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

336 found
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  1. Yu Bai, Timothy Lane, Georg Northoff & et al (2015). Resting State Glutamate Predicts Elevated Pre-Stimulus Alpha During Self-Relatedness: A Combined EEG-MRS Study on 'Rest-Self' Overlap. Social Neuroscience:DOI:10.1080/17470919.2015.107258.
    Recent studies have demonstrated neural overlap between resting state activity and self-referential processing. Thisrest-selfoverlap occurs especially in anterior cortical midline structures like the perigenual (...) anterior cingulate cortex (PACC). However, the exact neurotemporal and biochemical mechanisms remain to be identified. Therefore, we conducted a combined electroencephalography (EEG)-magnetic resonance spectroscopy (MRS) study. EEG focused on pre-stimulus (e.g., prior to stimulus presentation or perception) power changes to assess the degree to which those changes can predict subjectsperception (and judgment) of subsequent stimuli as high or low self-related. MRS measured resting state concentration of glutamate, focusing on PACC. High pre-stimulus (e.g., prior to stimulus presentation or perception) alpha power significantly correlated with both perception of stimuli judged to be highly self-related and with resting state glutamate concentrations in the PACC. In sum, our results show (i) pre-stimulus (e.g., prior to stimulus presentation or perception) alpha power and resting state glutamate concentration to mediate rest-self overlap that (ii) dispose or incline subjects to assign high degrees of self-relatedness to perceptual stimuli. (shrink)
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  2.  3
    Andrew A. Fingelkurts, Alexander A. Fingelkurts, Sergio Bagnato, Cristina Boccagni & Giuseppe Galardi (2016). Long-Term (Six Years) Clinical Outcome Discrimination of Patients in the Vegetative State Could Be Achieved Based on the Operational Architectonics EEG Analysis: A Pilot Feasibility Study. The Open Neuroimaging Journal 10:69-79.
    Electroencephalogram (EEG) recordings are increasingly used to evaluate patients with disorders of consciousness (DOC) or assess their prognosis outcome in the short-term perspective. However, there is (...)a lack of information concerning the effectiveness of EEG in classifying long-term (many years) outcome in chronic DOC patients. Here we tested whether EEG operational architectonics parameters (geared towards consciousness phenomenon detection rather than neurophysiological processes) could be useful for distinguishing a very long-term (6 years) clinical outcome of DOC patients whose EEGs were registered within 3 months post-injury. The obtained results suggest that EEG recorded at third month after sustaining brain damage, may contain useful information on the long-term outcome of patients in vegetative state: it could discriminate patients who remain in a persistent vegetative state from patients who reach a minimally conscious state or even recover a full consciousness in a long-term perspective (6 years) post-injury. These findings, if confirmed in further studies, may be pivotal for long-term planning of clinical care, rehabilitative programs, medical-legal decisions concerning the patients, and policy makers. (shrink)
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  3.  81
    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.
    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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  4.  53
    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.
    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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  5.  23
    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.
    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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  6.  65
    Andrew A. Fingelkurts & Alexander A. Fingelkurts (2006). Timing in Cognition and EEG Brain Dynamics: Discreteness Versus Continuity. Cognitive Processing 7 (3):135-162.
    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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  7.  73
    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.
    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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  8.  20
    Paul L. Nunez (2000). Toward a Quantitative Description of Large-Scale Neocortical Dynamic Function and EEG. Behavioral and Brain Sciences 23 (3):371-398.
    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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  9.  47
    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.
    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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  10.  2
    Claudio Carvalhaes & de Barros (2015). A Review of the Method of Using the Scalp Electric Field in EEG Analysis. [REVIEW] Cosmos and History: The Journal of Natural and Social Philosophy 11 (2):154-159.
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  11.  2
    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.
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  12. Frank Leavitt (1968). Eeg Activation and Reaction Time. Journal of Experimental Psychology 77 (2):194.
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  13.  18
    Jon A. Frederick (2012). Psychophysics of EEG Alpha State Discrimination. Consciousness and Cognition 21 (3):1345-1354.
    Nearly all research in neurofeedback since the 1960s has focused on training voluntary control over EEG constructs. By contrast, EEG state discrimination training focuses on awareness of (...)
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  14.  28
    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.
    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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  15.  6
    Jana Speth, Clemens Frenzel & Ursula Voss (2013). A Differentiating Empirical Linguistic Analysis of Dreamer Activity in Reports of EEG-Controlled REM-Dreams and Hypnagogic Hallucinations. Consciousness and Cognition 22 (3):1013-1021.
    We present Activity Analysis as a new method for the quantification of subjective reports of altered states of consciousness with regard to the indicated level of simulated (...)
