and Fries, 2010). These findings have led to suggestions that oscillations are computationally relevant for neuronal synchrony/ communication and higher-order cognition (Canolty and Knight, 2010). ...
Article
Rhythmic neural activity has been proposed to play a fundamental role in cognition. Both healthy and pathological aging are characterized by frequency-specific changes in oscillatory activity. However, the cognitive relevance of these changes across the spectrum from normal to pathological aging remains unknown. We examined electroencephalography (EEG) correlates of cognitive function in healthy aging and 2 of the most prominent and debilitating age-related disorders: type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD). Relative to healthy controls (HC), patients with AD were impaired on nearly every cognitive measure, whereas patients with T2DM performed worse mainly on learning and memory tests. A continuum of alterations in resting-state EEG was associated with pathological aging, generally characterized by reduced alpha (α) and beta (β) power (AD < T2DM < HC) and increased delta (δ) and theta (θ) power (AD > T2DM > HC), with some variations across different brain regions. There were also reductions in the frequency and power density of the posterior dominant rhythm in AD. The ratio of (α + β)/(δ + θ) was specifically associated with cognitive function in a domain- and diagnosis-specific manner. The results thus captured both similarities and differences in the pathophysiology of cerebral oscillations in T2DM and AD. Overall, pathological brain aging is marked by a shift in oscillatory power from higher to lower frequencies, which can be captured by a single cognitively relevant measure of the ratio of (α + β) over (δ + θ) power.
... 7,8,9,10,11,12 There are a number of reviews concerning the connection of brain rhythms and functional state of the body in various general and local pathological processes. 4,13,14 We believe that application of the proposed method in combination with the main diagnostic techniques should extend to the interpretation of the clinical features of any pathological process and it would increase the level of diagnostics of the disease, and determine the best strategy for treatment and prevention of periodontitis. ...
... Sigma power was observed to be maximal centrally shortly after sleep onset (Broughton andHasan, 1995; De Gennaro et al., 2001;Tinguely et al., 2006). The socalled anteriorisation of alpha oscillations during the sleep-wake transition is an indicator of drowsiness (Davis et al., 1937;Hori, 1985;Wright et al., 1995;Broughton and Hasan, 1995;Cantero et al., 1999;De Gennaro et al., 2001 andTinguely et al., 2006) and found entry into clinically widely used scoring manuals (Rechtschaffen, 1968;AASM, 2007). Beta power appears to be maximal at frontal derivations also during drowsiness (Tinguely et al., 2006). ...
Article
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Objectives: We investigated blood oxygenation level-dependent (BOLD) brain activity changes in wakefulness and light sleep and in relation to those associated with the posterior alpha rhythm, the most prominent feature of the clinical EEG. Studies have reported different sets of brain regions changing their oxygen consumption with waxing and waning alpha oscillations. Here, we hypothesize that these dissimilar activity patterns reflect different wakefulness-dependent brain states. Methods: We recorded BOLD signal changes and electroencephalography (EEG) simultaneously in 149 subjects at rest. Based on American Academy of Sleep Medicine criteria, we selected subjects exhibiting wakefulness or light sleep (N1). We identified brain regions in which BOLD signal changes correlated with (i) clinical sleep stages, (ii) alpha band power and (iii) a multispectral EEG index, respectively. Results: During light sleep, we found increased BOLD activity in parieto-occipital regions. In wakefulness compared to light sleep, we revealed BOLD signal increases in the thalamus. The multispectral EEG-index revealed hippocampal activity changes in light sleep not reported before. Conclusion: Changes in alpha oscillations reflect different brain states associated with different levels of wakefulness and thalamic activity. We can link the previously described parieto-occipital pattern to drowsiness. Additionally, in that stage, we identify hippocampal activity fluctuations. Significance: Thalamic activity varies with early changes of wakefulness, which is important to consider in resting state experiments. The EEG-indexed activation of the hippocampus during light sleep suggests that memory encoding might already take place during this early stage of sleep.
... In our adaptation of WoW, called WoW, we make use of the power in the alpha band over parietal regions. According to Cantero et al. [22], high alpha levels in the parietal lobe indicate a state of relaxed alertness. Also, Barry et al. "confirm the arousal link between alpha and electrodermal activity" [23]. ...
Article
Brain-computer interfaces (BCIs) are not only being developed to aid disabled individuals with motor substitution, motor recovery, and novel communication possibilities, but also as a modality for healthy users in entertainment and gaming. This study investigates whether the incorporation of a BCI in the popular game World of Warcraft (WoW) has effects on the user experience. A BCI control channel based on parietal alpha band power is used to control the shape and function of the avatar in the game. In the experiment, participants , a mix of experienced and inexperienced WoW players, played with and without the use of BCI in a within-subjects design. Participants themselves could indicate when they wanted to stop playing. Actual and estimated duration was recorded and questionnaires on presence and control were administered. Afterwards, oral interviews were taken. No difference in actual duration was found between conditions. Results indicate that the difference between estimated and actual duration was not related to user experience but was person specific. When using a BCI, control and involvement were rated lower. But BCI control did not significantly decrease fun. During interviews, experienced players stated that they saw potential in the application of BCIs in games with complex interfaces such as WoW. This study suggests that BCI as an additional control can be as much fun and natural to use as keyboard/mouse control, even if the amount of control is limited. Index Terms-Brain-computer interface (BCI), games, human factors, presence, user experience.
