The DOI matches its Crossref record; 54 of the 56 references it cites were found in a registry or an index.
Aperiodic phase re‐setting in scalp EEG of beta–gamma oscillations by state transitions at alpha–theta rates
Open accessAbstract
We evaluated the rapid changes in regional scalp EEG synchronization in normal subjects with spatial and temporal resolution exceeding prior art 10‐fold with a high spatial density array and the Hilbert transform. A curvilinear array of 64 electrodes 3 mm apart extending 18.9 cm across the scalp was used to record EEG at 200/sec. Analytic amplitude (AA) and phase (AP) were calculated at each time step for the 64 traces in the analog pass band of 0.5–120 Hz. AP differences approximated the AP derivative (instantaneous frequency). The AP from unfiltered EEG revealed no reproducible patterns. Filtering was necessary in the β and gamma ranges according to a technique that optimized the correlation of the AP differences with the activity band pass filtered in the alpha range. The sizes of temporal AP differences were usually within ±0.5 radian from the average step corresponding to the center frequency of the pass band. Large AP differences were often synchronized over distances of 6 to 19 cm. An optimal pass band to detect and measure these recurring jumps in AP in the β and γ ranges was found by maximizing the α peak in the cospectrum of the correlation between unfiltered EEG and the band pass AP differences. Synchronized AP jumps recurred in clusters (CAP) at α and theta rates in resting subjects and with EMG. Cortex functions by serial changes in state. The Hilbert transform of EEG from high‐density arrays can visualize these state transitions with high temporal and spatial resolution and should be useful in relating EEG to cognition. Hum. Brain Mapping 19:248–272, 2003. © 2003 Wiley‐Liss, Inc.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Using a Hilbert analysis of the cortical EEG, Freeman et al. (2003) report that electrophysiological equilibria reflect a hemisphere-wide spatial phase change, occurring at theta rates.”
From Global Workspace Dynamics: Cortical “Binding and Propagation” Enables Conscious Contents (2013) · cites this paper as Freeman et al. (2003)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
PIKOVSKY, A. S., Rosenblum, M., & Kurths, J. (2001) · Cambridge University Press eBooks · doi:10.1017/cbo9780511755743 · Cited by 8,250
Self-organized criticality: An explanation of the 1/ f noise
Bak, P., Tang, C., & Wiesenfeld, K. A. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.381 · Cited by 7,644
The brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761
Visual Feature Integration and the Temporal Correlation Hypothesis
Singer, W., & Gray, C. M. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.003011 · Cited by 3,217
Perception's shadow: long-distance synchronization of human brain activity
Rodriguez, E., George, N., Lachaux, J.-P., Martinerie, J., Renault, B., & Varela, F. J. (1999) · Nature · doi:10.1038/17120 · Cited by 1,915
Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz Visual Responses in Human
Tallon‐Baudry, C., Bertrand, O., Delpuech, C., & Pernier, J. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-13-04240.1996 · Cited by 1,526
Nunez, P. L., Srinivasan, R., Westdorp, A. F., Wijesinghe, R. S., Tucker, D. M., Silberstein, R. B., & Cadusch, P. J. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(97)00066-7 · Cited by 1,230
Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations
Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., & Ilmoniemi, R. J. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-04-01370.2001 · Cited by 1,149
Induced γ-Band Activity during the Delay of a Visual Short-Term Memory Task in Humans
Tallon‐Baudry, C., Bertrand, O., Peronnet, F., & Pernier, J. (1998) · Journal of Neuroscience · doi:10.1523/jneurosci.18-11-04244.1998 · Cited by 900
Research works printed but not linked: 5
Cited by
Sorted by citations
Mining event-related brain dynamics
Makeig, S., Debener, S., Onton, J., & Arnaud Delorme, A. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.03.008 · Cited by 1,499
Human hippocampal theta oscillations and the formation of episodic memories
Lega, B. C., Jacobs, J., & Kahana, M. (2012) · Hippocampus · doi:10.1002/hipo.20937 · Cited by 485
“When plotted in logarithmic coordinates, these power spectra appeared as a straight line with positive deviations (i.e., peaks) at particular frequencies, consistent with previous findings that iEEG power has a 1/f a distribution (Freeman et al., 2003; Miller et al., 2007).”