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  16.  36
    F. Vialatte (2009). EEG Paroxysmal Gamma Waves During Bhramari Pranayama: A Yoga Breathing Technique. Consciousness and Cognition 18 (4):977-988.
    Here we report that a specific form of yoga can generate controlled high-frequency gamma waves. For the first time, paroxysmal gamma waves were observed in eight (...)subjects practicing a yoga technique of breathing control called Bhramari Pranayama . To obtain new insights into the nature of the EEG during BhPr, we analyzed EEG signals using time-frequency representations , independent component analysis , and EEG tomography . We found that the PGW consists of high-frequency biphasic ripples. This unusual activity is discussed in relation to previous reports on yoga and meditation. It is concluded this EEG activity is most probably non-epileptic, and that applying the same methodology to other meditation recordings might yield an improved understanding of the neurocorrelates of meditation. (shrink)
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  17.  19
    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.
    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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  18.  39
    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.
    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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  19.  21
    Richard Griffin, Infant EEG Activity as a Biomarker for Autism: a Promising Approach or a False Promise?
    The ability to determine an infants likelihood of developing autism via a relatively simple neurological measure would constitute an important scientific breakthrough. In their recent publication (...)in this journal, Bosl and colleagues claim that a measure of EEG complexity can be used to detect, with very high accuracy, infants at high risk for autism (HRA). On the surface, this appears to be that very scientific breakthrough and as such the paper has received widespread media attention. But a close look at how these high accuracy rates were derived tells a very different story. This stems from a conflation betweenhigh riskas a population-level property andhigh riskas a property of an individual. We describe the.. (shrink)
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  20.  7
    Lester Ingber (2000). Statistical Mechanics of Neocortical Interactions: EEG Eigenfunctions of Short-Term Memory. Behavioral and Brain Sciences 23 (3):403-405.
    This commentary focuses on how bottom-up neocortical models can be developed into eigenfunction expansions of probability distributions appropriate to describe short-term memory in the context of (...) scalp EEG. The mathematics of eigenfunctions are similar to the top-down eigenfunctions developed by Nunez, despite different physical manifestations. The bottom-up eigenfunctions are at the local mesocolumnar scale, whereas the top-down eigenfunctions are at the global regional scale. Our respective approaches have regions of substantial overlap, and future studies may expand top-down eigenfunctions into the bottom-up eigenfunctions, yielding a model of scalp EEG expressed in terms of columnar states of neocortical processing of attention and short-term memory. Footnotes1 The author is also affiliated with DRW Investments LLC, 311 S. Wacker Drive, Chicago, IL 60606. (shrink)
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  21.  9
    Colin Andrew (2000). Sensorimotor EEG Rhythms and Their Connection to Local/Global Neocortical Dynamic Theory. Behavioral and Brain Sciences 23 (3):399-400.
    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. (shrink)
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  22.  8
    Sanford I. Nidich, Robert A. Ryncarz, Allan I. Abrams, David Orme‐Johnson & Robert Keith Wallace (1983). Kohlbergian Cosmic Perspective Responses, EEG Coherence, and the TM and TMSidhi Programme. Journal of Moral Education 12 (3):166-173.
    Abstract While considerable attention has been given to Kohlberg's stages of moral reasoning, little effort has been given to studying Kohlberg's notion of a metaphorical Stage (...) Seven, which presupposes a cosmic rather than a universal humanistic orientation. The purpose of this study was to determine whether EEG coherence can distinguish cosmic orientation responses from non?cosmic orientation responses to the question, ?Why be moral??. Thirteen cosmic orientation candidates were compared with thirteen non?cosmic orientation subjects, matched for age, using EEG coherence measures. Results indicate that cosmic orientation candidates exhibited higher EEG bilateral frontal alpha coherence than non?cosmic orientation subjects (p< .01). In addition, a positive correlation was found between the Principled Thinking Scale of Rest's Defining Issues Test and right homolateral EEG coherence (rs = .46, p< .05). Discussion of the results, along with suggestions for further research and implications for education are included. (shrink)
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  23.  2
    M. Kukleta, P. Bob, M. Brázdil, R. Roman & I. Rektor (2010). The Level of Frontal-Temporal Beta-2 Band EEG Synchronization Distinguishes Anterior Cingulate Cortex From Other Frontal Regions. Consciousness and Cognition 19 (4):879-886.