... Alpha power is influenced by attention and arousal [45][46][47][48] . The interindividual nature of late alpha power seen in our data (Fig. 7A,B) may be through an elicited difference of attention. ...
Article
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As technology in Artificial Intelligence has developed, the question of how to program driverless cars to respond to an emergency has arisen. It was recently shown that approval of the consequential behavior of driverless cars varied with the number of lives saved and showed interindividual differences, with approval increasing alongside the number of lives saved. In the present study, interindividual differences in individualized moral decision-making at both the behavioral and neural level were investigated using EEG. It was found that alpha event-related spectral perturbation (ERSP) and delta/theta phase-locking – intertrial coherence (ITC) and phase-locking value (PLV) – play a central role in mediating interindividual differences in Moral decision-making. In addition, very late alpha activity differences between individualized and shared stimuli, and delta/theta ITC, where shown to be closely related to reaction time and subjectively perceived emotional distress. This demonstrates that interindividual differences in Moral decision-making are mediated neuronally by various markers – late alpha ERSP, and delta/theta ITC - as well as psychologically by reaction time and perceived emotional distress. Our data show, for the first time, how and according to which neuronal and behavioral measures interindividual differences in Moral dilemmas can be measured.
... For example, rather than reflecting the inhibition of underlying brain areas and related functions (i.e., affect regulation), it may reflect enhanced arousal instead (83). However, it has been shown that the topography of lower alpha band (specifically, 7-9 Hz) is similar across wakefulness and REM sleep, which has been suggested to indicate a common mechanism of generation independent of the physiological state (83,84). Because our results pertained mostly to the lower alpha band, FAA was highly correlated across wakefulness and REM sleep, and both REM sleep and evening waking FAA were similarly related to dream anger, a similar mechanism and function of FAA in affective processes across wakefulness and REM sleep dreaming can be inferred. ...
Preprint
Affective experiences are central not only to our waking life but also to rapid eye movement (REM) sleep dreams. While the neural correlates of REM sleep are well documented, we know little about the neural correlates of dream affect. Frontal alpha asymmetry (FAA) is considered a marker of affective states and traits as well as affect regulation in the waking state. Here, we explored whether FAA during REM sleep and during evening resting wakefulness is related to affective experiences in REM sleep dreams. Electroencephalography (EEG) recordings were obtained from participants who spent two nights in the sleep laboratory. Participants were awakened five minutes after the onset of every REM stage after which they provided a dream report and rated their dream affect. Two-minute pre-awakening EEG preceding each dream report were analyzed. Additionally, eight minutes of evening pre-sleep and morning post-sleep EEG were recorded during resting wakefulness. Mean spectral power in the alpha band (8-13 Hz) and corresponding FAA were calculated over the frontal (F4-F3) sites. Results showed that FAA during REM sleep, and during evening resting wakefulness, predicted ratings of dream anger. This suggests that individuals with lower right frontal activity (reflected in increased alpha power) may be less able to regulate (i.e., inhibit) strong affective states, such as anger, in dreams. Additionally, FAA was positively correlated across wakefulness and REM sleep. These findings imply that FAA may serve as a neural correlate of state and trait affect regulation not only in the waking but also in the dreaming state. --> Link to preprint: https://osf.io/bfs28/
... On the basis of previous studies (Cantero et al. 1999;Grützner et al. 2013;Tang et al. 2007), the spectra of EEG signals were separated into five frequency bands, namely delta (0.5-3.5 Hz), theta (3.5-8 Hz), alpha (range 8-13 Hz; low, 8-9 Hz; middle, 9-11 Hz; high, 11-13 Hz), beta (range 13-30 Hz; low, 13-16 Hz; middle, 16-20 Hz; high, 20-30 Hz), and gamma (range 30-100 Hz; low, 30-60 Hz; typical, 40 Hz; high [ 60 Hz). Only low gamma bands ranging between 30 and 50 Hz were investigated in this study because high gamma bands rarely appear in EEG results. ...
Article
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Understanding the cognitive processes used in creative practices is essential to design research. In this study, electroencephalography was applied to investigate the brain activations of visual designers when they responded to various types of word stimuli during design thinking. Thirty visual designers were recruited, with the top third and bottom third of the participants divided into high-creativity (HC) and low-creativity (LC) groups. The word stimuli used in this study were two short poems, adjectives with similar meanings, and adjectives with opposing meanings. The derived results are outlined as follows: (i) The brain activations of the designers increased in the frontal and right temporal regions and decreased in the right prefrontal region; (ii) the negative association between the right temporal and middle frontal regions was notable; (iii) the differences in activations caused by distinct word stimuli varied between HC and LC designers; (iv) the spectral power in the middle frontal region of HC designers was lower than that of LC designers during the short love poem task; (v) the spectral power in the bilateral temporal regions of HC designers was higher than that of LC designers during the short autumn poem task; (vi) the spectral power in the frontoparietal region of HC designers was lower than that of LC designers during the similar concept task; and (vii) the spectral power in the frontoparietal and left frontotemporal regions of HC designers was higher than that of LC designers during the opposing concept task.
... According to prior research [34][35][36], the spectra of EEG signals were separated into five frequency bands, namely delta (0.5-3.5 Hz), theta (3. ...
