Статті в журналах: "EEG rhythms"

1

Morris, Harold H. "EEG Rhythms." Journal of Clinical Neurophysiology 7, no. 2 (April 1990): 155–56. http://dx.doi.org/10.1097/00004691-199004000-00001.

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2

Prado, G. Fernandes do, L. B. C. Carvalho, A. Baptista da Silva, and J. G. C. Lima. "EEG and dementia indicators in AIDS patients' Rorschach test." Arquivos de Neuro-Psiquiatria 52, no. 3 (September 1994): 314–19. http://dx.doi.org/10.1590/s0004-282x1994000300005.

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We studied the EEG and Rorschach test (RT) of nineteen AIDS patients and eight normal people in the same age group. Eight patients presented slow alpha rhythms (8 to 9 Hz); three, not-slow alpha rhythms (>9 to 13Hz); and eight, beta rhythms in background activity. Paroxystic activity, characterized by diffuse theta or delta waves, was present in eleven patients. We observed Oberholzer syndrome (organic dementia diagnosed by RT) in ten patients and Piotrowski syndrome (organic dementia diagnosed by RT) in eleven patients; six presented both. When considering only the group of AIDS patients, we did not observe a significant relation among slow alpha rhythm, not-slow alpha rhythm and the presence of paroxystic activity with the above-mentioned syndromes. AIDS patients with slow alpha rhythms showed a significantly greater number of Piotrowski syndrome dementia indicators when compared to normal individuals or those with slow alpha rhythms. We did not observe the same with Oberholzer syndrome.

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3

Gilmore, Sean A., and Frank A. Russo. "Neural and Behavioral Evidence for Vibrotactile Beat Perception and Bimodal Enhancement." Journal of Cognitive Neuroscience 33, no. 4 (April 2021): 635–50. http://dx.doi.org/10.1162/jocn_a_01673.

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The ability to synchronize movements to a rhythmic stimulus, referred to as sensorimotor synchronization (SMS), is a behavioral measure of beat perception. Although SMS is generally superior when rhythms are presented in the auditory modality, recent research has demonstrated near-equivalent SMS for vibrotactile presentations of isochronous rhythms [Ammirante, P., Patel, A. D., & Russo, F. A. Synchronizing to auditory and tactile metronomes: A test of the auditory–motor enhancement hypothesis. Psychonomic Bulletin & Review, 23, 1882–1890, 2016]. The current study aimed to replicate and extend this study by incorporating a neural measure of beat perception. Nonmusicians were asked to tap to rhythms or to listen passively while EEG data were collected. Rhythmic complexity (isochronous, nonisochronous) and presentation modality (auditory, vibrotactile, bimodal) were fully crossed. Tapping data were consistent with those observed by Ammirante et al. (2016), revealing near-equivalent SMS for isochronous rhythms across modality conditions and a drop-off in SMS for nonisochronous rhythms, especially in the vibrotactile condition. EEG data revealed a greater degree of neural entrainment for isochronous compared to nonisochronous trials as well as for auditory and bimodal compared to vibrotactile trials. These findings led us to three main conclusions. First, isochronous rhythms lead to higher levels of beat perception than nonisochronous rhythms across modalities. Second, beat perception is generally enhanced for auditory presentations of rhythm but still possible under vibrotactile presentation conditions. Finally, exploratory analysis of neural entrainment at harmonic frequencies suggests that beat perception may be enhanced for bimodal presentations of rhythm.

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4

Hu, Hai, Zihang Pu, and Peng Wang. "A flexible and accurate method for electroencephalography rhythms extraction based on circulant singular spectrum analysis." PeerJ 10 (March 23, 2022): e13096. http://dx.doi.org/10.7717/peerj.13096.

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Rhythms extraction from electroencephalography (EEG) signals can be used to monitor the physiological and pathological states of the brain and has attracted much attention in recent studies. A flexible and accurate method for EEG rhythms extraction was proposed by incorporating a novel circulant singular spectrum analysis (CiSSA). The EEG signals are decomposed into the sum of a set of orthogonal reconstructed components (RCs) at known frequencies. The frequency bandwidth of each RC is limited to a particular brain rhythm band, with no frequency mixing between different RCs. The RCs are then grouped flexibly to extract the desired EEG rhythms based on the known frequencies. The extracted brain rhythms are accurate and no mixed components of other rhythms or artifacts are included. Simulated EEG data based on the Markov Process Amplitude EEG model and experimental EEG data in the eyes-open and eyes-closed states were used to verify the CiSSA-based method. The results showed that the CiSSA-based method is flexible in alpha rhythms extraction and has a higher accuracy in distinguishing between the eyes-open and eyes-closed states, compared with the basic SSA method, the wavelet decomposition method, and the finite impulse response filtering method.

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5

Suzuki, Takako, Makoto Suzuki, Kilchoon Cho, Naoki Iso, Takuhiro Okabe, Toyohiro Hamaguchi, Junichi Yamamoto, and Naohiko Kanemura. "EEG Oscillations in Specific Frequency Bands Are Differently Coupled with Angular Joint Angle Kinematics during Rhythmic Passive Elbow Movement." Brain Sciences 12, no. 5 (May 14, 2022): 647. http://dx.doi.org/10.3390/brainsci12050647.

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Rhythmic passive movements are often used during rehabilitation to improve physical functions. Previous studies have explored oscillatory activities in the sensorimotor cortex during active movements; however, the relationship between movement rhythms and oscillatory activities during passive movements has not been substantially tested. Therefore, we aimed to quantitatively identify changes in cortical oscillations during rhythmic passive movements. Twenty healthy young adults participated in our study. We placed electroencephalography electrodes over a nine-position grid; the center was oriented on the transcranial magnetic stimulation hotspot of the biceps brachii muscle. Passive movements included elbow flexion and extension; the participants were instructed to perform rhythmic elbow flexion and extension in response to the blinking of 0.67 Hz light-emitting diode lamps. The coherence between high-beta and low-gamma oscillations near the hotspot of the biceps brachii muscle and passive movement rhythms was higher than that between alpha oscillation and passive movement rhythm. These results imply that alpha, beta, and gamma oscillations of the primary motor cortex are differently related to passive movement rhythm.

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6

Thuraisingham, R. A. "Revisiting ICEEMDAN and EEG rhythms." Biomedical Signal Processing and Control 68 (July 2021): 102701. http://dx.doi.org/10.1016/j.bspc.2021.102701.

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7

Andrew, Colin. "Sensorimotor EEG rhythms and their connection to local/global neocortical dynamic theory." Behavioral and Brain Sciences 23, no. 3 (June 2000): 399–400. http://dx.doi.org/10.1017/s0140525x0022325x.

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The EEG activity recorded from the human sensorimotor cortical area exhibits rhythmic activity covering a broad range of frequencies, including alpha, mu, beta, and gamma (40-Hz) rhythms. This commentary elaborates on connections between these sensorimotor rhythms and Nunez's neocortical dynamic theory.