    Recent findings indicate that complex cognitive functions are organized at a global level in the brain and rely on large-scale information processing requiring functional integration of (...)multiple disparate neural assemblies. The critical question of the integration of distributed brain activities is whether the essential integrative role can be attributed to a specific structure in the brain or whether this ability is inherent to the cognitive network as a whole. The results of the present study show that mean values of the running correlation function in frontal-temporal EEG pairs with one electrode in the anterior cingulate cortex are significantly higher than the same values in other frontal-temporal pairs. These findings indicate a particular role of the ACC in large-scale communication, which could reflect its unique integrative functions in cognitive processing. (shrink)
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  24.  8
    Mark E. Pflieger (2000). Brain Function Theories, EEG Sources, and Dynamic States. Behavioral and Brain Sciences 23 (3):411-412.
    This commentary discusses three features of the general theoretical framework proposed by Nunez: (1) Functional concepts, such as computation and control, are not foundational. (2) A mismatch (...)
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  25.  7
    Mark E. Pflieger (2003). Inferring Contextual Field Interactions From Scalp EEG. Behavioral and Brain Sciences 26 (1):99-100.
    This commentary highlights methods for using scalp EEG to make inferences about contextual field interactions, which, in view of the target article, may be specially relevant to (...)
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  26.  8
    Robert J. Wilkus (1980). The EEG as Confirmatory Evidence of Brain Death: Previous and Current Approaches. [REVIEW] Bioethics Quarterly 2 (1):39-45.
    The role of EEG in confirming the clinical diagnosis of isolated brain death has undergone evolutionary changes since the original recommendations concerning its use. Accumulated evidence now (...)
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  27.  7
    Emrah Düzel (2003). Some Mechanisms of Working Memory May Not Be Evident in the Human EEG. Behavioral and Brain Sciences 26 (6):732-732.
    Ruchkin et al. use brain-activity data from healthy subjects to assess the physiological validity of a cognitive working memory model and to propose modifications. The conclusions (...)drawn from this data are interesting and plausible, but they have limitations. Much of what is known about the neural mechanisms of working memory comes from single neuron recordings in animals, and it is currently not fully understood how these translate to scalp recordings of EEG. (shrink)
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  28.  8
    L. Senhadji, G. Carrault, H. Gauvrit, E. Wodey, P. Pladys & F. Carré (2000). Pediatric Anesthesia Monitoring with the Help of EEG and ECG. Acta Biotheoretica 48 (3-4):289-302.
    This paper presents research regarding the monitoring of the brain and the adequacy of anesthesia during surgery. Particular variables are derived from EEG and ECG signals and (...)
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  29.  5
    Richard E. Greenblatt (2000). Large-Scale Neocortical Dynamics: Some EEG Data Analysis Implications. Behavioral and Brain Sciences 23 (3):401-402.
    The spatial time-frequency distribution matrix and associated Rényi entropy is proposed as the basis for a method that may be useful for estimating the significance of (...)nonlocal neocortical interactions in the analysis of scalp EEG data. Implications of nonlocal interactions for source estimation are also considered. (shrink)
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  30.  5
    Michael Murias & James M. Swanson (2000). Large-Scale Neocortical Dynamic Function and EEG: Use of Theory and Methods in Clinical Research on Children with Attention Deficit Hyperactivity Disorder. Behavioral and Brain Sciences 23 (3):411-411.
    We used Nunez's physiologically based dynamic theory of EEG to make predictions about a clinical population of children with Attention Deficit Hyperactivity Disorder (ADHD) known to (...)have neuronanatomical abnormalities. Analysis of high-density EEG data (long-range coherence) showed expected age-related differences and surprising regional specificity that is consistent with some of the literature in this clinical area. (shrink)
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  31.  2
    V. Kolev & J. Yordanova (2000). The Position of Event-Related EEG Activity in the Local/Global Theory. Behavioral and Brain Sciences 23 (3):407-407.
    The theory of local/global neocortical EEG dynamics responds to newly emerging conceptualizations in neuroscience. An extended application of the model to event-related EEG activity composed of (...) distinctive global and local functional epochs with presumably different timing is proposed. (shrink)
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  32. Laura K. Cirelli, Dan Bosnyak, Fiona C. Manning, Christina Spinelli, Cã©Line Marie, Takako Fujioka, Ayda Ghahremani & Laurel J. Trainor (2014). Beat-Induced Fluctuations in Auditory Cortical Beta-Band Activity: Using EEG to Measure Age-Related Changes. Frontiers in Psychology 5.
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  33. M. Jawad Khan, Melissa Jiyoun Hong & Keum-Shik Hong (2014). Decoding of Four Movement Directions Using Hybrid NIRS-EEG Brain-Computer Interface. Frontiers in Human Neuroscience 8.
  34. Martin Seeber, Reinhold Scherer, Johanna Wagner, Teodoro Solis-Escalante & Gernot R. Mã¼Ller-Putz (2014). EEG Beta Suppression and Low Gamma Modulation Are Different Elements of Human Upright Walking. Frontiers in Human Neuroscience 8.