Article
This study aimed to examine the brain activations of designers during visual association and identify the differences in brain activations caused by distinct visual stimulus types among designers with different creativity levels. Twenty-one professional production designers were recruited and divided into three groups. The top third and bottom third of the participants (7 for each) were divided into high creativity (HC) and low creativity (LC) groups for neural comparison analyses. The derived results are outlined as follows: (i) The brain activations of the production designers notably increased in the prefrontal and parietal regions during visual association; (ii) the spectral power of most HC designers was lower than that of the LC designers; (iii) realist art stimulation evoked strong activation in the anterior ventral regions, whereas abstract art stimulation primarily activated the posterior regions; (iv) the differences in brain activations between the HC and LC designers resulting from realist art stimulation were generally larger than were those resulting from abstract art stimulation; and (v) the brain activations of the HC designers resulting from abstract art stimulation were stronger than were those resulting from realist art stimulation, whereas an opposite trend was observed in the LC designers. Few studies to date have empirically explored the relationship between the creativity levels of designers and their visual association. Enhancing the creative performance of designers should feature among the primary goals of the design industry and design education. This study elucidates a novel approach in this critical research area, although further studies are necessary.
... As increases in power in the EEG theta range in monotonous activity are associated with changes in the functional state of the brain towards reduced activation [2], the results reported in [24] indicate that that brain activity also decreases in sleep deprivation. Considering data showing that the alpha rhythm of the upper subrange appears as an EEG component mainly in the state of waking [14], reductions in power in this subrange during sleep deprivation point to a decreased level of waking in this state. Rhythms in the beta range are regarded as intracortical, and are generally associated with cognitive processes [27] and with visual-auditory and sensory-motor integration [28,46]. ...
Article
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We suggest here that impairments to learning, memory, and attention following sleep deprivation are based on the following changes to the composition of neuromodulators and intracellular processes, affecting synaptic plasticity and the functioning of the hippocampal formation, as well as cortex-basal ganglia-thalamus- cortex circuits. Firstly, the Ca2+ concentration and expression of NMDA receptors decrease, preventing potentiation of the efficiency of synaptic transmission in the cortex and hippocampus. Secondly, the orexin concentration decreases, also degrading conditions for potentiation and weakening the transmission of excitation in the trisynaptic pathway via the hippocampus. The formation of neural representations of “object–place” associations deteriorates. Thirdly, the dopamine concentration decreases, though the adenosine level and the number of A1 receptors in the striatum increase, degrading the functioning of the cortex-basal ganglia-thalamus-cortex circuit. This weakens voluntary and involuntary attention, degrades the processing of sensory information, and impairs motor responses. Neuron excitation in the reinforcement circuits also decreases, weakening the motivational significance of stimuli.
... How changes in alpha activity are experienced by the user, depends on the location where the activity is measured. According to Cantero et al. [2], high alpha activity measured over the parietal lobe is related to a relaxed alertness. This seems a beneficial state of mind for gaming, especially compared to drowsiness, which is said to be measured frontally. ...
Conference Paper
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BCIs are traditionally conceived as a way to control apparatus, an interface that allows you to "act on" external devices as a form of input control. We propose an alternative use of BCIs, that of monitoring users as an additional intelligent sensor to enrich traditional means of interaction. This vision is what we consider to be a grand challenge in the field of multimodal interaction. In this article, this challenge is introduced, related to existing work, and illustrated using some best practices and the contributions it has received.
... In our adaptation of WoW, called WoW, we make use of the power in the alpha band over parietal regions. According to Cantero et al. [22], high alpha levels in the parietal lobe indicate a state of relaxed alertness. Also, Barry et al. "confirm the arousal link between alpha and electrodermal activity" [23]. ...
... С другой стороны, это увеличение может говорить в пользу выдвинутой нами гипотезы о том, что в состоянии микросна уменьшает ся активация первичных зрительных обла стей коры в ответ на стимул и ослабляется внимание к этому стимулу. Если принять во внимание данные о том, что альфа ритм верхнего поддиапазона появляется в основ ном как компонента ЭЭГ в состоянии бодр ствования [9], наблюдавшееся нами в состоя нии микросна ослабление мощности в верх нем альфа поддиапазоне указывает на снижение уровня бодрствования. ...
Article
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We tested our earlier suggested hypothesis that one of mechanisms for failures of performance of behav ioural tasks during microsleep is a spontaneous generation of ponto geniculo occipital (PGO) waves that suppress transmission of visual information from the retina via lateral geniculate nucleus to primary visual cortical areas and the striatum, and therefore significantly impair visual perception and attention. Experiments were done during the nighttime. Monotonic testing during performance of the two alter native psychomotor test invoked participants into a state defined as a microsleep with open eyes. For each participant we made a comparative analysis of intensity of EEG spectrum during state of micros leep with open eyes when failure in test performance occurred and during accurate performance in wak ing state. Following trends in changes of EEG spectrum were found: increase in intensity of low alpha range, and decrease in intensity of high alpha and beta ranges. Changes in theta , low beta and gam ma ranges were differently directed. Taking into account the known from the literature data these changes specify decrease in activation of primary visual cortical areas. Revealed data could support our hypothesis concerning mechanism of visual motor disturbances during microsleep with open eyes.