ResultDehaene, S., & Changeux, J.-P. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030141 · Cited by 329
“Likewise, Freeman has observed, in both awake cats and humans, sharp transitions in the phase of local potentials that occur simultaneously at distant sites across the scalp and delimit metastable periods of 100–300 ms duration [95].”
BackgroundNumerical representation in the parietal lobes: Abstract or not abstract?
Kadosh, R. C., & Walsh, V. (2009) · Behavioral and Brain Sciences · doi:10.1017/s0140525x09990938 · Cited by 339
Global Workspace Dynamics: Cortical “Binding and Propagation” Enables Conscious Contents
Baars, B. J., Franklin, S., & Ramsoy, T. Z. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00200 · Cited by 294
“Llinas and Pare (1991) have emphasized the integration of specific and non-specific thalamocortical signaling, and Freeman et al. (2003) have developed a conception of hemisphere-wide signaling and phase changes.”
BackgroundMethodInfluentialOperational principles of neurocognitive networks
Bressler, S. L., & Tognoli, E. (2006) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2005.12.008 · Cited by 268
A theoretical and experimental study of neuromorphic atomic switch networks for reservoir computing
Sillin, H. O., Aguilera, R., Shieh, H.-H., Avizienis, A. V., Aono, M., Stieg, A. Z., & Gimzewski, J. K. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/384004 · Cited by 256
Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude
Freeman, W. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.02.029 · Cited by 248
“The standard padding was adopted (Freeman et al., 2003).”
Self-citationScale‐free dynamics of global functional connectivity in the human brain
Stam, C. J., & de Bruin, E. A. (2004) · Human Brain Mapping · doi:10.1002/hbm.20016 · Cited by 242
“Finally, several authors have suggested that the complex dynamics of synchronization and desynchronization processes in the brain might be due to so called “self-organized criticality” [Bak et al., 1987; Freeman et al., 2003; Le van Quyen, 2003].”
BackgroundRest-stimulus interaction in the brain: a review
Northoff, G., Qin, P., & Nakao, T. (2010) · Trends in Neurosciences · doi:10.1016/j.tins.2010.02.006 · Cited by 248
Development of cortical connections as measured by EEG coherence and phase delays
Thatcher, R. W., North, D. M., & Biver, C. J. (2007) · Human Brain Mapping · doi:10.1002/hbm.20474 · Cited by 203
Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations
Gollo, L. L., Zalesky, A., Hutchison, R. M., van den Heuvel, M. P., & Breakspear, M. J. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0165 · Cited by 189
“Freeman WJ, Burke BC, Holmes MD. 2003 Aperiodic phase re-setting in scalp EEG of beta – gamma oscillations by state transitions at alpha – theta rates.”
BackgroundNonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics
Freeman, W. J., III, & Vitiello, G. (2006) · eScholarship (California Digital Library) · Cited by 184
Self-citationOrigin, structure, and role of background EEG activity. Part 2. Analytic phase
Freeman, W. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.02.028 · Cited by 180
“Likewise the EEG gives records that appear chaotic, with temporal and spatial spectra that are 1/f α, where the exponent α has been estimated 1 < α < 3 (Pereda et al., 1998; Freeman et al., 2003).”
Self-citationOrigin, structure, and role of background EEG activity. Part 4: Neural frame simulation
Freeman, W. J. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2005.10.025 · Cited by 177
“During EEG recording the sulci were avoided by making the 8x8 square array small enough (1x1 cm) to fit onto a single gyrus [Freeman et al., 2000; Freeman et al. 2005].”
MethodSelf-citationKozma, R., Puljic, M., Balister, P., Bollobás, B., & Freeman, W. (2005) · Biological Cybernetics · doi:10.1007/s00422-005-0565-z · Cited by 167
“Synchronization of neural electical activity while completing cognitive tasks is studied in various animals, e.g., in cats, rabbits, gerbils, macaque monkeys (Barrie et al, 1996; Ohl, et al, 2001, 2003, Freeman et al, 2003a; Bressler, 2003).”