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8

Yakovenko, Irina A., Nadejda E. Petrenko, Evgeniy A. Cheremoushkin, Vladimir B. Dorokhov, Zarina B. Bakaeva, Elena B. Yakunina, Vladimir I. Torshin, Yuri P. Starshinov, and Dmitry S. Sveshnikov. "Influence of lack of night sleep on the cognitive set by indicators of EEG rhythms coupling." SOCIALNO-ECOLOGICHESKIE TECHNOLOGII 10, no. 2 (2020): 226–39. http://dx.doi.org/10.31862/2500-2961-2020-10-2-226-239.

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The aim of the study is investigation of nighttime sleep effect on the performance of a cognitive setting in terms of the coupling of EEG rhythms. The coupling of 5 rhythm: beta-1, beta-2, gamma, alpha and theta rhythms of EEG during the formation and testing of cognitive set was studied for 120 students (17 with short-term night sleep and 15 with a full night sleep). Multi-channel EEG was recorded. EEG evaluation was carried out by continuous wavelet transform based on the “mother” complex Morlet wavelet in the range of 1–35 Hz. Maps of the distribution of the values of the modulus of the wavelet transformation coefficient, which reflect amplitude changes of the potentials were analyzed. The Pearson correlation coefficient was a measure evaluating the coupling of EEG rhythms. The subjects with a short night’s sleep showed almost all of the relations of EEG rhythms (8 couples) during the formation stage of presentation. Students with a full night’s sleep showed statistically significant coupling of the following pairs of rhythms: alpha–beta-1, alpha–gamma and beta-2–gamma. Students with short-term night sleep demonstrated the 3 significant couples: alpha–beta-1, beta-1–gamma and beta-2–gamma during the testing stage. Well-slept students showed an increase in the number of connections (6 couples) in relation to the stage of formation of the set due to the addition of connections with the theta rhythm. The obtained data could indicate that the thalamo-cortical and cortico-hippocampal structural-functional associations work differently in the groups of subjects.

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9

Eismont, Ye V., T. A. Aliyeva, N. V. Lutsyuk, and V. B. Pavlenko. "APPLICATION OF EEG FEEDBACK FOR THE CORRECTION OF PSYCHOEMOTIONAL STATE OF CHILDREN." Bulletin of Siberian Medicine 12, no. 2 (April 28, 2013): 175–81. http://dx.doi.org/10.20538/1682-0363-2013-2-175-181.

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Evaluated the efficacy of neurofeedbacktherapy to normalize the psychoemotional state of children. Trained parameters of EEG were the amplitude of alpha-rhythm, the ratio of amplitudes of alphaand theta-rhythms, sensorimotor and theta-rhythms. In the experimental group showed an increase in the trained parameters of the electroencephalogram, reduced anxiety, “feelings of inferiority” and improvement of voluntary attention. The results indicate the feasibility of neurofeedbacktherapy to optimize the psychoemotional state of children.

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10

Bushov, Yu V., and M. V. Svetlik. "PHASE INTERACTION BETWEEN EEG RHYTHMS IN THE STUDY OF PROCESSES OF TIME PERCEPTION." Bulletin of Siberian Medicine 13, no. 6 (December 28, 2014): 121–25. http://dx.doi.org/10.20538/1682-0363-2014-6-121-125.

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The present study pursued to investigate the role of phase interactions between EEG rhythms in the process of the perception of time. The purpose of the study was to analyse the dependence of these interactions on the type and stage of the activity being performed, as well as on the individual characteristics of a human. For this purpose, 27 boys and 29 girls, all university students, were asked to reproduce and measure short intervals of time (200 and 800 ms), during which their EEG was recorded in frontal, central, parietal, temporal, and occipital lobes, according to the system 10–20%. While studying phase interactions between EEG rhythms, we used wavelet bispectral analysis and calculated the bicoherence function. As it follows from the conducted research, most often close phase interactions are observed between the gamma-rhythm and other rhythms of EEG or between different frequencies of the gamma-rhythm. It was established that the phase interactions under study were influenced by the factors of “sex”, “activity type”, and “activity stage”. The study showed correlations of phase interactions with the levels of intellect, extraversion, neuroticism, with the particularities of the lateral organisation of brain, and the accuracy of time perception.

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11

Mathias, Brian, Anna Zamm, Pierre G. Gianferrara, Bernhard Ross, and Caroline Palmer. "Rhythm Complexity Modulates Behavioral and Neural Dynamics During Auditory–Motor Synchronization." Journal of Cognitive Neuroscience 32, no. 10 (October 2020): 1864–80. http://dx.doi.org/10.1162/jocn_a_01601.

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We addressed how rhythm complexity influences auditory–motor synchronization in musically trained individuals who perceived and produced complex rhythms while EEG was recorded. Participants first listened to two-part auditory sequences (Listen condition). Each part featured a single pitch presented at a fixed rate; the integer ratio formed between the two rates varied in rhythmic complexity from low (1:1) to moderate (1:2) to high (3:2). One of the two parts occurred at a constant rate across conditions. Then, participants heard the same rhythms as they synchronized their tapping at a fixed rate (Synchronize condition). Finally, they tapped at the same fixed rate (Motor condition). Auditory feedback from their taps was present in all conditions. Behavioral effects of rhythmic complexity were evidenced in all tasks; detection of missing beats (Listen) worsened in the most complex (3:2) rhythm condition, and tap durations (Synchronize) were most variable and least synchronous with stimulus onsets in the 3:2 condition. EEG power spectral density was lowest at the fixed rate during the 3:2 rhythm and greatest during the 1:1 rhythm (Listen and Synchronize). ERP amplitudes corresponding to an N1 time window were smallest for the 3:2 rhythm and greatest for the 1:1 rhythm (Listen). Finally, synchronization accuracy (Synchronize) decreased as amplitudes in the N1 time window became more positive during the high rhythmic complexity condition (3:2). Thus, measures of neural entrainment corresponded to synchronization accuracy, and rhythmic complexity modulated the behavioral and neural measures similarly.

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12

Aeschbach, Daniel, Jeffery R. Matthews, Teodor T. Postolache, Michael A. Jackson, Holly A. Giesen, and Thomas A. Wehr. "Two circadian rhythms in the human electroencephalogram during wakefulness." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 277, no. 6 (December 1, 1999): R1771—R1779. http://dx.doi.org/10.1152/ajpregu.1999.277.6.r1771.

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The influence of the circadian pacemaker and of the duration of time awake on the electroencephalogram (EEG) was investigated in 19 humans during ∼40 h of sustained wakefulness. Two circadian rhythms in spectral power density were educed. The first rhythm was centered in the theta band (4.25–8.0 Hz) and exhibited a minimum ∼1 h after the onset of melatonin secretion. The second rhythm was centered in the high-frequency alpha band (10.25–13.0 Hz) and exhibited a minimum close to the body temperature minimum. The latter rhythm showed a close temporal association with the rhythms in subjective alertness, plasma melatonin, and body temperature. In addition, increasing time awake was associated with an increase of power density in the 0.25- to 9.0-Hz and 13.25- to 20.0-Hz ranges. It is concluded that the waking EEG undergoes changes that can be attributed to circadian and homeostatic (i.e., sleep-wake dependent) processes. The distinct circadian variations of EEG activity in the theta band and in the high-frequency alpha band may represent electrophysiological correlates of different aspects of the circadian rhythm in arousal.

