Search results for 'EEG' (try it on Scholar)

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  1. Alexander A. Fingelkurts, Andrew A. Fingelkurts, Sakari Kallio & Antti Revonsuo (2007). HYPNOSIS INDUCES A CHANGED COMPOSITION OF BRAIN OSCILLATIONS IN EEG: A CASE STUDY. Contemporary Hypnosis 24 (1):3-18.score: 24.0
    Cognitive functions associated with the frontal lobes of the brain may be specifi cally involved in hypnosis. Thus, the frontal area of the brain has recently been (...)
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  2. Andrew A. Fingelkurts & Alexander A. Fingelkurts (2006). Timing in Cognition and EEG Brain Dynamics: Discreteness Versus Continuity. Cognitive Processing 7 (3):135-162.score: 24.0
    This article provides an overview of recent developments in solving the timing problem (discreteness vs. continuity) in cognitive neuroscience. Both theoretical and empirical studies have been considered, (...)
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  3. Alexander A. Fingelkurts, Andrew A. Fingelkurts, Sergio Bagnato, Cristina Boccagni & Giuseppe Galardi (2012). EEG Oscillatory States as Neuro-Phenomenology of Consciousness as Revealed From Patients in Vegetative and Minimally Conscious States. Consciousness and Cognition 21 (1):149-169.score: 24.0
    The value of resting electroencephalogram (EEG) in revealing neural constitutes of consciousness (NCC) was examined. We quantified the dynamic repertoire, duration and oscillatory type of EEG microstates (...)
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  4. Paul L. Nunez (2000). Toward a Quantitative Description of Large-Scale Neocortical Dynamic Function and EEG. Behavioral and Brain Sciences 23 (3):371-398.score: 24.0
    A general conceptual framework for large-scale neocortical dynamics based on data from many laboratories is applied to a variety of experimental designs, spatial scales, and brain (...)states. Partly distinct, but interacting local processes (e.g., neural networks) arise from functional segregation. Global processes arise from functional integration and can facilitate (top down) synchronous activity in remote cell groups that function simultaneously at several different spatial scales. Simultaneous local processes may help drive (bottom up) macroscopic global dynamics observed with electroencephalography (EEG) or magnetoencephalography (MEG). A local/global dynamic theory that is consistent with EEG data and the proposed conceptual framework is outlined. This theory is neutral about properties of neural networks embedded in macroscopic fields, but its global component makes several qualitative and semiquantitative predictions about EEG measures of traveling and standing wave phenomena. A more generalmetatheorysuggests what large-scale quantitative theories of neocortical dynamics may be like when more accurate treatment of local and nonlinear effects is achieved. The theory describes the dynamics of excitatory and inhibitory synaptic action fields. EEG and MEG provide large-scale estimates of modulation of these synaptic fields around background levels. Brain states are determined by neuromodulatory control parameters. Purely local states are dominated by local feedback gains and rise and decay times of postsynaptic potentials. Dominant local frequencies vary with brain region. Other states are purely global, with moderate to high coherence over large distances. Multiple global mode frequencies arise from a combination of delays in corticocortical axons and neocortical boundary conditions. Global frequencies are identical in all cortical regions, but most states involve dynamic interactions between local networks and the global system. EEG frequencies may involve amatchingof local resonant frequencies with one or more of the many, closely spaced global frequencies. Key Words: binding problem; cell assemblies; coherence; EEG; limit cycles; neocortical dynamics; pacemakers; phase locking; spatial scale; standing waves; synchronization. Footnotes1 The relationship between the synaptic action fields proposed in the target article and cell assemblies is clarified with Figure R1 (p. 416) of the Response. (This figure was not available to Commentators. (shrink)
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  5. Fanny Lachat, Laurent Hugeville, Jean-Didier Lemarechal, Laurence Conty & Nathalie George (2012). Oscillatory Brain Correlates of Live Joint Attention: A Dual-EEG Study. Frontiers in Human Neuroscience 6.score: 24.0