BackgroundSelf-citationCoherence, Phase Differences, Phase Shift, and Phase Lock in EEG/ERP Analyses
Thatcher, R. W. (2012) · Developmental Neuropsychology · doi:10.1080/87565641.2011.619241 · Cited by 159
“A method to distinguish zero phase lag due to volume conduction versus zero phase lag due to network connectivity is by phase reset measures that precisely define the onset and offset of phase shift in single pairs of electrodes which is by definition independent of volume conduction ( Freeman & Rogers, 2002 ; Freeman…”
MethodValidity and Reliability of Quantitative Electroencephalography
Thatcher, R. W. (2010) · Journal of Neurotherapy · doi:10.1080/10874201003773500 · Cited by 173
Ahmad, J., Ellis, C. L., Leech, R., Voytek, B., Garcés, P., Jones, E. J. H., Buitelaar, J. K., Loth, E., dos Santos, F. P., Amil, A. F., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02218-z · Cited by 181
“Changes in brain state—for example, from synchronous to asynchronous, or substantial changes between oscillatory phases, may result from GABAmediated thalamocortical circuit inhibitory bursts [148, 149].”
BackgroundTHE WAVE PACKET: AN ACTION POTENTIAL FOR THE 21st CENTURY
Freeman, W. J. (2003) · Journal of Integrative Neuroscience · doi:10.1142/s0219635203000214 · Cited by 132
“A new cospectral peak appeared in the theta range at 4.6 Hz, reflecting the change in recurrence rate of the spatially aligned jumps in |AP| differences.”
BackgroundInfluentialSelf-citationJacobs, J., & Kahana, M. J. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2187-09.2009 · Cited by 127
“Nonetheless, both findings seem closely related because they each describe stimulus-specific patterns of gamma-amplitude modulation that occur during periods of elevated overall gamma activity and that are linked to theta oscillations (Freeman et al., 2003).”
BackgroundOrigin, structure, and role of background EEG activity. Part 3. Neural frame classification
Freeman, W. J. (2005) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.12.023 · Cited by 124
“The power spectral densities (PSD) of the EEGs had power-law distributions (1/f) in log–log displays; the maximal energy of carrier waves was in spectral peaks in the beta (12–30 Hz) or gamma (30–80 Hz) ranges, while the repetition of frames gave peaks in the theta and alpha ranges (Freeman et al., 2003).”
InfluentialSelf-citationNikulin, V. V., & Brismar, T. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.03.019 · Cited by 117
“Both the analytic signal approach (Freeman et al., 2003; Le Van Quyen et al., 2001; Rosenblum et al., 2001; Tass et al., 1998) and the wavelet transform (Lachaux et al.”
Grouiller, F., Vercueil, L., Krainik, A., Segebarth, C. M., Kahane, P., & Olivier David, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.025 · Cited by 119
“More rarely, spatial correlation in terms of spatial power spectral density (Freeman et al., 2003) and of synchronisation (Astolfi et al.”
"Dynamic" Connectivity in Neural Systems
Breakspear, M. (2004) · Neuroinformatics · doi:10.1385/ni:2:2:205 · Cited by 113
“Several authors have attempted to utilize the properties of the Hilbert transform in order to demonstrate it in neurophysiological data (Breakspear, 2002; Freeman et al., 2002, 2003; Breakspear et al., 2003d).”
MethodPresacco, A., Forrester, L. W., & Contreras-Vidal, J. L. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2188304 · Cited by 114
“In order to quantify the intra- and inter-limb phase coordination, a phase analysis using the Hilbert transform [32] was applied to the left and right limbs.”
MethodFine spatiotemporal structure of phase in human intracranial EEG
Freeman, W. J., Holmes, M., West, G. A., & Vanhatalo, S. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.03.012 · Cited by 109
“The differences in amplitude and phase among channels demonstrated that the shared waveform was intrinsic to the dynamics and could not be ascribed solely to activity at the reference electrode or to volume conduction [Freeman et al., 2003].”
BackgroundSelf-citationMetastability, instability, and state transition in neocortex
Freeman, W., & Holmes, M. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.014 · Cited by 102
Self-citationEvidence from human scalp electroencephalograms of global chaotic itinerancy
Freeman, W. (2003) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.1596553 · Cited by 96
Self-citationA field-theoretic approach to understanding scale-free neocortical dynamics
Freeman, W. (2005) · Biological Cybernetics · doi:10.1007/s00422-005-0563-1 · Cited by 94
“…fall in log power with increasing log frequency imposed by the impedance of the skull and scalp, and partly due to a spatial spectral peak in the middle spatial frequency range that corresponded to the spatial frequency of the gyri and sulci of the human cerebral hemisphere [Freeman et al., 2003].”