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13

ARMITAGE, R., R. F. HOFFMANN, and A. J. RUSH. "Biological rhythm disturbance in depression: temporal coherence of ultradian sleep EEG rhythms." Psychological Medicine 29, no. 6 (November 1999): 1435–48. http://dx.doi.org/10.1017/s0033291799001300.

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Background. Recent studies have suggested that major depressive disorders are associated with a breakdown in the organization of ultradian rhythm in sleep EEG. The present study used cross-spectral analysis of sleep EEG to confirm this finding, in a larger-scale study, evaluating the influence of gender and age on ultradian rhythms in depression.Methods. Temporal coherence of ultradian (80–120 min) rhythms in beta, theta and delta, recorded from central and parietal sites, were compared in 120 symptomatic, unmedicated, depressed out-patients and 59 healthy normal controls.Results. Few macro-architectural differences were noted between patients and controls. However, interhemispheric beta and theta coherence and intrahemispheric coherence between beta and delta rhythms were significantly lower in depressed patients. Coherence measures were lowest in women with depression and highest in men in the control group, but were not strongly influenced by age. Over 65% of depressed patients were [ges ]2 standard deviations below normal on at least one coherence measure, in sharp contrast to less than 10% of patients on macro-architectural variables.Conclusions. It was concluded that dysregulation of ultradian rhythms characterizes the majority of depressed out-patients, primarily women, even when macro-architecture did not differentiate groups. The outcome of this study supports the view that the pathophysiology of depression is strongly influenced by gender. It was suggested that low temporal coherence in depression reflects a breakdown in the organization of sleep EEG rhythms within and between the two hemispheres.

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14

Klimesch, W. "EEG-alpha rhythms and memory processes." International Journal of Psychophysiology 26, no. 1-3 (June 1997): 319–40. http://dx.doi.org/10.1016/s0167-8760(97)00773-3.

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15

Collura, Thomas F. "Neocortical Dynamics and Human EEG Rhythms." Journal of Clinical Neurophysiology 13, no. 2 (March 1996): 177–78. http://dx.doi.org/10.1097/00004691-199603000-00010.

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16

Nowack, W. J. "Neocortical Dynamics and Human EEG Rhythms." Neurology 45, no. 9 (September 1, 1995): 1793. http://dx.doi.org/10.1212/wnl.45.9.1793-a.

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17

Nunez, Paul L., and Samuel J. Williamson. "Neocortical Dynamics and Human EEG Rhythms." Physics Today 49, no. 4 (April 1996): 57. http://dx.doi.org/10.1063/1.2807585.

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18

Breska, Assaf, and Leon Y. Deouell. "Automatic Bias of Temporal Expectations following Temporally Regular Input Independently of High-level Temporal Expectation." Journal of Cognitive Neuroscience 26, no. 7 (July 2014): 1555–71. http://dx.doi.org/10.1162/jocn_a_00564.

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Exposure to rhythmic stimulation results in facilitated responses to events that appear in-phase with the rhythm and modulation of anticipatory and target-evoked brain activity, presumably reflecting “exogenous,” unintentional temporal expectations. However, the extent to which this effect is independent from intentional processes is not clear. In two EEG experiments, we isolated the unintentional component of this effect from high-level, intentional factors. Visual targets were presented either in-phase or out-of-phase with regularly flickering colored stimuli. In different blocks, the rhythm could be predictive (i.e., high probability for in-phase target) or not, and the color could be predictive (i.e., validly cue the interval to the target) or not. Exposure to nonpredictive rhythms resulted in faster responses for in-phase targets, even when the color predicted specific out-of-phase target times. Also, the contingent negative variation, an EEG component reflecting temporal anticipation, followed the interval of the nonpredictive rhythm and not that of the predictive color. Thus, rhythmic stimulation unintentionally induced expectations, even when this was detrimental. Intentional usage of predictive rhythms to form expectations resulted in a stronger behavioral effect, and only predictive cues modulated the latency of the target-evoked P3, presumably reflecting stimulus evaluation. These findings establish the existence of unintentional temporal expectations in rhythmic contexts, dissociate them from intentional expectations, and highlight the need to distinguish between the source of expectation (exogenous–endogenous) and the level of voluntary control involved in it (unintentional–intentional).

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19

Poepel, Annkathrin, Christoph Helmstaedter, Edgar Kockelmann, Nikolai Axmacher, Wieland Burr, Christian E. Elger, and Juergen Fell. "Correlation between EEG rhythms during sleep: surface versus mediotemporal EEG." NeuroReport 18, no. 8 (May 2007): 837–40. http://dx.doi.org/10.1097/wnr.0b013e3281053c1d.

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20

Nacharova, M. A., A. A. Mikhailova, Ya Yu Govorun, A. A. Portugalskaia, and V. B. Pavlenko. "NEUROPHYSIOLOGICAL MECHANISMS OF SPEECH PERCEPTION AND THEIR PERCULARITIES IN HEALTHY CHILDREN AND CHILDREN WITH DEVELOPMENTAL DISORDERS." Scientific Notes of V.I. Vernadsky Crimean Federal University. Biology. Chemistry 6(72), no. 3 (2021): 146–62. http://dx.doi.org/10.37279/2413-1725-2020-6-3-146-162.

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Neuronal synchronization, reflected in the EEG pattern, is the mechanism by which the brain integrates different types of information contained in a speech message and presented in different areas of the brain (for example, phonological, spelling, semantic and syntactic information). The process of understanding a sentence consists of two groups of interrelated cognitive operations: it begins with searching in memory for phonological, syntactic and semantic properties of words, which is followed by integrating information into a general idea of the sentence meaning. The stage of searching for words in adults results in an increase in the theta rhythm power. The stage of integrating words into a sentence results in the growth of theta, beta, and gamma rhythms. At the same time, the growth of theta rhythm is more typical for children than for adults. Higher rhythms reactivity during speech perception indicates better developed speech skills in children. Under conditions of relative relaxation, the EEG of children with a high level of speech development is characterized by a moderate power level of theta and beta rhythms and a high level of alpha and mu rhythms. It is assumed that a key role in the process of understanding speech is played by the so-called «action perception circuits», surrounding the Sylvian sulcus of the left hemisphere. The «action perception circuits» are composed of nerve cells capable of providing the speech signals perception and generation. The most important subgroup of neurons included in the «action perception circuits» are mirror neurons that are activated when performing and observing actions. The desynchronization of the EEG mu rhythm is considered as mirror neurons activation marker. In several studies, it revealed that the level of mirror neurons activation and the level of speech understanding in children are connected. It is a topic of great interest to research the mu rhythm alpha and beta components reactivity both during the production of speech and during the perception of another person speech. At present, it is becoming obvious that analyzing the EEG rhythms power changes during the speech understanding in different scenarios could be used to identify the mechanisms of the brain language network and speech disorders. The revealed patterns make it possible to propose ways of correcting the children speech development using EEG biological feedback methods.