    Joint attention consists in following anothers gaze onto an environmental object, which leads to the alignment of both subjects' attention onto this object. It is a (...)fundamental mechanism of non-verbal communication, and it is essential for dynamic, online, interindividual synchronization during interactions. We aimed at investigating the oscillatory brain correlates of joint attention in a face-to-face paradigm where dyads of participants dynamically oriented their attention toward objects during joint and no-joint attention periods respectively. We also manipulated task instruction: in socially-driven instructions, the participants had to follow explicitly their partners gaze, while in color-driven instructions, the objects to be looked at were designated at by their color so that no explicit gaze following was required. We focused on oscillatory activities in the 10 Hz frequency range, where parieto-occipital alpha and the centro-parietal mu rhythms have been described, as these rhythms have been associated with attention and social coordination processes respectively. We tested the hypothesis of a modulation of these oscillatory activities by joint attention. We used dual EEG to record simultaneously the brain activities of the participant dyads during our live, face-to-face joint attention paradigm. We showed that joint attention periodsas compared to the no-joint attention periodswere associated with a decrease of signal power between 11 and 13 Hz over a large set of left centro-parieto-occipital electrodes, encompassing the scalp regions where alpha and mu rhythms have been described. This 11-13 Hz signal power decrease was observed independently of the task instruction: it was similar when joint versus no-joint attention situations were socially-driven and when they were color-driven. These results are interpreted in terms of the processes of attention mirroring, social coordination, and mutual attentiveness associated with joint attention state. (shrink)
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  6. Alexander A. Fingelkurts, Andrew A. Fingelkurts, Sergio Bagnato, Cristina Boccagni & Giuseppe Galardi (2013). The Value of Spontaneous EEG Oscillations in Distinguishing Patients in Vegetative and Minimally Conscious States. In Eror Basar & et all (eds.), Application of Brain Oscillations in Neuropsychiatric Diseases. Supplements to Clinical Neurophysiology. Elsevier. 81-99.score: 24.0
    Objective: The value of spontaneous EEG oscillations in distinguishing patients in vegetative and minimally conscious states was studied. Methods: We quantified dynamic repertoire of EEG oscillations in (...)
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  7. Michael Plöchl, José Pablo Ossandón & Peter König (2012). Combining EEG and Eye Tracking: Identification, Characterization, and Correction of Eye Movement Artifacts in Electroencephalographic Data. Frontiers in Human Neuroscience 6.score: 24.0
    Eye movements introduce large artifacts to electroencephalographic recordings (EEG) and thus render data analysis difficult or even impossible. Trials contaminated by eye movement and blink artifacts have (...)
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  8. Andrew A. Fingelkurts, Alexander A. Fingelkurts, Sergio Bagnato, Cristina Boccagni & Giuseppe Galardi (2013). Prognostic Value of Resting-State EEG Structure in Disentangling Vegetative and Minimally Conscious States: A Preliminary Study. Neurorehabilitation and Neural Repair 27 (4):345-354.score: 24.0
    Background: Patients in a vegetative state pose problems in diagnosis, prognosis and treatment. Currently, no prognostic markers predict the chance of recovery, which has serious consequences, especially (...)
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  9. Gennady G. Knyazev (2013). EEG Correlates of Self-Referential Processing. Frontiers in Human Neuroscience 7.score: 24.0
    Self-referential processing has been principally investigated using functional magnetic resonance imaging (fMRI). However, understanding of the brain functioning is not possible without careful comparison of the (...)evidence coming from different methodological domains. This paper aims to review electroencephalographic (EEG) studies of self-referential processing and to evaluate how they correspond, complement, or contradict the existing fMRI evidence. There are potentially two approaches to the study of EEG correlates of self-referential processing. Firstly, because simultaneous registration of EEG and fMRI has become possible, the degree of overlap between these two signals in brain regions related to self-referential processing could be determined. Second and more direct approach would be the study of EEG correlates of self-referential processing per se. In this review, I discuss studies, which employed both these approaches and show that in line with fMRI evidence, EEG correlates of self-referential processing are most frequently found in brain regions overlapping with the default network, particularly in the medial prefrontal cortex. In the time domain, the discrimination of self- and others-related information is mostly associated with the P300 ERP component, but sometimes is observed even earlier. In the frequency domain, different frequency oscillations have been shown to contribute to self-referential processing, with spontaneous self-referential mentation being mostly associated with the alpha frequency band. (shrink)
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  10. R. L. Gordon, C. L. Magne & E. W. Large (2010). EEG Correlates of Song Prosody: A New Look at the Relationship Between Linguistic and Musical Rhythm. Frontiers in Psychology 2:352-352.score: 24.0
    Song composers incorporate linguistic prosody into their music when setting words to melody, a process calledtextsetting”. Composers tend to align the expected stress of the lyrics (...)