... The MEG average power (Φ(0)) in alpha was significantly larger than that in other frequency bands over the temporal, parietal, and, especially, occipital regions in both experiments. Previous studies have also reported that cortical oscillations in the alpha frequency range showed a maximum power over occipital areas and progressively decreased towards anterior areas (Rodin and Rodin, 1995) independently of the brain state (Cantero et al., 1999). Generally, there is a certain degree of coherence between τ e and φ 1 . ...
Article
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Human cortical activities in regard to flickering lights with different regularities of luminance fluctuations were investigated. The regularities of luminance fluctuations were changed by utilizing a sinusoidal wave of 1-Hz frequency and band noises with different bandwidths centered on 1 Hz. A pair of the most- and less-preferred stimuli, which were selected according to the results of subjective preference tests, was presented to the subject via a single, green light-emitting diode (LED). Whole-head magnetoencephalography (MEG) signals in the theta, alpha, and beta ranges were analyzed by autocorrelation function (ACF). Significant results were found regarding the alpha activity over the left occipital region: the values of effective duration (τe) of the MEG signals in the alpha range were significantly larger for the most-preferred stimuli than those for the less-preferred stimuli. Given that the value of τe represents a repetitive feature contained within the signal, the results indicate that the stable rhythm of the alpha activity over the left occipital region persists longer for more-preferred regularity of a fluctuating light.
... Lehmann assumed that three stationary, semi-independent generators can account for the main features of alpha fields [14]. José Luis Cantero and et al. provided evidence for an alpha power modulation and a different scalp distribution according to the cerebral arousal state [15]. Basar E and et al. assumed that a 'diffuse and distributed alpha system' exists [16]. ...
Article
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The aim of this study is to propose a method for detecting α wave in EEG (electroencephalograph) and find the characteristics of EEG spatial distribution. We also investigated the difference of spatial characteristics between Zen-meditation practitioners (experimental group) and non-practitioners (control group). We firstly adopted wavelet transform to decompose EEG signals and reconstruct waves in each frequency band using wavelet coefficients. From the power ratio, we selected the candidates (normalized α-power vectors) for further spatial analysis. Fuzzy C-means based algorithm was applied to the normalized vectors to explore various brain spatial characteristics during meditation (or, at rest). Here we evaluated correlation coefficients to decide the number of clusters. From the results we found (1) the α power in the control group decreased dramatically but not in the experimental group, (2) after meditation, α power in the frontal area of meditators increased more than that of the control subjects (after resting-EEG recording). From the literatures, activating medial prefrontal cortex and anterior cingulated cortex during meditation may be the reason of increasing frontal α power.
... A number of complex physiological transitional phenomena between these two brain states were tracked with ever more sophisticated instrumentation and analysis techniques. Classical spectral analysis (Värri et al., 1992; Jung et al., 1997; Morikawa et al., 1997; Cantero et al., 1999; Cantero and Atienza, 2000; Bódizs et al., 2008) and nonlinear techniques (Accardo et al., 1997; Pereda et al., 1998 Pereda et al., , 1999 Kobayashi et al., 1999; Acharya et al., 2005; Šušmáková and Krakovská, 2008; Bojić et al., 2010 ) revealed that various brain regions differ electrophysiologically during the wake-to-sleep transition. Three groups of different but complementary results could be summarized based on the most frequently used methods: a) those using Fourier amplitude or power topography (Ota et al., 1996; Jung et al., 1997; Cantero and Atienza, 2000 ) where changes in the activity of separate cortical regions were measured; b) analyses which reveal strength of intracortical connections between pairs of EEG channels, such as correlation and coherence, (Tanaka et al., 1996; Cantero et al., 2002; Cover et al., 2004; Koenig et al., 2005); c) phase, synchronicity and time delay measurements which offer an insight into time-related electrophysiological events between different cortical regions (Govindan et al., 2005Govindan et al., , 2006 Jann et al., 2009). ...
Article
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Phases of alpha oscillations recorded by EEG were typically studied in the context of event or task related experiments, rarely during spontaneous alpha activity and in different brain states. During wake-to-drowsy transition they change unevenly, depending on the brain region. To explore their dynamics, we recorded ten adult healthy individuals in these two states. Alpha waves were treated as stable frequency and variable amplitude signals with one carrier frequency (CF). A method for calculating their CF phase shifts (CFPS) and CF phase potentials (CFPP) was developed and verified on surrogate signals as more accurate than phase shifts of Fourier components. Probability density estimate (PDE) of CFPS, CFPP and CF phase locking showed that frontal and fronto-temporal areas of the cortex underwent more extensive changes than posterior regions. The greatest differences were found between pairs of channels involving F7, F8, F3 and F4 (PDE of CFPS); F7, F8, T3 and T4 (CFPP); F7, F8, F3, F4, C3, C4 and T3 (decrease in CF phase locking). A topographic distribution of channels with above the average phase locking in the wake state revealed two separate regions occupying anterior and posterior brain areas (with intra regional and inter hemispheric connections). These regions merged and became mutually phase locked longitudinally in the drowsy state. Changes occurring primarily in the frontal and fronto-temporal regions correlated with an early decrease of alertness. Areas of increased phase locking might be correlated with topography of synchronous neuronal assemblies conceptualized within neural correlates of consciousness.