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21

Nikolaenko, M. V., E. A. Kizhevatova, and N. V. Drobotya. "Early detection of cognitive impairment in patients with hypertension and evaluation of treatment effectiveness according to EEG data." Medical Herald of the South of Russia 11, no. 2 (June 30, 2020): 81–93. http://dx.doi.org/10.21886/2219-8075-2020-11-2-81-93.

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Objective: to establish the relationship between the presence of cognitive disorders in patients with arterial hypertension and changes in EEG, to assess the dynamics of these changes against the background of various modes of cerebroprotective therapy.Materials and methods: the study involved 92 people with arterial hypertension, whose average age was 63 ± 8.2 years. The research was carried out on the device “Encephalan-EEGR-19/26”. To assess cognitive functions, patients were tested using the MoСA test. Patients with cognitive impairment were divided into three groups of dynamic monitoring with diff erent modes of cerebroprotective therapy.Results: non-specifi c patterns in the slow-wave range were registered in patients with cognitive impairment during visual EEG analysis. In the quantitative analysis of the EEG revealed changes in the frequency and amplitude of the alpha rhythm, the power variation on the basic rhythms, the reduction of the total strength of the rhythms, the increase in relative power of slow rhythms in the frontal leads to the total power of the rhythms. After the treatment, most patients showed an increase in scores on the “Montreal scale”, a decrease in anxiety and depression on the” Hospital scale”, and an increase in the SF-36 index. Quantitative EEG analysis revealed positive dynamics comparable to the clinic and test data. The most favorable EEG dynamics was registered in groups of patients receiving neuroprotective and combined therapy.Conclusions: the results obtained indicate the diagnostic value of quantitative EEG analysis and the feasibility of adding drugs that improve the metabolism and blood supply to the brain to standard antihypertensive therapy.

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Guadalupe, C. B. "258 EEG RHYTHMS AND SLEEP APNEA EPISODES." Sleep Medicine 10 (December 2009): S69—S70. http://dx.doi.org/10.1016/s1389-9457(09)70260-6.

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23

van der Stelt, Odin. "Development of human EEG posterior alpha rhythms." Clinical Neurophysiology 119, no. 8 (August 2008): 1701–2. http://dx.doi.org/10.1016/j.clinph.2008.04.001.

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24

Choi, Jee Hyun, Klaus Peter Koch, Wigand Poppendieck, Mina Lee, and Hee-Sup Shin. "High Resolution Electroencephalography in Freely Moving Mice." Journal of Neurophysiology 104, no. 3 (September 2010): 1825–34. http://dx.doi.org/10.1152/jn.00188.2010.

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Electroencephalography (EEG) is a standard tool for monitoring brain states in humans. Understanding the molecular and cellular mechanisms underlying diverse EEG rhythms can be facilitated by using mouse models under molecular, pharmacological, or electrophysiological manipulations. The small size of the mouse brain, however, poses a severe limitation in the spatial information of EEG. To overcome this limitation, we devised a polyimide based microelectrode array (PBM array) with nanofabrication technologies. The microelectrode contains 32 electrodes, weighs 150 mg, and yields noise-insensitive signals when applied on the mouse skull. The high-density microelectrode allowed both global and focused mapping of high resolution EEG (HR-EEG) in the mouse brain. Mapping and dynamical analysis tools also have been developed to visualize the dynamical changes of spatially resolved mouse EEG. We demonstrated the validity and utility of mouse EEG in localization of the seizure onset in absence seizure model and phase dynamics of abnormal theta rhythm in transgenic mice. Dynamic tracking of the EEG map in genetically modified mice under freely moving conditions should allow study of the molecular and cellular mechanisms underlying the generation and dynamics of diverse EEG rhythms.

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25

Nazarov, A. I. "Comparison of the EDA and EEG frequency components (qualitative analysis)." Experimental Psychology (Russia) 10, no. 2 (2017): 104–13. http://dx.doi.org/10.17759/exppsy.2017100209.

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The author recorded galvanic skin response (GSR, Feret method), skin potential (SP, Tarkhanov method) and local frontal encephalogram (Fp1). GSR was recorded with an amplifier bandwidth of 0.05-1 Hz, SP and EEG amplified with biopotential broadband amplifiers in the range 0.05-300 Hz. SP and local EEG signals were processed by the same algorithm. We obtained profiles of SP and EEG containing topologically similar low (LF) and high frequency (HF) components in the range of 0.05-45 Hz. LF-components of SP and EEG in some subjects were identical (in phase) of the GSR, in other subjects they were antiphased, in still others both types were observed. High-frequency components of SP contained rhythms, similar to EEG rhythms (Delta, Theta, Alpha, Beta, Gamma). However, frequency analysis showed that SP rhythms differ from local EEG rhythms in power and frequency distribution. The findings are discussed in the context of the nature of biorhythms.

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26

Lobasyuk, B., L. Bartsevich, and A. Zamkovaya. "Analysis of electrogenesis’ changes in mental retardation persons by using computer electroencephalography." Journal of Education, Health and Sport 11, no. 11 (November 30, 2021): 249–65. http://dx.doi.org/10.12775/jehs.2021.11.11.025.

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Justification. Mental retardation is a persistent decrease in human cognitive activity against the background of organic damage to the central nervous system. Neurophysiological diagnostics, in particular electroencephalography (EEG), most adequately reflects the morpho-functional state of the central nervous system, which is the basis of the mechanisms of mental activity, and the originality of the bioelectrical activity of the brain can be considered as the main indicator that determines a decrease in the level of intellectual development and, thereby, characterizes this state. This provision actualizes the search for highly informative indicators of the originality of the bioelectrical activity of the brain in children with intellectual disabilities. Purspose. With the use of periodometric analysis investigate EEG’s indicators and interhemispheric asymmetry of rhythms amplitudes in MR patients. Materials and methods. The EEG was recorded in a state of calm wakefulness with closed eyes with Neuron-Spectrum-2 electroencephalograph. Differences in indicators were tracked using the calculation of the coefficient of compliance (CC), EEG functional asymmetry coefficients in amplitude were determined, too. Results. It was revealed that in MR patients the amplitudes of the rhythms were greater than in healthy subjects. The greatest increase was determined in theta rhythm in the anterior temporal and posterior temporal leads in the left hemispheres. Duration indices in the delta, theta and alpha ranges of the EEG in mental retardation compared with the control group were increased, and the indices of the duration of beta rhythms - decreased. When analyzing FMPA in MR persons it turned out that in right-handers the negativeness of FMPA indices increased, and in left-handers there was an increase in the positivity of FMPA indices. Conclusions 1. With mental retardation, the amplitudes of the rhythms were greater than in healthy people. The greatest increase was determined in theta rhythm in the anterior temporal and posterior temporal leads in the left hemispheres. 2. The indices of duration in the delta, theta and alpha ranges of the EEG of MR subjects were increased, and the indices of the duration of beta rhythms – decreased. 3. When analyzing FMPA in MR persons, it turned out that in right-handers the negativeness of FMPA indices increased, and in left-handers there was an increase in the positivity of FMPA indices.