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  11. Gert Pfurtscheller, Teodoro Solis Escalante, Robert J. Barry, Daniela Sabine Klobassa, Christa Neuper & Gernot Mueller-Putz (2013). Brisk Heart Rate and EEG Changes During Execution and Withholding of Cue-Paced Foot Motor Imagery. Frontiers in Human Neuroscience 7.score: 24.0
    Cue-paced motor imagery is a frequently used mental strategy to realize a Brain-Computer Interace (BCI). Recently it has been reported that 2 motor imagery tasks can (...) be separated with a high accuracy within the first second after cue presentation onset. To investigate this phenomenon in detail we studied the dynamics of motor cortex beta oscillations in EEG and the changes in heart rate (HR) during visual cue-paced foot imagery using a go (execution of imagery) versus nogo (withholding of imagery) paradigm in 16 healthy subjects. Both execution and withholding of motor imagery resulted in a brisk centrally localized beta ERD with a maximum at ~ 400 ms and a concomitant HR deceleration. We found that response patterns within the first second after stimulation differed between conditions. The ERD was significantly larger in go as compared to nogo. In contrast the HR deceleration was somewhat smaller and followed by an acceleration in go as compared to nogo. These findings suggest that the early beta ERD reflects visually induced preparatory activity in motor cortex networks. Both the early beta ERD and HR deceleration are the result of automatic operating processes that are likely part of the orienting reflex. Of interest, however, is that the preparatory cortical activity is strengthened and the HR modulated already within the first second after stimulation during the execution of motor imagery. The subtraction of the HR time course of the nogo from the go condition revealed a slight HR acceleration in the first seconds most likely due to the increased mental effort associated with the imagery process. (shrink)
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  12. Stefania Maggi Amedeo D'Angiulli, Joanne Weinberg, Tim F. Oberlander, Ruth E. Grunau, Clyde Hertzman (2012). Frontal EEG/ERP Correlates of Attentional Processes, Cortisol and Motivational States in Adolescents From Lower and Higher Socioeconomic Status. Frontiers in Human Neuroscience 6.score: 24.0
    Event-related potentials (ERPs) and other electroencephalographic (EEG) evidence show that frontal brain areas of higher and lower socioeconomic status (SES) children are recruited differently during selective (...)attention tasks. We assessed whether multiple variables related to self-regulation (perceived mental effort) emotional states (e.g., anxiety, stress, etc.) and motivational states (e.g., boredom, engagement, etc.) may co-occur or interact with frontal attentional processing probed in two matched-samples of fourteen lower-SES and higher-SES adolescents. ERP and EEG activation were measured during a task probing selective attention to sequences of tones. Pre- and post-task salivary cortisol and self-reported emotional states were also measured. At similar performance level, the higher-SES group showed a greater ERP differentiation between attended (relevant) and unattended (irrelevant) tones than the lower-SES group. EEG power analysis revealed a cross-over interaction, specifically, lower-SES adolescents showed significantly higher theta power when ignoring rather than attending to tones, whereas, higher-SES adolescents showed the opposite pattern. Significant theta asymmetry differences were also found at midfrontal electrodes indicating left hypo-activity in lower-SES adolescents. The attended vs. unattended difference in right midfrontal theta increased with individual SES rank, and (independently from SES) with lower cortisol task reactivity and higher boredom. Results suggest lower-SES children used additional compensatory resources to monitor/control response inhibition to distracters, perceiving also more mental effort, as compared to higher-SES counterparts. Nevertheless, stress, boredom and other task-related perceived states were unrelated to SES. Ruling out presumed confounds, this study confirms the midfrontal mechanisms responsible for the SES effects on selective attention reported previously and here reflect genuine cognitive differences. (shrink)
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  13. Peter Heil Benedikt Zoefel (2013). Detection of Near-Threshold Sounds is Independent of EEG Phase in Common Frequency Bands. Frontiers in Psychology 4.score: 24.0