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Objective The severity of menopausal symptoms, despite being triggered by hormonal imbalance, does not directly correspond to hormone levels in the blood; thus, the level of unpleasantness is assessed using subjective questionnaires in clinical practice. To provide better treatments, alternative objective assessments have been anticipated to support medical interviews and subjective assessments. This study aimed to develop a new objective measurement for assessing unpleasantness. Methods Fourteen participants with menopausal symptoms and two age-matched participants who visited our outpatient section were enrolled. Resting-state brain activity was measured using magnetoencephalography. The level of unpleasantness of menopausal symptoms was measured using the Kupperman Kohnenki Shogai Index. The blood level of follicle-stimulating hormone and luteinizing hormone were also measured. Correlation analyses were performed between the oscillatory power of brain activity, index score, and hormone levels. Results The level of unpleasantness of menopausal symptoms was positively correlated with high-frequency oscillatory powers in the parietal and bordering cortices (alpha; P = 0.016, beta; P = 0.015, low gamma; P = 0.010). The follicle-stimulating hormone blood level was correlated with high-frequency oscillatory powers in the dorsal part of the cortex (beta; P = 0.008, beta; P = 0.005, low gamma; P = 0.017), whereas luteinizing hormone blood level was not correlated. Conclusion Resting-state brain activity can serve as an objective measurement of unpleasantness associated with menopausal symptoms, which aids the selection of appropriate treatment and monitors its outcome.
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Conference Paper
The aim of this paper is to report our preliminary results of investigating the α spatial properties in Zen-meditation EEG (electroencephalograph). Results of practitioners (experimental group) were compared with that of non-practitioners (control group). We firstly applied wavelet transform to decomposing multi-channel EEG signals and reconstructing various EEG rhythms using wavelet coefficients. From the power ratio, we selected the candidates (normalized α-power vectors) for further spatial analysis. Fuzzy C-means based algorithm was applied to the normalized vectors to explore various brain spatial characteristics during meditation (or, at rest). Here we evaluated correlation coefficients to decide the number of clusters. From the results we found (1) during meditation, the possessing ration of α power in the frontal area of meditators increased more than that of the control subjects (during relaxation with eyes closed). Contrarily, in the parietal area the possessing ratio is decreased in the experimental group but increased in the control group. (2) The ratio of non-α waves in the control group decreased dramatically during relaxation but not in the experimental group. From the literatures, activating medial prefrontal cortex and anterior cingulated cortex during meditation may be the reason of increasing frontal α power.
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Recently research into Brain-Computer Interfacing (BCI) applications for healthy users, such as games, has been initiated. But why would a healthy person use a still-unproven technology such as BCI for game interaction? BCI provides a combination of information and features that no other input modality can offer. But for general acceptance of this technology, usability and user experience will need to be taken into account when designing such systems. Therefore, this chapter gives an overview of the state of the art of BCI in games and discusses the consequences of applying knowledge from Human-Computer Interaction (HCI) to the design of BCI for games. The integration of HCI with BCI is illustrated by research examples and showcases, intended to take this promising technology out of the lab. Future research needs to move beyond feasibility tests, to prove that BCI is also applicable in realistic, real-world settings.
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In an intensive single-subject design, electroencephalographic (EEG) alpha power and receptive and expressive language in dreaming were studied in 12 dreams during rapid eye movement (REM) sleep on 12 separate nights. Bilateral EEG was recorded continuously from 21 sites and digitized. We used the Fast Fourier transformation (FFT) for power spectral analysis to measure EEG power in the alpha frequency range (8–12 Hz) at each of the EEG sites. The subject was awakened after about 14 minutes into the second REM period, and dream reports were collected. We scored the dream reports for expressive and receptive language. The lower the alpha power on the left sides of those homologous pairs that roughly correspond to Broca's (C3) or Wernicke's area (P3), the more expressive or receptive language in dream reports. The largest difference between the correlation of the left and that of the right homologous pair of regions was found in the central (C3, C4) area for expressive language and in the parietal (P3, P4) area for receptive language. Our finding suggests lateralized and localized cortical activation in relation to language in dreaming. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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There is evidence for two types of sleep spindle activity, one with a frequency of about 12 cycles/s (cps) and the other of about 14 cps. Visual examination indicates that both spindle types occur independently, whereby the 12-cps spindles are more pronounced in the frontal and the 14-cps spindles in the parietal region. The purpose of this paper is to provide more information about the exact topography of these patterns. First the occurrence of distinct signals in anterior and posterior brain regions was verified using pattern recognition techniques based on matched filtering. Thus the existence of two distinct sources of activity located in the frontal and parietal region of the brain, respectively, was demonstrated using EEG frequency mapping. Evaluation of sleep recordings showed high stability both in the frequency and location of the presumed spindle generators across sleep. Pharmacological effects of lormetazepam and zopiclone on both spindle types were investigated. Both substances enhanced the sleep spindle activity recorded from the frontal and parietal electrodes, but this increase was more pronounced in the parietal brain region.