BackgroundSelf-citationCombining fMRI with EEG and MEG in order to relate patterns of brain activity to cognition
FREEMAN, W. (2009) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2008.12.019 · Cited by 91
“The most valuable tool is the spatial power spectral density (PSDX), which has been calculated for the scalp EEG (Freeman et al., 2003), intracranial human electrocorticogram (ECoG, Freeman et al., 2000) and fMRI (Worsely, 2005) but surprisingly not for MEG.”
Self-citationFreeman, W. J. (2003) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127403008144 · Cited by 94
Self-citationDissipation and spontaneous symmetry breaking in brain dynamics
Freeman, W. J., & Vitiello, G. (2008) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8113/41/30/304042 · Cited by 91
Self-citationIndirect biological measures of consciousness from field studies of brains as dynamical systems
Freeman, W. J. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.09.004 · Cited by 88
Self-citationCerebral Energy Metabolism and the Brain’s Functional Network Architecture: An Integrative Review
Lord, L.-D., Expert, P., Huckins, J. F., & Turkheimer, F. (2013) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2013.94 · Cited by 86
Hebert, R., Lehmann, D., Tan, G., Travis, F., & Arenander, A. (2005) · Signal Processing · doi:10.1016/j.sigpro.2005.07.009 · Cited by 82
Chaotic resonance in Hodgkin–Huxley neuron
Baysal, V., Saraç, Z., & Yılmaz, E. (2019) · Nonlinear Dynamics · doi:10.1007/s11071-019-05047-w · Cited by 78
The fractal brain: scale-invariance in structure and dynamics
Grosu, G. F., Hopp, A. V., Moca, V. V., Bârzan, H., Ciuparu, A., Ercsey-Ravasz, M., Winkel, M., Linde, H., & Muresan, R. C. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac363 · Cited by 87
“In several studies, Freeman and colleagues demonstrated that human EEG obeys a 1/f property (Freeman et al. 2000, 2003).”
BackgroundFoundational perspectives on causality in large-scale brain networks
Mannino, M., & Bressler, S. L. (2015) · Physics of Life Reviews · doi:10.1016/j.plrev.2015.09.002 · Cited by 80
Freeman, W. J., & ROGERS, L. J. (2003) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127403008296 · Cited by 79
Self-citationDimitriadis, S. I., Laskaris, N., Simos, P. G., Micheloyannis, S., Fletcher, J. M., Rezaie, R., & Papanicolaou, A. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.036 · Cited by 76
“Such properties were attributed by some researchers to a universal phenomenon displayed by global synchronization dynamics, known as ‘self-organized criticality’ (SOC; Freeman et al., 2003; Kitzbichler et al., 2009; Le van Quyen, 2003).”
Intelligence and EEG phase reset: A two compartmental model of phase shift and lock
Thatcher, R. W., North, D. M., & Biver, C. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.06.009 · Cited by 81
“Phase shift duration is a period of high uncertainty and instability, whereas, phase locking is a period of stability and low uncertainty (Freeman et al., 2003; Thatcher et al., in press).”
Self‐organized criticality and the development of EEG phase reset
Thatcher, R. W., North, D. M., & Biver, C. J. (2008) · Human Brain Mapping · doi:10.1002/hbm.20524 · Cited by 74
EEG synchrony during a perceptual-cognitive task: Widespread phase synchrony at all frequencies
Pockett, S., Bold, G. E. J., & Freeman, W. J. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.12.044 · Cited by 75
“…significant background synchrony has also been reported by Freeman and colleagues, both in ECoG measurements from a grid of electrodes implanted on the surface of rabbit sensory cortex (Freeman and Rogers, 2002; Freeman, 2004a,b) and in EEG measurements from human scalp (Freeman et al., 2003a,b).”
BackgroundSelf-citationA neurobiological theory of meaning in perception
Freeman, W. J. (2004) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2003.1223896 · Cited by 75
Self-citationA cinematographic hypothesis of cortical dynamics in perception
Freeman, W. (2006) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2005.12.009 · Cited by 74
“Pass bands for individuals varied from the normative values after classifier-directed optimization of the filter settings with tuning curves [Freeman et al., 2003a].”