    Low-frequency oscillations in the electroencephalogram (EEG) are thought to reflect periodic excitability changes of large neural networks. Consistent with this notion, detection probability of near-threshold somatosensory (...), visual, and auditory targets has been reported to co-vary with the phase of oscillations in the EEG. In audition, entrainment of δ-oscillations to the periodic occurrence of sounds has been suggested to function as a mechanism of attentional selection. Here, we examine in humans whether the detection of brief near-threshold sounds in quiet depends on the phase of EEG oscillations. When stimuli were presented at irregular intervals, we did not find a systematic relationship between detection probability and phase. When stimuli were presented at regular intervals (2-s), reaction times were significantly shorter and we observed phase entrainment of EEG oscillations corresponding to the frequency of stimulus presentation (0.5 Hz), revealing an adjustment of the system to the regular stimulation. The amplitude of the entrained oscillation was higher for hits than for misses, suggesting a link between entrainment and stimulus detection. However, detection was independent of phase at frequencies1 Hz. Furthermore, we show that when the data are analyzed using acausal, though common, algorithms, an apparententrainmentof the δ-phase to presented stimuli emerges and detection probability appears to depend on δ-phase, similar to reports in the literature. We show that these effects are artifacts from phase distortion at stimulus onset by contamination with the event-related potential, which differs markedly for hits and misses. This highlights the need to carefully deal with this common problem, since otherwise it might bias and mislead this exciting field of research. (shrink)
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  14. Giacomo Della Marca Claudio Imperatori, Benedetto Farina, Riccardo Brunetti, Valentina Gnoni, Elisa Testani, Maria I. Quintiliani, Claudia Del Gatto, Allegra Indraccolo, Anna Contardi, Anna M. Speranza (2013). Modifications of EEG Power Spectra in Mesial Temporal Lobe During N-Back Tasks of Increasing Difficulty. A sLORETA Study. Frontiers in Human Neuroscience 7.score: 24.0
    The n-back task is widely used to investigate the neural basis of Working Memory (WM) processes. The principal aim of this study was to explore and (...)compare the EEG power spectra during two n-back tests with different levels of difficulty (1-back vs 3-back). Fourteen healthy subjects were enrolled (7 men and 7 women, mean age 31.21±7.05 years, range: 23-48). EEG was recorded while performing the N-back test, by means of 19 surface electrodes referred to joint mastoids. EEG analysis were conducted by means of the standardized LOw Resolution brain Electric Tomography (sLORETA) software. The statistical comparison between EEG power spectra in the two conditions was performed using paired t-statistics on the coherence values after Fishers z transformation available in the LORETA program package. The frequency bands considered were: delta (0.5-4 Hz); theta (4.57.5 Hz); alpha (812.5 Hz); beta (1330 Hz); gamma (30.5100 Hz). Significant changes occurred in the delta band: in the 3-back condition an increased delta power was localized in a brain region corresponding to the Brodmann Area (BA) 28 in the left posterior entorhinal cortex (T = 3.112; p<0.05) and in the BA 35 in the left peririnhal cortex in the parahippocampal gyrus (T = 2.876; p<0.05). No significant differences were observed in the right hemisphere and in the alpha, theta, beta and gamma frequency bands. Our results indicate that the most prominent modification induced by the increased complexity of the task occur in the mesial left temporal lobe structures. (shrink)
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  15. Heike Eichele, Hilde Juvodden, Markus Ullsperger & Tom Eichele (2010). Mal-Adaptation of Event-Related EEG Responses Preceding Performance Errors. Frontiers in Human Neuroscience 4.score: 24.0
    Recent EEG and fMRI evidence suggests that behavioral errors are foreshadowed by systematic changes in brain activity preceding the outcome by seconds. In order to further characterize (...)
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  16. A. K. Kuhlen, C. Allefeld & J. D. Haynes (2011). Content-Specific Coordination of Listeners' to Speakers' EEG During Communication. Frontiers in Human Neuroscience 6:266-266.score: 24.0
    Cognitive neuroscience has recently begun to extend its focus from the isolated individual mind to two or more individuals coordinating with each other. In this study we (...)
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  17. Matthias Christoph Meyer, Ronald Johannes Janssen, Erik Sophius Bartus Van Oort, Christian F. Beckmann & Markus Barth (2013). The Quest for EEG Power Band Correlation with ICA Derived fMRI Resting State Networks. Frontiers in Human Neuroscience 7.score: 24.0
    The neuronal underpinnings of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) resting state networks (RSNs) are still unclear. To investigate the underlying mechanisms, specifically (...)
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  18. Juliane Britz Michael A. Pitts (2011). Insights From Intermittent Binocular Rivalry and EEG. Frontiers in Human Neuroscience 5.score: 24.0
    Novel stimulation and analytical approaches employed in EEG studies of ambiguous figures have recently been applied to binocular rivalry. The combination of intermittent stimulus presentation and EEG (...)
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  19. Elizabeth Milne (2011). Increased Intra-Participant Variability in Children with Autistic Spectrum Disorders: Evidence From Single-Trial Analysis of Evoked EEG. Frontiers in Psychology 2:51.score: 24.0
    Intra-participant variability in clinical conditions such as autistic spectrum disorder (ASD) is an important indicator of pathophysiological processing. The data reported here illustrate that trial-by-trial (...)