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These experiments were undertaken to demonstrate that pure mental activity, thinking, increases the cerebral blood flow and that different types of thinking increase the regional cerebral blood flow (rCBF) in different cortical areas. As a first approach, thinking was defined as brain work in the form of operations on internal information, done by an awake subject. The rCBF was measured in 254 cortical regions in 11 subjects with the intracarotid 133Xe injection technique. In normal man, changes in the regional cortical metabolic rate of O2 leads to proportional changes in rCBF. One control study was taken with the subjects at rest. Then the rCBF was measured during three different simple algorithm tasks, each consisting of retrieval of a specific memory followed by a simple operation on the retrieved information. Once started, the information processing went on in the brain without any communication with the outside world. In 50-3 thinking, the subjects started with 50 and then, in their minds only, continuously subtracted 3 from the result. In jingle thinking the subjects internally jumped every second word in a nine-word circular jingle. In route-finding thinking the subjects imagined that they started at their front door and then walked alternatively to the left or the right each time they reached a corner. The rCBF increased only in homotypical cortical areas during thinking. The areas in the superior prefrontal cortex increased their rCBF equivalently during the three types of thinking. In the remaining parts of the prefrontal cortex there were multifocal increases of rCBF. The localizations and intensities of these rCBF increases depended on the type of internal operation occurring. The rCBF increased bilaterally in the angular cortex during 50-3 thinking. The rCBF increased in the right midtemporal cortex exclusively during jingle thinking. The intermediate and remote visual association areas, the superior occipital, posterior inferior temporal, and posterior superior parietal cortex, increased their rCBF exclusively during route-finding thinking. We observed no decreases in rCBF. All rCBF increases extended over a few square centimeters of the cortex. The activation of the superior prefrontal cortex was attributed to the organization of thinking. The activation of the angular cortex in 50-3 thinking was attributed to the retrieval of the numerical memory and memory for subtractions. The activation of the right midtemporal cortex was attributed to the retrieval of the nonverbal auditory memory.(ABSTRACT TRUNCATED AT 400 WORDS)
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Magnetic recording from five normal human adults demonstrates large 40-Hz coherent magnetic activity in the awake and in rapid-eye-movement (REM) sleep states that is very reduced during delta sleep (deep sleep characterized by delta waves in the electroencephalogram). This 40-Hz magnetic oscillation has been shown to be reset by sensory stimuli in the awake state. Such resetting is not observed during REM or delta sleep. The 40 Hz in REM sleep is characterized, as is that in the awake state, by a fronto-occipital phase shift over the head. This phase shift has a maximum duration of approximately 12-13 msec. Because 40-Hz oscillation is seen in wakefulness and in dreaming, we propose it to be a correlate of cognition, probably resultant from coherent 40-Hz resonance between thalamocortical-specific and nonspecific loops. Moreover, we proposed that the specific loops give the content of cognition, and a nonspecific loop gives the temporal binding required for the unity of cognitive experience.
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The spontaneous EEG, viewed as a series of momentary scalp field maps, shows stable map configurations (of periodically reversed polarity) for varying durations, and discontinuous changes of the configurations. For adaptive segmentation of map series into spatially stationary epochs, the maps at the times of maximal map relief are selected and spatially described by the 3wo locations of maximal and minimal (extreme) potentials; a segment ends if over time an extreme leaves its pre-set spatial window. Over 6 objects, the resting alpha EEG showed 210 msec mean segment duration; segments longer than 323 msec covered 50% of the total time; the most prominent segment class (1.5% of all classes) covered 20% of total time (prominence varied strongly over classes; not all possible classes occured). Spectral power and phase of averages of adaptive and pre-determined segments demonstrated the adequacy of the strategy, and the homegeneity of adaptive segment classes by their reduced within-class variance. It is suggested that different segment classes manifest different brain functional states exerting different effects on information processing. The spatially stationary segments might be basic building blocks of brain information processing, possibly operationalizing consciousness time and offering a common phenomenology for spontaneous activity and event-related potentials. The functional significance of segments might be modes or steps of information processing or performance, tested, e.g., as reaction time.
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The most important recent work on the neurobiology of sleep has focused on the precise cellular and biochemical mechanisms of rapid eye movement sleep mediation. Direct and indirect evidence implicates acetylcholine-containing neurons in the peribrachial pons as critical in the triggering and maintenance of rapid eye movement sleep. Other new studies provide support for the hypothesis that the cholinergic generator system is gated during waking by serotonergic and noradrenergic influences. A growing consensus regarding the basic neurobiology has stimulated new thinking about the brain basis of consciousness during waking and dreaming.
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Topographic EEG analysis includes different techniques to display the spatial distribution of brain electrical activity. The correct reconstruction of the scalp potentials and the consequent inference on cortical generators is influenced by many factors. In this study we focused our attention on the topographical representation of high resolution spectral EEG parameters, the choice of the interpolation algorithms for EEG mapping and the application of a spatial filtering method to scalp potential distribution. From our results there is evidence that different approaches are needed with relation to the EEG spatial features to be detected. The actual standard procedures seem not entirely adequate and the new methods proposed can improve significantly the visual reading of the EEG and its sensitivity.
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Source determination of alpha activity was studied using the relative power contribution analysis (RPCA) method which allows determination of the relative contributions of different areas to the power of a certain area at different frequencies. In 20 normal subjects, EEGs were recorded from F3, F4, C3, C4, P3, P4, O1 and O2, each referenced to a linked ear. An 8-dimensional autoregressive model was fitted to the EEGs of 10.24 sec. Based on the model, RPCA was performed. For each area, alpha activity was divided into two parts: one originating in its own area (endogenous) and another in the other areas (exogenous). Endogenous alpha activity increased as the area was more posterior. In the anterior regions (frontal and central), endogenous alpha power (power of endogenous alpha activity) was small, while exogenous alpha power was large. In the posterior regions (parietal and occipital), the amount of endogenous alpha power did not differ markedly from that of exogenous alpha power. The posterior regions, which generate more endogenous alpha activity, can be considered to play a dominant role in alpha generating mechanisms. In some subjects, alpha generators with a different frequency from that of the occipital areas were observed.