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27

Hu, Hai, Shengxin Guo, Ran Liu, and Peng Wang. "An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography." PeerJ 5 (June 28, 2017): e3474. http://dx.doi.org/10.7717/peerj.3474.

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Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%).

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Zaleshin, Alexander, and Galina Merzhanova. "Synchronization of Independent Neural Ensembles in Human EEG during Choice Tasks." Behavioral Sciences 9, no. 12 (November 28, 2019): 132. http://dx.doi.org/10.3390/bs9120132.

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During behavioral experiments, humans placed in a situation of having to choose between a more valuable but risky reward and a less valuable but guaranteed reward make their decisions in accordance with external situational factors and individual characteristics, such as inclination to risk or caution. In such situations, humans can be divided into “risk-inclined” and “risk-averse” (or “cautious”) subjects. In this work, characteristics of EEG rhythms, such as phase–phase relationships and time lags between rhythms, were studied in pairs of alpha–beta and theta–beta rhythms. Phase difference can also be expressed as a time lag. It has been suggested that statistically significant time lags between rhythms are due to the combined neural activity of anatomically separate, independent (in activation/inhibition processes) ensembles. The extents of synchronicity between rhythms were compared as percentages between risk-inclined and risk-averse subjects. The results showed that synchronicity in response to stimuli was more often observed in pairs of alpha–beta rhythms of risk-averse subjects compared with risk-inclined subjects during the choice of a more valuable but less probable reward. In addition, significant differences in the percentage ratio of alpha and beta rhythms were revealed between (i) cases of synchronization without long time lags and (ii) cases with long time lags between rhythms (from 0.08 to 0.1 s).

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Barios, Juan A., Santiago Ezquerro, Arturo Bertomeu-Motos, Marius Nann, Fco Javier Badesa, Eduardo Fernandez, Surjo R. Soekadar, and Nicolas Garcia-Aracil. "Synchronization of Slow Cortical Rhythms During Motor Imagery-Based Brain–Machine Interface Control." International Journal of Neural Systems 29, no. 05 (May 29, 2019): 1850045. http://dx.doi.org/10.1142/s0129065718500454.

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Modulation of sensorimotor rhythm (SMR) power, a rhythmic brain oscillation physiologically linked to motor imagery, is a popular Brain–Machine Interface (BMI) paradigm, but its interplay with slower cortical rhythms, also involved in movement preparation and cognitive processing, is not entirely understood. In this study, we evaluated the changes in phase and power of slow cortical activity in delta and theta bands, during a motor imagery task controlled by an SMR-based BMI system. In Experiment I, EEG of 20 right-handed healthy volunteers was recorded performing a motor-imagery task using an SMR-based BMI controlling a visual animation, and during task-free intervals. In Experiment II, 10 subjects were evaluated along five daily sessions, while BMI-controlling same visual animation, a buzzer, and a robotic hand exoskeleton. In both experiments, feedback received from the controlled device was proportional to SMR power (11–14[Formula: see text]Hz) detected by a real-time EEG-based system. Synchronization of slow EEG frequencies along the trials was evaluated using inter-trial-phase coherence (ITPC). Results: cortical oscillations of EEG in delta and theta frequencies synchronized at the onset and at the end of both active and task-free trials; ITPC was significantly modulated by feedback sensory modality received during the tasks; and ITPC synchronization progressively increased along the training. These findings suggest that phase-locking of slow rhythms and resetting by sensory afferences might be a functionally relevant mechanism in cortical control of motor function. We propose that analysis of phase synchronization of slow cortical rhythms might also improve identification of temporal edges in BMI tasks and might help to develop physiological markers for identification of context task switching and practice-related changes in brain function, with potentially important implications for design and monitoring of motor imagery-based BMI systems, an emerging tool in neurorehabilitation of stroke.

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Osintseva, Ye V., M. V. Nadezhdina, M. N. Zhezher, O. S. Kurus, and N. I. Skulskaya. "The vegetative status and bioelectric activity of the brain in different terms of the remote period of a fighting craniocereberal trauma." Bulletin of Siberian Medicine 9, no. 4 (August 28, 2010): 84–88. http://dx.doi.org/10.20538/1682-0363-2010-4-84-88.

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2 groups on 35 patients from 32 till 52 years which have transferred an easy fighting craniocerebral trauma. It is spent electroencephalographic (EEG) research, the computer analysis with definition of indexes of the basic rhythms, the vegetative status under the program of variability of a rhythm of heart is studied, functional classes (FC) on D.I. Zhemajtite are defined. At all patients changed types EEG prevailed. It is noted low FC at patients with I, II, V types ЭЭГ. Correlation of type EEG with level FC is revealed: at patients with V type EEG prevailed IV and V, with I and II types EEG — higher (II, III) FC. The high index of an alpha rhythm correlates with high II FC, and a beta rhythm with III FC. Decrease in adaptable mechanisms of vegetative regulation defines necessity of medicament correction.

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Singh, Pushpendra, S. D. Joshi, R. K. Patney, and Kaushik Saha. "Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms." Circuits, Systems, and Signal Processing 35, no. 10 (December 22, 2015): 3700–3715. http://dx.doi.org/10.1007/s00034-015-0225-z.

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Basar, E., and M. Schurmann. "Alpha Rhythms in the Brain: Functional Correlates." Physiology 11, no. 2 (April 1, 1996): 90–96. http://dx.doi.org/10.1152/physiologyonline.1996.11.2.90.

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Experimental evidence suggests a functional significance of electroencephalographic (EEG) alpha rhythms. Event-related, evoked, and induced alpha rhythms may have functional correates in primary sensory processing and preparatory processes. These results are in accord with the view that spontaneous and induced EEG alpha rhythms have quasi-deterministic properties.

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Upadhyaya, Prajna, and Tohru Yagi. "Using Entropies for the Analysis of Brain Rhythms." International Journal of Signal Processing Systems 8, no. 3 (September 2020): 54–58. http://dx.doi.org/10.18178/ijsps.8.3.54-58.

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Epilepsy is a chronic neurological disorder characterized by seizures. It involves abnormal discharging of neurons that effects smaller section of the brain, referred to as partial epilepsy or larger section of the brain resulting in generalized epilepsy. Sometimes these abnormal activities spread from smaller section to the larger section of the brain resulting in secondary generalized epilepsy. Hence, it is important to detect and control epileptic seizure in an early stage. In this work, we design a system that classifies interictal (period between the seizure) and ictal (after onset of seizure) signals by extracting subtle information from the EEG rhythms: gamma, beta, alpha, theta and delta. The following system also aims to determine the sensitivity of these EEG rhythms towards epileptic seizure. In this research, we have used entropy methods namely: Shannon entropy, approximate entropy and sample entropy to extract the subtle information from the EEG rhythms. Classifiers namely: k-nearest neighbor, support vector machine and linear discriminant analysis is utilized to distinguish interictal and ictal signals with a classification accuracy of 94%, 95.5% and 97.5%.