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  20. Patrick David Schelenz, Martin Klasen, Barbara Reese, Christina Regenbogen, Dhana Wolf, Yutaka Kato & Klaus Mathiak (2013). Multisensory Integration of Dynamic Emotional Faces and Voices: Method for Simultaneous EEG-fMRI Measurements. Frontiers in Human Neuroscience 7.score: 24.0
    Combined EEG-fMRI analysis correlates time courses from single electrodes or independent EEG components with the hemodynamic response. Implementing information from only one electrode, however, may miss (...)relevant information from complex electrophysiological networks. Component based analysis, in turn, depends on a priori knowledge of the signal topography. Complex designs such as studies on multisensory integration of emotions investigate subtle differences in distributed networks based on only a few trials per condition. Thus, they require a sensitive and comprehensive approach which does not rely on a-priori knowledge about the underlying neural processes. In this pilot study, feasibility and sensitivity of source localization-driven analysis for EEG-fMRI was tested using a multisensory integration paradigm. Dynamic audiovisual stimuli consisting of emotional talking faces and pseudowords with emotional prosody were rated in a delayed response task. The trials comprised affectively congruent and incongruent displays.In addition to event-locked EEG and fMRI analyses, induced oscillatory EEG responses at estimated cortical sources and in specific temporo-spectral windows were correlated with the corresponding BOLD responses. EEG analysis showed high data quality with less than 10% trial rejection. In an early time window, alpha oscillations were suppressed in bilateral occipital cortices and fMRI analysis confirmed high data quality with reliable activation in auditory, visual and frontal areas to the presentation of multisensory stimuli. In line with previous studies, we obtained reliable correlation patterns for event locked occipital alpha suppression and BOLD signal time course.Our results suggest a valid methodological approach to investigate complex stimuli using the present source localization driven method for EEG-fMRI. This novel procedure may help to investigate combined EEG-fMRI data from novel complex paradigms with high spatial and temporal resolution. (shrink)
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  21. Vincenzo Crunelli Stuart W. Hughes, Magor L. Lőrincz, Kate Blethyn, Katalin A. Kékesi, Gábor Juhász, Mark Turmaine, John G. Parnavelas (2011). Thalamic Gap Junctions Control Local Neuronal Synchrony and Influence Macroscopic Oscillation Amplitude During EEG Alpha Rhythms. Frontiers in Psychology 2.score: 24.0
    Although EEG alpha () (8-13 Hz) rhythms are often considered to reflect anidlingbrain state, numerous studies indicate that they are also related to many aspects (...)
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  22. Joydeep Bhattacharya Susanne Reiterer, Ernesto Pereda (2011). On a Possible Relationship Between Linguistic Expertise and EEG Gamma Band Phase Synchrony. Frontiers in Psychology 2.score: 24.0
    Recent research has shown that extensive training in and exposure to a second language can modify the language organization in the brain by causing both structural and (...)
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  23. Blake E. Butler & Laurel J. Trainor (2012). Sequencing the Cortical Processing of Pitch-Evoking Stimuli Using EEG Analysis and Source Estimation. Frontiers in Psychology 3.score: 24.0
    Cues to pitch include spectral cues that arise from tonotopic organization and temporal cues that arise from firing patterns of auditory neurons. fMRI studies suggest a common (...)
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  24. Amedeo D'angiulli, Patricia Maria Van Roon, Joanne Weinberg, Tim F. Oberlander, Ruth E. Grunau, Clyde Hertzman & Stefania Maggi (2012). Frontal EEG/ERP Correlates of Attentional Processes, Cortisol and Motivational States in Adolescents From Lower and Higher Socioeconomic Status. Frontiers in Human Neuroscience 6.score: 24.0