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The aim of the following study in one male volunteer was to show the EEG topography of the four sleep cycles (stages 1 to 4), of REM phases, and of EEG phenomena during sleep such as K-complexes and sigma spindles
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The question is asked, can the same visceral changes occur in different states of consciousness. A survey of EEG and autonomic activity found in the awake state and during the various stages of sleep leads to the conclusion that the question must be affirmatively answered. The conclusion is reached that EEG and autonomic activity cannot be used to define states of consciousness. The state of consciousness of the subject must first be known before the physiological significance and possible behavioral meaning of the EEG and autonomic responses can be inferred.
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Our investigation was concerned with 25 children, 10-13 years old, with an IQ 50-70 ("mild mental retardation," following the ICD). Among these, 14 attended a school for the mentally retarded and 11 one for the learning disabled. A control group was recruited, matched in age, sex and social class. The unipolar 8-channel record of EEG at rest was subjected to blind clinical rating, and a computerized analysis (broad band spectral parameters delta, theta, alpha 1, alpha 2, beta 1, beta 2). A significantly higher frequency of paroxysms was found by the clinical rating. It also allowed the diagnosis of a maturational lag with respect to the items "maturity" and "prominence of alpha rhythm"). Spectral parameters differentiated the two matched groups particularly in bands and leads of developmental relevance (theta, delta, and fronto-central beta in absolute power and theta, delta occipitally and alpha 2, with the exception of frontal leads for relative power).: As is well known, the mentally retarded constitute a heterogeneous group: this could also be verified with respect to EEG activity for the segment of mild mental retardation. A multivariate classification by nonmetric multidimensional scaling yielded a subgroup of 10 children deviant with respect to its overall EEG activity and a group of 15 children within the normal range. This assignment did not overlap with the assignment to the two schools. By computing ratios of broad band power in antero-posterior and symmetric-interhemispheric leads a reduced topographic differentiation was found for the experimental group in their antero-posterior distribution.
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Alpha activity during REM sleep without signs of awakening can discriminate between the blind classification of prelucid, lucid, and nonlucid dreams. Ten good dream recallers were aroused after relatively high or low amplitude REM alpha. The spectral and temporal characteristics of EEG alpha within each REM period were associated with lucidity and other content dimensions. Each type of dream had a reasonably distinct pattern of REM alpha. High amplitude alpha was found to be associated with prelucid dreams and bizarre content, which is consistent with theories of waking alpha activity and the hypothesis that lucidity sometimes emerges from prelucid experiences. The data are also consistent with the idea that lucidity is a viable dream content dimension, and interpreted in terms of systems theory imply that training which emphasizes dream content control may constrain the potential information integration function of lucid dreams.
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The level of consciousness between the alert and drowsy states was classified into four stages (Alert, Resting I, Resting II, Drowsy) by studying three factors of the EEG patterns on 23 normal subjects. The eye movements recorded by electro-oculograph were divided into two groups, i.e. rapid eye movements (R type, r type) and slow eye movements (S type, s type). The occurrence of each type of eye movements was confirmed to change in close correspondence to the stages of consciousness. The eye movement on 43 cases with a disturbance of consciousness by metabolic disease were recorded longitudinally according to clinical states. The S type movements were predominantly observed in a state of clouding of consciousness, while the R type and R-S type were observed in a delirious state.
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Thirty-two linear regression equations predict the frequency composition of the electroencephalogram within four frequency bands, for four bilateral regions of the brain, as a function of age. Equations based on such data from large groups of healthy children in the United States and Sweden are closely similar. These equations describe the development of the electrical activity of the normal human brain, independent of cultural, ethnic, socioeconomic, or sex factors.
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Dipole sources were investigated in 22 normal subjects with a variety of strategies available through the BESA program. When all the data were summed one regional source, located near the midline in the basal portions of the occipital lobe, explained 92% of the variance. Two regional sources, initially constrained for symmetry but subsequently freed from constraint placed them also in the occipital regions near the midline and reduced the residual variance to 4%. Pooled data obscure, however, the marked individual differences especially in regard to lateralization. In the individual case the major source was also always in one occipital area but its location, especially the degree of separation from the midline depended upon alpha distribution and the strategy used in the workup of the data. The orientation of the major components of the regional sources was usually in the posterior-anterior direction, fairly parallel to the midline and while the other one pointed to the upper convexity. Because of the considerable variability of the alpha rhythm in given subjects and even within the same individual a model which requires symmetry constraints is not optimal for all instances, even when constraints are lifted thereafter. The study demonstrated the feasibility of distinguishing predominantly mesial sources from those which are bihemipheric with more lateral origins but several different models may have to be used to reach the most realistic conclusions.
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Topographic aspects of all night sleep EEG were investigated in 10 healthy volunteers (age 20-35 years). EEG brain maps showed an increase of delta power from stage 1 to 4, a decrease of alpha power most pronounced parieto-occipitally and a slowing of the dominant alpha frequency. Differences of EEG power in different sleep stages (as compared to wakefulness) are displayed topographically. Analysis of the course of stage 2 showed an increase of delta power and a decrease of theta power in the first sections of the night, and an increase of beta power later in the night.