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Pyatin, VF F., AV V. Kolsanov, MS S. Segreeva, ES S. Korovina, and AV V. Zakharov. "CHANGES IN PATTERNS OF SENSORIMOTOR EEG RHYTHMS DURING MOTOR IMAGERY." Science and Innovations in Medicine 1, no. 1 (March 15, 2016): 46–51. http://dx.doi.org/10.35693/2500-1388-2016-0-1-46-51.

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Aim - the determination of common and individual characteristics in patterns of sensorimotor rhythms of EEG during motor imagery in upper and lower limbs. Materials and methods. 20 right-handed students of Samara State Medical University at the age of 18-20 years took part in the investigation, signing informed consent. Monopolar EEG was recorded with the use of 128-channel EEG recording system (BP-010302 BrainАmpStandart 128) at rest and during the imagining of monovector movements in 4 limbs (bending fingers of the right hand, bending fingers of the left hand, dorsiflexion of the right foot, dorsiflexion of the left foot); and during the imagining of triple-vector movements in the dominant hand (fingers bending, elbow flexion, wrist rotation). The following programs and methods were used during the processing of EEG: MatLab, IBM SPSS Statistics 22, ICA (independent component analysis), CSP (Common Spatial Pattern), LORETA. Results. It was found out that alpha2- and beta2- EEG frequency bands are highly significant for the formation of contralateral activation focus during motor imagery in the 4 limbs. ERD / ERS of the EEG rhythms were more pronounced during imagining movements in the dominant limbs (right hand, right leg) than in non-dominant.We found individuality of responses of sensorimotor EEG rhythms in addition to the general trends of EEG changes during imagination of one-type movement in the 4 limbs. The significance of changes in the power of EEG sensorimotor rhythms for differentiating 3 degrees of freedom during motor imagery in one limb was not found. Conclusion. Event-related desynchronization/synchro-nization(ERD/ERS) of sensorimotor EEG rhythms related to motor imagery has individual characteristics and their classification will lead to the significant increase of the number of degrees of freedom in creation and implementation of BCI.

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Iliadou, Paraskevi, Ioannis Paliokas, Stelios Zygouris, Eftychia Lazarou, Konstantinos Votis, Dimitrios Tzovaras, and Magdalini Tsolaki. "A Comparison of Traditional and Serious Game-Based Digital Markers of Cognition in Older Adults with Mild Cognitive Impairment and Healthy Controls." Journal of Alzheimer's Disease 79, no. 4 (February 16, 2021): 1747–59. http://dx.doi.org/10.3233/jad-201300.

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Background: Electroencephalography (EEG) has been used to assess brain activity while users are playing an immersive serious game. Objective: To assess differences in brain activation as measured with a non-intrusive wearable EEG device, differences in game performance and correlations between EEG power, game performance and global cognition, between cognitively impaired and non-impaired older adults, during the administration of a novel self-administered serious game-based test, the Virtual Supermarket Test (VST). Methods: 43 older adults with subjective cognitive decline (SCD) and 33 older adults with mild cognitive impairment (MCI) were recruited from day centers for cognitive disorders. Global cognition was assessed with the Montreal Cognitive Assessment (MoCA). Brain activity was measured with a non-intrusive wearable EEG device in a resting state condition and while they were administered the VST. Results: During resting state condition, the MCI group showed increased alpha, beta, delta, and theta band power compared to the SCD group. During the administration of the VST, the MCI group showed increased beta and theta band power compared to the SCD group. Regarding game performance, alpha, beta, delta, and theta rhythms were positively correlated with average duration, while delta rhythm was positively correlated with mean errors. MoCA correlated with alpha, beta, delta, and theta rhythms and with average game duration and mean game errors indicating that elevated EEG rhythms in MCI may be associated with an overall cognitive decline. Conclusion: VST performance can be used as a digital biomarker. Cheap commercially available wearable EEG devices can be used for obtaining brain activity biomarkers.

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Trushina, D. A., O. A. Vedyasova, and M. A. Paramonova. "SPATIAL PICTURE OF DISTRIBUTION OF ELECTROENCEPHALOGRAM RHYTHMS IN THE RIGHT-HANDED STUDENTS DURING AN EXAM." Vestnik of Samara University. Natural Science Series 20, no. 3 (May 31, 2017): 202–12. http://dx.doi.org/10.18287/2541-7525-2014-20-3-202-212.

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Registration of electroencephalogram (EEG) in the right-handed students during an exam revealed weakening of alpha rhythm in all areas of right and left cerebral hemispheres as well as increased beta rhythm in parietal and end-lobes of the right hemisphere. Simultaneously regular delta waves in the frontal leads ofboth hemispheres and theta waves in right frontal, occipitalis and temporal leads were marked. Oﬀset of EEG slow wave activity in the right hemisphere in partial right-handers was marked in more noticeable way than in that of the true right-handers. The obtained data suggest that the distribution of EEG rhythms in condition of mental and emotional stress in right-handers depends on the type and severity of functional hemispheric asymmetry.

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da Silva, F. Lopes. "S14.2 Temporal codes in networks sustaining EEG rhythms." Clinical Neurophysiology 122 (June 2011): S35. http://dx.doi.org/10.1016/s1388-2457(11)60113-2.

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Orekhova, Elena V., Mikael Elam, and Vladislav Yu Orekhov. "Unraveling superimposed EEG rhythms with multi-dimensional decomposition." Journal of Neuroscience Methods 195, no. 1 (January 2011): 47–60. http://dx.doi.org/10.1016/j.jneumeth.2010.11.010.

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39

Willoughby, J. O., L. Mackenzie, K. J. Pope, M. Broberg, and M. Nilsson. "Localised astroglial dysfunction disrupts high-frequency EEG rhythms." Journal of Neural Transmission 112, no. 2 (September 10, 2004): 205–13. http://dx.doi.org/10.1007/s00702-004-0189-9.

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40

Mikhailova, A. A., A. I. Kaida, E. V. Eismont, and V. B. Pavlenko. "AGE FEATURES OF BETA RHYTHMS EEG IN CHILDREN AGED 4-15 IN A STATE OF STABLE VISUAL ATTENTION." Scientific Notes of V.I. Vernadsky Crimean Federal University. Biology. Chemistry 7 (73), no. 2 (2021): 95–103. http://dx.doi.org/10.37279/2413-1725-2021-7-2-95-103.