    Event-related potentials (ERPs) and other electroencephalographic (EEG) evidence show that frontal brain areas of higher and lower socioeconomic status (SES) children are recruited differently during selective (...)attention tasks. We assessed whether multiple variables related to self-regulation (perceived mental effort) emotional states (e.g., anxiety, stress, etc.) and motivational states (e.g., boredom, engagement, etc.) may co-occur or interact with frontal attentional processing probed in two matched-samples of fourteen lower-SES and higher-SES adolescents. ERP and EEG activation were measured during a task probing selective attention to sequences of tones. Pre- and post-task salivary cortisol and self-reported emotional states were also measured. At similar performance level, the higher-SES group showed a greater ERP differentiation between attended (relevant) and unattended (irrelevant) tones than the lower-SES group. EEG power analysis revealed a cross-over interaction, specifically, lower-SES adolescents showed significantly higher theta power when ignoring rather than attending to tones, whereas, higher-SES adolescents showed the opposite pattern. Significant theta asymmetry differences were also found at midfrontal electrodes indicating left hypo-activity in lower-SES adolescents. The attended vs. unattended difference in right midfrontal theta increased with individual SES rank, and (independently from SES) with lower cortisol task reactivity and higher boredom. Results suggest lower-SES children used additional compensatory resources to monitor/control response inhibition to distracters, perceiving also more mental effort, as compared to higher-SES counterparts. Nevertheless, stress, boredom and other task-related perceived states were unrelated to SES. Ruling out presumed confounds, this study confirms the midfrontal mechanisms responsible for the SES effects on selective attention reported previously and here reflect genuine cognitive differences. (shrink)
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  25. Megan DeBettencourt, Robin I. Goldman, Truman R. Brown & Paul Sajda (2011). Adaptive Thresholding for Improving Sensitivity in Single-Trial Simultaneous EEG/fMRI. Frontiers in Psychology 2.score: 24.0
    A common approach used to fuse simultaneously recorded EEG and fMRI is to correlate trial-by-trial variability in the EEG, or variability of components derived therefrom, with (...) the BOLD response. When this correlation is done using the conventional univariate approach, for example with the general linear model (GLM), there is the usual problem of correcting the statistics for multiple comparisons. Cluster thresholding is often used as the correction of choice, though in many cases it is utilized in an ad hoc way, for example by employing the same cluster thresholds for both traditional regressors (stimulus or behaviorally derived) and EEG-derived regressors. In this paper we describe a resampling procedure that takes into account the a priori statistics of the trial-to-trial variability of the EEG-derived regressors in a way that trades off cluster size and maximum voxel z-score to properly correct for multiple comparisons. We show that this data adaptive procedure improves sensitivity for smaller clusters of activation, without sacrificing the specificity of the results. Our results suggest that extra care is needed in correcting statistics when the regressor model is derived from noisy and/or uncertain measurements, as is the case for regressors constructed from single-trial variations in the EEG. (shrink)
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  26. Scott Makeig Diane Whitmer, Gregory Worrell, Matt Stead, Il Keun Lee (2010). Utility of Independent Component Analysis for Interpretation of Intracranial EEG. Frontiers in Human Neuroscience 4.score: 24.0
    Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings (...)
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  27. Hillel Pratt Goded Shahaf (2013). Thorough Specification of the Neurophysiologic Processes Underlying Behavior and of Their Manifestation in EEGDemonstration with the Go/No-Go Task. Frontiers in Human Neuroscience 7.score: 24.0
    In this work we demonstrate the principles of a systematic modeling approach of the neurophysiologic processes underlying a behavioral function. The modeling is based upon a flexible (...)
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  28. Peter J. Bayley Linda Isaac (2012). Eeg Coherence Between Prefrontal and Posterior Cortical Regions is Related to Negative Personality Traits. Frontiers in Human Neuroscience 6.score: 24.0
    Eeg Coherence between Prefrontal and Posterior Cortical Regions is Related to Negative Personality Traits.
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  29. Bert De Smedt Roland H. Grabner (2012). Oscillatory EEG Correlates of Arithmetic Strategies: A Training Study. Frontiers in Psychology 3.score: 24.0
    There has been a long tradition of research on mathematics education showing that children and adults use different strategies to solve arithmetic problems. Neurophysiological studies have recently (...)
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  30. Douglas McLelland Rufin VanRullen (2013). What Goes Up Must Come Down: EEG Phase Modulates Auditory Perception in Both Directions. Frontiers in Psychology 4.score: 24.0
    What goes up must come down: EEG phase modulates auditory perception in both directions.