BackgroundMethodInfluentialSelf-citationHigh Resolution Electroencephalography in Freely Moving Mice
Choi, J. H., Koch, K. P., Poppendieck, W., Lee, M., & Shin, H. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00188.2010 · Cited by 70
LORETA EEG phase reset of the default mode network
Thatcher, R. W., North, D. M., & Biver, C. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00529 · Cited by 75
“…are consistent with physiological models of EEG PR and have added to earlier studies by Varela (1995), Breakspear (2002, 2004), Freeman (2003), and Freeman et al. (2003) by measuring discontinuities of electrical potentials and current sources of the two main physiological processes that underlie…”
ResultGraph theoretical analysis of EEG functional connectivity during music perception
Wu, J., Zhang, J., Liu, C., Liu, D., Ding, X., & Zhou, C. (2012) · Brain Research · doi:10.1016/j.brainres.2012.09.014 · Cited by 71
Dynamics of spontaneous transitions between global brain states
Ito, J., Nikolaev, A. R., & van Leeuwen, C. (2007) · Human Brain Mapping · doi:10.1002/hbm.20316 · Cited by 68
“The concept of chaotic itinerancy offers a theoretical basis for the transient character of brain dynamics, which has been observed in many studies besides the present one [Stam et al., 2003; Freeman et al., 2003; Breakspear et al., 2004; Schack, 2004] and raised as a property essential for effective and integrated br…”
BackgroundInfluentialAutism and EEG Phase Reset: Deficient GABA Mediated Inhibition in Thalamo-Cortical Circuits
Thatcher, R. W., North, D. M., Neubrander, J. A., Biver, C. J., Cutler, S., & DeFina, P. A. (2009) · Developmental Neuropsychology · doi:10.1080/87565640903265178 · Cited by 64
“Studies by Freeman and Rogers (2002) and Freeman et al. (2003) show that the spontaneous EEG is made up of a mixture of synchronous processes that involve rapid phase shifts (approx.”
BackgroundA method to study global spatial patterns related to sensory perception in scalp EEG
Ruiz, Y., Pockett, S., Freeman, W. J., Gonzalez, E. A., & Li, G. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.05.021 · Cited by 61
“…of the binomial probability of the classification rate of the features es (C3, C2 and C1) and 6 post-stimulus test frames (T1, T2, T3, T4, T5 and T6) for tom: mean classification rate for same frames, vertical bars represent the standard theta range (Freeman et al., 2003a; Freeman, 2006, 2009).”
BackgroundMethodInfluentialSelf-citationThe principle of coherence in multi-level brain information processing
Plankar, M., Brežan, S., & Jerman, I. (2012) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2012.08.006 · Cited by 61
“37 implying a global state transition of cortical activity occuring with a phase velocity of over 40 m/s (Freeman et al., 2003).”
BackgroundNeurodynamics of cognition and consciousness
Kozma, R., & Freeman, W. J. (2009) · Workshop on Performance Metrics for Intelligent Systems (PerMIS) · doi:10.1145/1865909.1865939 · Cited by 60
Self-citationFreeman, W. J. (2009) · Cognitive Neurodynamics · doi:10.1007/s11571-009-9075-3 · Cited by 59
Self-citationDimitriadis, S. I., Laskaris, N., & Tzelepi, A. (2013) · Brain Topography · doi:10.1007/s10548-013-0276-z · Cited by 57
“A similar transitivity behavior of brain dynamics has been observed in many previous studies (Breakspear et al. 2004; Freeman et al. 2003; Schack 2004; Stam et al. 2003) and appears as an essential property of the dynamics of the intact brain (Friston 2000a, b, c; Freeman 2004a, b, 2005, 2006; Kaplan et al.”
NDN, VOLUME TRANSMISSION, AND SELF-ORGANIZATION IN BRAIN DYNAMICS
Freeman, W. J. (2005) · Journal of Integrative Neuroscience · doi:10.1142/s0219635205000963 · Cited by 55
“proximity by synaptic connections enables rapid changes in states of neuron populations over large areas of cortex [21, 67], the diameters of which exceed the mean length of”
BackgroundSelf-citationThe KIV model of intentional dynamics and decision making
Kozma, R., & Freeman, W. J. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.03.019 · Cited by 54
“The present work is based on recent progress on phase transitions in brains using advanced, highresolution spatio-temporal EEG measurements (Freeman et al., 2003; Freeman, 2007).”
Self-citationFreeman, W. J., & Vitiello, G. (2010) · International Journal of Modern Physics B · doi:10.1142/s0217979210056025 · Cited by 53
Self-citationPierro, M. L., Sassaroli, A., Bergethon, P. R., Ehrenberg, B. L., & Fantini, S. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.015 · Cited by 53
“Examples are EEG recordings aimed at evaluating synchronization between cortical states on the scalp (Freeman et al., 2003), andmagnetoencephalography (MEG) studies on patients affected by Parkinson's disease (Tass et al.”