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One of the reliable parameters for assessing the bioelectrical activity of the brain in children is the relative power (RP) of the EEG. Previous results have shown that EEG RP systematically changes not only depending on age, but also with various developmental disorders. The beta rhythm RP is of particular interest when performing tasks requiring the involvement of cognitive functions. In connection with the above, the purpose of the present study was to identify and analyze the RP of beta1 and beta2 EEG rhythms in a state of stable visual attention (SVА) in children 4–15 years old. The study involved 159 children (82 girls and 77 boys) aged 4–15 years with a normal level of physical and mental development. In the present study, children were required to observe and reproduce the movements of a computer mouse. At the same time, at certain moments, an image of a stationary computer mouse was presented, which was accompanied by a request to look at the computer screen, in connection with which both voluntary and involuntary attention of the subjects was activated. The use of the SVА situation makes it possible to analyze the nature of differences in the EEG pattern of children, taking into account the data on the neurophysiological mechanisms of attention. The greatest increase in the RP of the EEG beta1 rhythm found in the age range of 7–12. For the beta2 rhythm, the greatest increase discovered in the period of 10–15 years. The obtained values of the RP of the beta1 and beta2 rhythms of the EEG, recorded in the state of stable visual attention, in children of different ages can be used as reference data in the relevant research areas, as well as in the development of correction protocols for the cognitive functions and psychoemotional state using EEG biofeedback. The age-related dynamics of the beta rhythm RP can serve as an indicator of the maturation of cortical inhibition processes in children, which underlie the formation of voluntary cognitive functions and conscious purposeful behavior.

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Krauss, G. L., A. Abdallah, R. Lesser, R. E. Thompson, and E. Niedermeyer. "Clinical and EEG features of patients with EEG wicket rhythms misdiagnosed with epilepsy." Neurology 64, no. 11 (June 13, 2005): 1879–83. http://dx.doi.org/10.1212/01.wnl.0000163991.97456.03.

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42

Pradhan, Cauchy, Susant K. Jena, Sreenivasan R. Nadar, and N. Pradhan. "Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms." Computational and Mathematical Methods in Medicine 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/206857.

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The fundamental nature of the brain's electrical activities recorded as electroencephalogram (EEG) remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEG because of their robustness, simplicity of interpretation, and apparent association with rhythmic behavioral patterns in nature. In this paper, we extend the use of higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extension Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, therefore, can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. The paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs) and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.

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Rosipal, Roman, Natália Porubcová, Peter Barančok, Barbora Cimrová, Igor Farkaš, and Leonardo Jose Trejo. "Effects of mirror-box therapy on modulation of sensorimotor EEG oscillatory rhythms: a single-case longitudinal study." Journal of Neurophysiology 121, no. 2 (February 1, 2019): 620–33. http://dx.doi.org/10.1152/jn.00599.2018.

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We provide direct electrophysiological evidence that mirror therapy (MT) can change brain activity and aid in the recovery of motor function after stroke. In this longitudinal single-case study, the subject was a 58-yr-old man with right-hand hemiplegia due to ischemic stroke. Over a 9-mo period we treated him with MT twice a week and measured electroencephalograms (EEG) before, during, and after each therapy session. Using advanced signal processing methods, we identified five distinct movement-related oscillatory EEG components: one slow component designated as mu rhythm and four faster components designated as sensorimotor rhythms. Results show that MT produced long-term changes of two oscillatory EEG components including the mu rhythm, which is a well-documented correlate of voluntary movement in the frequency range of 7.5–12 Hz. Specifically, MT was significantly associated with an increase in the power of mu rhythm recorded over both hemispheres and a decrease in the power of one sensorimotor component recorded over the affected hemisphere. To obtain robust, repeatable individual measures of EEG components suitable for longitudinal study, we used irregular-resampling autospectral analysis to separate fractal and oscillatory components in the EEG power spectrum and three-way parallel factor analysis to isolate oscillatory EEG components and track their activations over time. The rhythms were identified over individual days of MT training and were clearly related to the periods of event-related desynchronization and synchronization (rest, observe, and move) during MT. Our results are consistent with a model in which MT promotes recovery of motor function by altering neural activity associated with voluntary movement. NEW & NOTEWORTHY We provide novel evidence that mirror therapy (MT), which helps in the recovery of motor function after a stroke, is also associated with long-lasting changes in brain electrical activity. Using precise measurements of oscillatory EEG components over a 9-mo period in a victim of ischemic stroke, we showed that MT produced long-term increases in the mu rhythm recorded over both hemispheres and a decrease in a sensorimotor EEG component recorded over the affected hemisphere.

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Soltesz, I., and M. Deschenes. "Low- and high-frequency membrane potential oscillations during theta activity in CA1 and CA3 pyramidal neurons of the rat hippocampus under ketamine-xylazine anesthesia." Journal of Neurophysiology 70, no. 1 (July 1, 1993): 97–116. http://dx.doi.org/10.1152/jn.1993.70.1.97.

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1. Intracellularly recorded low- and high-frequency (4-6 and 25-50 Hz, respectively), rhythmic, spontaneous membrane potential oscillations were investigated in pyramidal neurons of the rat hippocampus in vivo, during theta (theta, 4-6 Hz) electroencephalographic (EEG) activity, under ketamine-xylazine anesthesia. 2. The EEG activity showed two spectral peaks, one in the theta range, the other at higher frequencies (25-50 Hz). On the basis of their electrophysiological and pharmacological properties, it was concluded that the EEG theta-waves, and the fast EEG rhythm, recorded during ketamine-xylazine anesthesia, share the basic properties of those theta and fast rhythms that are recorded under the effects of other types of anesthetics. 3. When intracellular recordings (n = 32) were made with electrodes filled with potassium-acetate (K-acetate), the only CA1 and CA3 pyramidal cells (PCs) considered for further analysis were those that did not fire rhythmically at most or each cycle of the theta rhythm at the resting membrane potential. During EEG-theta, the membrane potential (Vm) of these cells showed a prominent oscillation (3-15 mV) with frequencies similar to those of the EEG-theta (the intracellular theta rhythm, intra-theta). 4. The frequency of the intra-theta was independent of the Vm. However, the phase difference between the intra-theta and the EEG-theta was voltage dependent in both types of cells. CA1 PCs showed a large (120-180 degrees, where 360 degrees is the full cycle), gradual shift in the phase difference between the intra-theta and the EEG-theta, when the membrane was hyperpolarized to -85 from -65 mV. Although CA3 PCs displayed a larger variability in their phase-voltage relations, a voltage-dependent phase shift (90-180 degrees) could be observed in CA3 PCs as well. 5. Although the amplitude of the intra-theta in both CA1 and CA3 PCs could display large, sudden, spontaneous changes at a given Vm, the amplitude-Vm plots tended to show a minimum between -70 and -80 mV. Spontaneous changes in the amplitude of the intra-theta did not affect the phase difference between the intra- and the EEG-theta rhythms. 6. Intracellular injection of QX-314 (50-100 mM) did not change the phase-Vm or the amplitude-Vm relationships of CA1 PCs. 7. Intracellular injection of chloride (Cl-) ions greatly reduced the voltage dependency of the phase difference and revealed fast (duration: 20-25 ms), depolarizing potentials (5-20 mV), which appeared at high frequencies (25-50 Hz), amplitude modulated at theta-frequencies.(ABSTRACT TRUNCATED AT 400 WORDS)

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Kim, Youngjoo, Jiwoo Ryu, Ko Keun Kim, Clive C. Took, Danilo P. Mandic, and Cheolsoo Park. "Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1489692.