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  31. Julien Dubois R. VanRullen, N. A. Busch, J. Drewes (2011). Ongoing EEG Phase as a Trial-by-Trial Predictor of Perceptual and Attentional Variability. Frontiers in Psychology 2.score: 24.0
    Even in well-controlled laboratory environments, apparently identical repetitions of an experimental trial can give rise to highly variable perceptual outcomes and behavioral responses. This variability is (...)generally discarded as a reflection of intrinsic noise in neuronal systems. However, part of this variability may be accounted for by trial-by-trial fluctuations of the phase of ongoing oscillations at the moment of stimulus presentation. For example, the phase of an electro-encephalogram (EEG) oscillation reflecting the rapid waxing and waning of sustained attention can predict the perception of a subsequent visual stimulus at threshold. Similar ongoing periodicities account for a portion of the trial-by-trial variability of visual reaction times. We review the available experimental evidence linking ongoing EEG phase to perceptual and attentional variability, and the corresponding methodology. We propose future tests of this relation, and discuss the theoretical implications for understanding the neuronal dynamics of sensory perception. (shrink)
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  32. Manish Saggar, Brandon G. King, Anthony P. Zanesco, Katherine A. MacLean, Stephen R. Aichele, Tonya L. Jacobs, David A. Bridwell, Phillip R. Shaver, Erika L. Rosenberg, Baljinder K. Sahdra, Emilio Ferrer, Akaysha C. Tang, George R. Mangun, B. Alan Wallace, Risto Miikkulainen & Clifford D. Saron (2012). Intensive Training Induces Longitudinal Changes in Meditation State-Related EEG Oscillatory Activity. Frontiers in Human Neuroscience 6:256-256.score: 24.0
    The capacity to focus ones attention for an extended period of time can be increased through training in contemplative practices. However, the cognitive processes engaged during (...)meditation that support trait changes in cognition are not well characterized. We conducted a longitudinal wait-list controlled study of intensive meditation training. Retreat participants practiced focused attention meditation techniques for three months during an initial retreat. Wait-list participants later undertook formally identical training during a second retreat. Dense-array scalp-recorded electroencephalogram (EEG) data were collected during six minutes of mindfulness of breathing meditation at three assessment points during each retreat. Second-order blind source separation, along with a novel semi-automatic artifact removal tool, was used for data preprocessing. We observed replicable reductions in meditative state-related beta-band power bilaterally over anteriocentral and posterior scalp regions. In addition, individual alpha frequency decreased across both retreats and in direct relation to the amount of meditative practice. These findings provide evidence for replicable longitudinal changes in brain oscillatory activity during meditation and increase our understanding of the cortical processes engaged during meditation that may support long-term improvements in cognition. (shrink)
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  33. Marco Zanon, Piero Paolo Battaglini, Joanna Jarmolowska, Gilberto Pizzolato & Pierpaolo Busan (2013). Long-Range Neural Activity Evoked by Premotor Cortex Stimulation: a TMS/EEG Co-Registration Study. Frontiers in Human Neuroscience 7.score: 24.0
    The premotor cortex is one of the fundamental structures composing the neural networks of the human brain. It is implicated in many behaviors and cognitive tasks, ranging (...)
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  34. Andrew A. Fingelkurts, Alexander A. Fingelkurts, Sakari Kallio & Antti Revonsuo (2007). Cortex Functional Connectivity as a Neurophysiological Correlate of Hypnosis: An EEG Case Study. Neuropsychologia 45 (7):14521462.score: 21.0
    Cortex functional connectivity associated with hypnosis was investigated in a single highly hypnotizable subject in a normal baseline condition and under neutral hypnosis during two sessions separated (...)
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  35. Allison B. Sekuler Guillaume A. Rousselet, Carl M. Gaspar, Cyril R. Pernet, Jesse S. Husk, Patrick J. Bennett (2010). Healthy Aging Delays Scalp EEG Sensitivity to Noise in a Face Discrimination Task. Frontiers in Psychology 1.score: 21.0
    We used a single-trial ERP approach to quantify age-related changes in the time-course of noise sensitivity. A total of 62 healthy adults, aged between 19 (...)
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  36. Tuomas Mutanen (2013). TMS-Evoked Changes in Brain-State Dynamics Quantified by Using EEG Data. Frontiers in Human Neuroscience 7.score: 21.0
  37. Minkyu Ahn, Sangtae Ahn, Jun H. Hong, Hohyun Cho, Kiwoong Kim, Bong S. Kim, Jin W. Chang & Sung C. Jun (2013). Gamma Band Activity Associated with BCI Performance: Simultaneous MEG/EEG Study. Frontiers in Human Neuroscience 7.score: 21.0
  38. Pouya Ahmadian, Stefano Cagnoni & Luca Ascari (2013). How Capable is Non-Invasive EEG Data of Predicting the Next Movement? A Mini Review. Frontiers in Human Neuroscience 7.score: 21.0
  39. Katia Lehongre, Benjamin Morillon, Anne-Lise Giraud & Franck Ramus (2013). Impaired Auditory Sampling in Dyslexia: Further Evidence From Combined fMRI and EEG. Frontiers in Human Neuroscience 7.score: 21.0
  40. Pamela K. Douglas, Edward Lau, Ariana Anderson, Austin Head, Wesley Kerr, Margalit Wollner, Daniel Moyer, Wei Li, Mike Durnhofer, Jennifer Bramen & Mark S. Cohen (2013). Single Trial Decoding of Belief Decision Making From EEG and fMRI Data Using Independent Components Features. Frontiers in Human Neuroscience 7.score: 21.0
  41. Paolo Manganotti, Emanuela Formaggio, Alessandra Del Felice, Silvia F. Storti, Alessandro Zamboni, Alessandra Bertoldo, Antonio Fiaschi & Gianna M. Toffolo (2013). Time-Frequency Analysis of Short-Lasting Modulation of EEG Induced by TMS During Wake, Sleep Deprivation and Sleep. Frontiers in Human Neuroscience 7.score: 21.0
  42. Kyle E. Mathewson, Alejandro Lleras, Diane M. Beck, Monica Fabiani, Tony Ro & Gabriele Gratton (2011). Pulsed Out of Awareness: EEG Alpha Oscillations Represent a Pulsed-Inhibition of Ongoing Cortical Processing. Frontiers in Psychology 2.score: 21.0
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  43. M. Alessandra Umilta, Cristina Berchio, Mariateresa Sestito, David Freedberg & Vittorio Gallese (2012). Abstract Art and Cortical Motor Activation: an EEG Study. Frontiers in Human Neuroscience 6.score: 21.0
    The role of the motor system in the perception of visual art remains to be better understood. Earlier studies on the visual perception of abstract art (from (...)