Impacts of autapse on chaotic resonance in single neurons and small-world neuronal networks
Baysal, V., Erkan, E., & Yılmaz, E. (2021) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2020.0237 · Cited by 53
A Foundational Architecture for Artificial General Intelligence
Franklin, S. (2007) · Cited by 52
Thatcher, R. W. (2013) · Journal of Neurotherapy · doi:10.1080/10874208.2013.759032 · Cited by 51
“Phase locking is a telltale sign of synchronization, and this is why Freeman and colleagues (Freeman et al., 2003; Freeman & Rogers, 2002) and Breakspear and Williams (2004) and others (Lachaux et al., 2000; Lachaux, Rodriguez, Martinerie, & Varela, 1999; LeVan Quyen et al., 2001; Varela, Lachaux,…”
BackgroundDynamics of human neocortex that optimizes its stability and flexibility
Freeman, W., Holmes, M., West, G. A., & Vanhatalo, S. (2006) · International Journal of Intelligent Systems · doi:10.1002/int.20167 · Cited by 50
“…animals we proposed that the behavioral transition from waking rest to engagement with the environment is based in arousal that supports the likelihood of repetitive phase transitions recurring at rates in the theta range [Skarda and Freeman, 1987; Freeman, Burke and Holmes, 2003; Freeman, 2005].”
BackgroundInfluentialSelf-citationComputational Aspects of Cognition and Consciousness in Intelligent Devices
Kozma, R., Aghazarian, H., Huntsberger, T. L., Tunstel, E., & Freeman, W. (2007) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2007.385369 · Cited by 50
“Hilbert transform has the potential to avoid these difficulties , and it has been applied successfully to EEG analysis in the past few years (Lachaux et al., 1999; Le Van Quyen et al., 2001; Freeman and Rogers, 2002, 2003; Quiroga et al., 2002).”
BackgroundMethodInfluentialSelf-citationNeural Oscillations and the Initiation of Voluntary Movement
Armstrong, S. R., Sale, M. V., & Cunnington, R. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.02509 · Cited by 75
“This phase and amplitude coupling has been observed in different cortical regions (Freeman et al., 2003; Palva et al., 2005; Canolty et al., 2006; Isler et al., 2008; Sauseng et al., 2008), across the conventional EEG spectra (0.5 ≥ f ≤ 100 Hz); Lakatos et al., 2005), and extending into…”
BackgroundInfluentialNeuropercolation: A Random Cellular Automata Approach to Spatio-temporal Neurodynamics
Kozma, R., Puljic, M., Balister, P., Bollobás, B., & Freeman, W. J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30479-1_45 · Cited by 49
“Synchronization of neural electical activity while completing cognitive tasks is studied in various animals, e.g., in cats, rabbits, gerbils, macaque monkeys (Barrie et al, 1996; Ohl, et al, 2001, 2003, Freeman et al, 2003a; Bressler, 2003).”
BackgroundSelf-citationFreeman, W., & Kozma, R. (2010) · Scholarpedia · doi:10.4249/scholarpedia.8040 · Cited by 49
Self-citationNoguès, X., Corsini, M.-M., Marighetto, A., & Abrous, D. N. (2011) · Behavioural Brain Research · doi:10.1016/j.bbr.2011.08.009 · Cited by 47
Roß, B., Jamali, S., Miyazaki, T., & Fujioka, T. (2012) · Experimental Neurology · doi:10.1016/j.expneurol.2012.08.019 · Cited by 47
Operational Architectonics Methodology for EEG Analysis: Theory and Results
Fingelkurts, A. A., & Fingelkurts, A. A. (2013) · Neuromethods · doi:10.1007/7657_2013_60 · Cited by 43
“4 For other major methodologies of EEG analysis which explicitly utilize EEG nonstationarity see the work of Lehman and coworkers (60, 61, 67, 80, 138, 147, 149, 150, 165), Freeman and coworkers (62, 64, 65, 85, 87, 89, 90, 112, 123, 158, 166), and Breakspear and Stam with coworkers (75, 134, 145, 167-169).”