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High resolution spectral methods are explored as an alternative to broad band spectral parameters (BBSP) in quantitative EEG analysis. In a previous paper (Valdes et al. 1990b) regression equations ("Developmental surfaces") were introduced to characterize the age-frequency distribution of the mean and standard deviation of the log spectral EEG power in a normative sample. These normative surfaces allow the calculation of z transformed spectra for all derivations of the 10/20 system and z maps for each frequency. Clinical material is presented that illustrates how these procedures may pinpoint frequencies of abnormal brain activity and their topographic distribution, avoiding the frequency and spatial "smearing" that may occur using BBSP. The increased diagnostic accuracy of high resolution spectral methods is demonstrated by means of receiver operator characteristic (ROC) curve analysis. Procedures are introduced to avoid type I error inflation due to the use of more variables in this type of procedure.
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Previous attempts at automated analysis of sleep were mainly directed towards imitating the Rechtschaffen and Kales rules (RKR) in order to save scoring time and further objectify the procedure. RKR, however, do not take into consideration the sleep microstructure of REM, stage 2, and SWS. While the microstructure of stage 2 has been analyzed in the past decade, the microstructure of REM and SWS are virtually unknown. In stage 2 the amount and distribution of spindles, K complexes, and arousal reactions have been studied. At least two types of spindles (12/s and 14/s) with different dynamics and locations have been identified. Two different shapes for K complexes have been described: one related to external sensory stimuli with similarities to evoked potentials and another one more related to sinusoidal slow wave activity seen in SWS. These two different K complex shapes have different distributions and, obviously, different functions. The authors also suggest that one should differentiate between arousal reactions and true arousals. Recent investigations suggest two types of delta waves in SWS. The more sinusoidal 1-3/s delta waves with a frontal maximum are already seen with lower amplitude in late stage 2 and increase their amplitude and incidence towards stage 3 and Stage 4. The other delta-wave type is slower (< 1/s), polymorphic, and has varying amounts of theta and higher frequency waves superimposed. During REM sleep it seems to be important to separate phases with rapid eye movements from those with none (REM sine REM), and count the amount and distribution of sawtooth activity. Background activity during REM and REM sine REM, as well as intra- and interhemispheric coherence should be analyzed separately. Only if the microstructure of the sleep EEG can be analyzed automatically using newer techniques such as transformation into wavelets and pattern classification with neuronal networks, and only if we learn more about the importance of microstructure elements, can automated sleep analysis go beyond the limited information obtained from scoring according to RKR.
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State-dependent aspects of consciousness are explored with particular attention to waking and dreaming. First, those phenomenological differences between waking and dreaming that have been established through subjective reports are reviewed. These differences are robustly expressed in most aspects of consciousness including perception, attention, memory, emotion, orientation, and thought. Next, the roles of high frequency neuronal oscillation and neuromodulation are explored in waking and rapid eye movement sleep, the stage of sleep with which the most intense dreaming is associated. The high frequency neuronal oscillations serve similar functions in the wake and rapid eye movement states sleep but neuromodulation is very different in the two states. The collective high frequency oscillatory activity gives coherence to spatially separate neurons but, because of the different neuromodulation, the binding of sensory input in the wake state is very different from the binding of internally perceived input during rapid eye movement sleep. An explanatory model is presented which states that neuromodulation, as well as input source and brain activation level differentiate states of the brain, while the self-organized collective neuronal oscillations unify consciousness via long range correlations.
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Based on the findings of our previously published positron emission tomography study, we proposed that recorded eye movements during REM sleep are visually targeted saccades. In the present study, we examined the correlation between the number of eye movements in REM sleep (EM) and visual imagery in dreaming (V) and provided further support for our proposal. All the observations (N = 11) were made with one individual to eliminate interindividual variation and were made during the second REM sleep period to control for a time-of-night effect. V, with or without dream report length partialled out, was strongly associated with EM only in the 1-min interval immediately preceding awakening. The time course of the association suggests that the strong EM-V association reflects a phasic, localized activation of the eye-movement-control system in association with REM sleep eye movements.
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Auditory evoked potentials (AEPs) were recorded during presentation of stimuli of 1000 Hz (standard) and 2000 Hz (deviant) in trains of 10 tone bursts (one deviant per train) in the wake and rapid eye movement (REM) sleep states. The constant inter-stimulus interval (ISI) was 600 ms and the trains were separated by 3 s of silence. The deviant tone occurring at the train start elicited a mismatch negativity component (MMN) in both arousal states, displaying a peak latency between 100 and 150 ms post-stimulation at fronto-central areas. These results suggest the existence of an auditory memory trace (sensory memory) surviving for at least 3 s during REM sleep.
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In this study, we recorded the event-related potentials (ERPs) elicited by stimuli appearing at attended and unattended locations. The voltage amplitudes and latencies of the P1, N1, P2, N2 and P3 visual components showed statistically significant differences in the attended condition with respect to the unattended one. The power spectral density of the EEG following stimulus onset was calculated. The difference between the spectral densities of the attended and unattended conditions was computed. Statistically significant differences were found in the decrease of alpha (9-11 Hz) and the increase of beta (15-17 Hz) frequencies during the attention condition with respect to the unattended condition. These results suggest that the arrival of a visual stimulus during the attended condition generates a complex reorganization of neuronal activity in both time and frequency domains.