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  44. Wolfgang Klimesch (2013). An Algorithm for the EEG Frequency Architecture of Consciousness and Brain Body Coupling. Frontiers in Human Neuroscience 7.score: 21.0
  45. Frank Leavitt (1968). Eeg Activation and Reaction Time. Journal of Experimental Psychology 77 (2):194.score: 21.0
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  46. Vittorio Gallese M. Alessandra Umilta', Cristina Berchio, Mariateresa Sestito, David Freedberg (2012). Abstract Art and Cortical Motor Activation: an EEG Study. Frontiers in Human Neuroscience 6.score: 21.0
    The role of the motor system in the perception of visual art remains to be better understood. Earlier studies on the visual perception of abstract art (from (...)
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  47. J. J. Wright & D. T. J. Liley (1996). Dynamics of the Brain at Global and Microscopic Scales: Neural Networks and the EEG. Behavioral and Brain Sciences 19 (2):285.score: 21.0
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  48. R. W. Thatcher, J. F. Gomez-Molina, C. Biver, D. North, R. Curtin & R. W. Walker (2000). Two Compartmental Models of EEG Coherence and MRI Biophysics. Behavioral and Brain Sciences 23 (3):412-412.score: 18.0
    Studies have shown that as MRI T2 relaxation time lengthens there is a shift toward more unbound orfree-waterand less partitioning of the protein/lipid molecules (...) per unit volume. A shift toward less water partitioning or lengthened MRI T2 relaxation time is linearly related to reduced high frequency EEG amplitude, reduced short distance EEG coherence, increased long distance EEG coherence, and reduced cognitive functioning (Thatcher et al. 1998a; 1998b). (shrink)
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  49. Frederick T. Travis & R. K. Wallace (1999). Autonomic and EEG Patterns During Eyes-Closed Rest and Transcendental Meditation (TM) Practice: The Basis for a Neural Model of TM Practice. Consciousness and Cognition 8 (3):302-318.score: 18.0
    In this single-blind within-subject study, autonomic and EEG variables were compared during 10-min, order-balanced eyes-closed rest and Transcendental Meditation (TM) sessions. TM sessions were (...) distinguished by (1) lower breath rates, (2) lower skin conductance levels, (3) higher respiratory sinus arrhythmia levels, and (4) higher alpha anterior-posterior and frontal EEG coherence. Alpha power was not significantly different between conditions. These results were seen in the first minute and were maintained throughout the 10-min sessions. TM practice appears to (1) lead to a state fundamentally different than eyes-closed rest; (2) result in a cascade of events in the central and autonomic nervous systems, leading to a rapid change in state (within a minute) that was maintained throughout the TM session; and (3) be best distinguished from other conditions through autonomic and EEG alpha coherence patterns rather than alpha power. Two neural networks that may mediate these effects are suggested. The rapid shift in physiological functioning within the first minute might be mediated by a ''neural switch'' in prefrontal areas inhibiting activity in specific and nonspecific thalamocortical circuits. The resulting ''restfully alert'' state might be sustained by a basal ganglia-corticothalamic threshold regulation mechanism automatically maintaining lower levels of cortical excitability. (shrink)
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  50. Gerd Grübler, Abdul Al-Khodairy, Robert Leeb, Iolanda Pisotta, Angela Riccio, Martin Rohm & Elisabeth Hildt (2014). Psychosocial and Ethical Aspects in Non-Invasive EEG-Based BCI ResearchA Survey Among BCI Users and BCI Professionals. Neuroethics 7 (1):29-41.score: 18.0
    In this paper, the results of a pilot interview study with 19 subjects participating in an EEG-based non-invasive braincomputer interface (BCI) research study on stroke (...)
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