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Анотація:

Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.

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Krivonogova, Ye V., L. V. Poskotinova, and D. B. Dyomin. "Comparative analysis of the EEG components and heart rate variability during biofeedback training, depending on the serotonin serum level at girls 15—17 years." Bulletin of Siberian Medicine 10, no. 4 (August 28, 2011): 21–26. http://dx.doi.org/10.20538/1682-0363-2011-4-21-26.

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The aim is analysis of EEG-rhythms spectral capacity during biofeedback session by heart rhythm variability (HRV) parameters in dependence from serum serotonin level. The were surveyed 53 girls 15—17 years of Arkhangelsk. The girls with a deficit of serotonin to achieve positive results biofeedback-training is accompanied by desynchronization EEG components, which results in reduced spectral power of EEG α-, β- and θ-activity. At the level of serotonin in the blood serum in the normal range for girls is an increase in spectral power of α-activity in thethe anterior parts of of the brain.

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Hosamane, Nishitha, Yuval Levin, Taylor McNair, and Michael Sidorov. "796 Quantitative EEG Analysis in Angelman Syndrome." Sleep 44, Supplement_2 (May 1, 2021): A310. http://dx.doi.org/10.1093/sleep/zsab072.793.

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Анотація:

Abstract Introduction Angelman syndrome (AS) is a neurodevelopmental disorder resulting from decreased expression of the maternal copy of the imprinted UBE3A gene on chromosome 15. This disorder is characterized by intellectual disability, impaired speech and motor skills, and sleep abnormalities but currently lacks any treatment. However, mouse models have shown that un-silencing the dormant paternal copy of UBE3A has been an effective mechanism to restore the functionality of the UBE3A protein, thus clinical trials using this approach are on the near horizon. Developing biomarkers is essential for assessing responses to treatment when clinical trials begin, and quantitative EEG analysis has shown great promise as a biomarker for AS. Methods Here, we sought to define EEG biomarkers directly linked to sleep impairments seen in up to 90% of individuals with AS (Trickett). We analyzed nine overnight sleep studies from patients with AS with age and sex matched Down syndrome and neurotypical controls. We specifically examined low-frequency delta rhythms and sleep spindles during NREM sleep. Results We confirmed that low- delta rhythms are increased during overnight sleep in AS, and that this biomarker appears more reliable than possible changes in sleep spindles. Conclusion Our results suggest that quantitative measurement of delta rhythms during sleep can be used as a potential biomarker for treatments in Angelman syndrome clinical trials. Support (if any):

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Han, Chunxiao, Xiaozhou Sun, Yaru Yang, Yanqiu Che, and Yingmei Qin. "Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram Signals." Entropy 21, no. 4 (April 1, 2019): 353. http://dx.doi.org/10.3390/e21040353.

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Fatigued driving is one of the major causes of traffic accidents. Frequent repetition of driving behavior for a long time may lead to driver fatigue, which is closely related to the central nervous system. In the present work, we designed a fatigue driving simulation experiment and collected the electroencephalogram (EEG) signals. Complex network theory was introduced to study the evolution of brain dynamics under different rhythms of EEG signals during several periods of the simulated driving. The results show that as the fatigue degree deepened, the functional connectivity and the clustering coefficients increased while the average shortest path length decreased for the delta rhythm. In addition, there was a significant increase of the degree centrality in partial channels on the right side of the brain for the delta rhythm. Therefore, it can be concluded that driving fatigue can cause brain complex network characteristics to change significantly for certain brain regions and certain rhythms. This exploration may provide a theoretical basis for further finding objective and effective indicators to evaluate the degree of driving fatigue and to help avoid fatigue driving.

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SINGH, PUSHPENDRA, and RAM BILAS PACHORI. "CLASSIFICATION OF FOCAL AND NONFOCAL EEG SIGNALS USING FEATURES DERIVED FROM FOURIER-BASED RHYTHMS." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740002. http://dx.doi.org/10.1142/s0219519417400024.

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We propose a new technique for the automated classification of focal and nonfocal electroencephalogram (EEG) signals using Fourier-based rhythms in this paper. The EEG rhythms, namely, delta, theta, alpha, beta and gamma, are obtained using the discrete Fourier transform (DFT)-based filter bank applied on EEG signals. The mean-frequency (MF) and root-mean-square (RMS) bandwidth features are derived using DFT-based computation on rhythms of EEG signals and their envelopes. These derived features, namely, MF and RMS bandwidths have been provided as an input feature set for the classification of focal and nonfocal EEG signals using a least-squares support vector machine (LS-SVM) classifier. We present experimental results obtained from the publicly available database in order to demonstrate the effectiveness of the proposed feature sets for the automated classification of the focal and nonfocal classes of EEG signals. The obtained classification accuracy in this dataset for the automated classification of focal and nonfocal 50 pairs and 750 pairs of EEG signals are 89.7% and 89.52%, respectively.

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Voitenkov, V. B., A. B. A. B. Palchick, N. A. Savelieva, and E. P. Bogdanova. "Bioelectric activity of the brain in 3-4 years old children in eyes-open resting state." Translational Medicine 8, no. 4 (November 18, 2021): 47–56. http://dx.doi.org/10.18705/2311-4495-2021-8-4-47-56.

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Background. Electroencephalography is the main technique for assessing the functional state of the brain. Indications for EEG are diagnosis of paroxysmal states, prediction of the outcome of a pathological state, evaluation of bioelectrical activity if brain death is suspected. Up to 90 % of the native EEG in calm wakefulness in healthy individuals is occupied by “alpha activity”. In children in active wakefulness, the EEG pattern depends to a great extent on their age.Objective. The aim of the work was to assess EEG parameters in children aged 3–4 years in eyes-open resting state. Design and methods. 31 healthy participants aged 3–4 years were enrolled. EEG was registered for 30 minutes in a state of passive wakefulness in the supine position with open eyes. Average values of the power of the spectra for the alpha-rhythm, delta-rhythm and theta-rhythm in the frontal and temporal leads, as well as the ratio of the average power of alpha/theta and alpha/delta rhythms in the frontal and temporal leads were calculated.Results. Average power of the alpha-rhythm was significantly higher over the right frontal lobe than over the right frontal-temporal area, as well as average amplitude of it was significantly higher in F3-A1 than F7-A1, F4-A2 than F8-A2, which is associated with the articulatory praxis. Average alpha-rhythm power was significantly higher in T5-A1 than T3-A1 and T6-A2 than T4-A2, which corresponds to the recognition and naming of objects optically. Significant differences according to the total average power of the alpha- and theta-rhythms above the frontal and frontal-temporal regions reflect the relationship between the frontal cortex temporal lobes and the premotor zones, i.e. arcuate bundle, responsible for the “speech system”.Conclusion. The identified patterns can reflect the characteristics of the state of active wakefulness in a 3–4-year-old child and can be used for comparison in the future (both in the course of behavioral experiments and observation of patients with certain pathological processes).

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