  35. Robert Coben, Iman Mohammad-Rezazadeh & Rex L. Cannon (2014). Using Quantitative and Analytic EEG Methods in the Understanding of Connectivity in Autism Spectrum Disorders: a Theory of Mixed Over- and Under-Connectivity. Frontiers in Human Neuroscience 8.
  36. Benedikt V. Ehinger, Petra Fischer, Anna L. Gert, Lilli Kaufhold, Felix Weber, Gordon Pipa & Peter König (2014). Kinesthetic and Vestibular Information Modulate Alpha Activity During Spatial Navigation: a Mobile EEG Study. Frontiers in Human Neuroscience 8.
  37. Andrew D. Engell & Gregory McCarthy (2014). Face, Eye, and Body Selective Responses in Fusiform Gyrus and Adjacent Cortex: an Intracranial EEG Study. Frontiers in Human Neuroscience 8.
  38. Manuela Gander & Anna Buchheim (2015). Attachment Classification, Psychophysiology and Frontal EEG Asymmetry Across the Lifespan: a Review. Frontiers in Human Neuroscience 9.
  39. David Jenson, Andrew L. Bowers, Ashley W. Harkrider, David Thornton, Megan Cuellar & Tim Saltuklaroglu (2014). Temporal Dynamics of Sensorimotor Integration in Speech Perception and Production: Independent Component Analysis of EEG Data. Frontiers in Psychology 5.
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  40. Anna L. Mangia, Marco Pirini & Angelo Cappello (2014). Transcranial Direct Current Stimulation and Power Spectral Parameters: a tDCS/EEG Co-Registration Study. Frontiers in Human Neuroscience 8.
  41. Carlos Trenado (2015). Commentary: EEG Beta Suppression and Low Gamma Modulation Are Different Elements of Human Upright Walking. Frontiers in Human Neuroscience 9.
  42. Ahu Gokce, Thomas Geyer, Kathrin Finke, Hermann J. Mã¼Ller & Thomas Tã¶Llner (2014). What Pops Out in Positional Priming of Pop-Out: Insights From Event-Related EEG Lateralizations. Frontiers in Psychology 5.
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  43. Hartmut Heinrich, Katrin Busch, Petra Studer, Karlheinz Erbe, Gunther H. Moll & Oliver Kratz (2014). EEG Spectral Analysis of Attention in ADHD: Implications for Neurofeedback Training? Frontiers in Human Neuroscience 8.
  44. Petar Horki, Gã¼Nther Bauernfeind, Daniela S. Klobassa, Christoph Pokorny, Gerald Pichler, Walter Schippinger & Gernot R. Mã¼Ller-Putz (2014). Detection of Mental Imagery and Attempted Movements in Patients with Disorders of Consciousness Using EEG. Frontiers in Human Neuroscience 8.
  45. Lutz Jäncke, Jürg Kühnis, Lars Rogenmoser & Stefan Elmer (2015). Time Course of EEG Oscillations During Repeated Listening of a Well-Known Aria. Frontiers in Human Neuroscience 9.
  46. Periklis Y. Ktonas & Errikos-Chaim Ventouras (2014). Automated Detection of Sleep Spindles in the Scalp EEG and Estimation of Their Intracranial Current Sources: Comments on Techniques and on Related Experimental and Clinical Studies. Frontiers in Human Neuroscience 8.
  47. Vladimir Matic, Perumpillichira Joseph Cherian, Ninah Koolen, Amir H. Ansari, Gunnar Naulaers, Paul Govaert, Sabine Van Huffel, Maarten De Vos & Sampsa Vanhatalo (2015). Objective Differentiation of Neonatal EEG Background Grades Using Detrended Fluctuation Analysis. Frontiers in Human Neuroscience 9.
  48. Andrew Myrden & Tom Chau (2015). Effects of User Mental State on EEG-BCI Performance. Frontiers in Human Neuroscience 9.
  49. Till Nierhaus, Daniel Pach, Wenjing Huang, Xiangyu Long, Vitaly Napadow, Stephanie Roll, Fanrong Liang, Burkhard Pleger, Arno Villringer & Claudia M. Witt (2015). Differential Cerebral Response to Somatosensory Stimulation of an Acupuncture Point Vs. Two Non-Acupuncture Points Measured with EEG and fMRI. Frontiers in Human Neuroscience 9.
  50. Daniel Schneider, Sven Hoffmann & Edmund Wascher (2014). Sustained Posterior Contralateral Activity Indicates Re-Entrant Target Processing in Visual Change Detection: an EEG Study. Frontiers in Human Neuroscience 8.
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