BackgroundMethodFreeman, W. J. (2003) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127403008156 · Cited by 39
Self-citationScale-Free Cortical Planar Networks
Freeman, W. J., Kozma, R., Bollobá, B., & Riordan, O. M. (2008) · Bolyai Society mathematical studies · doi:10.1007/978-3-540-69395-6_7 · Cited by 36
“Definitive exploration of hubs may be optimized by recording EEG/MEG from a dense array of channels overlying a large percentage of the surface each hemisphere, and with sampling at 1 cm intervals corresponding to the width of gyri so as to capture adequately the texture of AM patterns of spatially coherent activity […”
BackgroundMethodSelf-citationProposed Cortical “Shutter” Mechanism in Cinematographic Perception
Freeman, W. (2007) · Springer eBooks · doi:10.1007/978-3-540-73267-9_2 · Cited by 37
“The result is seen in the fluctuations of the gamma band activity in the ECoG; the amplitude histogram is close to Gaussian; the envelope of the activity conforms to the Rayleigh distribution [14], p. 148, Fig. 3.13c; and the power spectral density in log-log coordinates has a slope near ‐2. The spikes in SDX (t) recu…”
MethodSelf-citationLogical chaotic resonance in the FitzHugh–Nagumo neuron
Yao, Y. (2022) · Nonlinear Dynamics · doi:10.1007/s11071-021-07155-y · Cited by 36
Diffusion spectral imaging modules correlate with EEG LORETA neuroimaging modules
Thatcher, R. W., North, D. M., & Biver, C. J. (2011) · Human Brain Mapping · doi:10.1002/hbm.21271 · Cited by 35
“…phase lag due to volume conduction vs zero phase lag due to network connectivity is by phase reset measures that precisely define the onset and offset of phase shift which is by definition independent of volume conduction [Buzsaki, 2006; Freeman et al., 2003, 2006; Thatcher et al., 2008c, 2009].”
BackgroundFreeman, W., & Burke, B. C. (2003) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127403008302 · Cited by 36
Self-citationMultifractal dynamics of resting-state functional connectivity in the prefrontal cortex
Rácz, F. S., Mukli, P., Nagy, Z. Z., & Eke, A. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aaa916 · Cited by 34
“…brain activity (Stam 2005, Chialvo 2010, Werner 2010), considering it as a plausible mechanism underlying scale-free structuring in neural dynamics (Freeman et al 2003, de Arcangelis et al 2006), in neural avalanches (Beggs and Plenz 2003) and in EEG synchronization (Stam and de Bruin 2004, Stam…”
BackgroundLi, X., Yu, D., Yang, L., Fu, Z., & Jia, Y. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-023-10021-9 · Cited by 32
“By recording and analyzing electroencephalography data from the scalp of healthy subjects, Freeman discovered that chaotic firing also exists in the human brain and suggested that this brain chaos is called stochastic chaos (Freeman 1990, 2000; Freeman et al. 2003).”
Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals
Sirpal, P., Damseh, R., Peng, K., Nguyen, D. K., & Lesage, F. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09538-3 · Cited by 31
“Scalp EEG technology is the clinical gold standard for studying the human brain (Müller-Putz, 2020) and EEG recordings can be classified into specific frequency bands: alpha, beta, delta, gamma, and theta (Cho et al., 2014; Freeman et al., 2003; Pedregosa et al., 2011; Zhao et al., 2018).”
BackgroundSameni, R., & Seraj, E. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa93a1 · Cited by 29
“…authors ’ knowledge, despite the rich literature on EEG phase, only some general guidelines have been proposed for the phase extraction procedure (Freeman et al 2003, Freeman 2007), and researchers have been basically using arbitrary and un-unified narrow-band filtering and phase extraction…”
BackgroundMethodInfluentialNeurobiological model of perception: Considerations for transference.
Pincus, D. I., Freeman, W. J., & Modell, A. H. (2007) · Psychoanalytic Psychology · doi:10.1037/0736-9735.24.4.623 · Cited by 28
Self-citationDuration of Coherence Intervals in Electrical Brain Activity in Perceptual Organization
Nikolaev, A. R., Gepshtein, S., Gong, P., & van Leeuwen, C. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp107 · Cited by 27
“These episodes emerge, hold, and dissipate on different temporal scales and in different brain structures (Friston 2000; Freeman et al. 2003; Gong et al. 2003; Stam et al. 2003; Ito et al. 2005).”
BackgroundInfluentialDissipative neurodynamics in perception forms cortical patterns that are stabilized by vortices
Freeman, W., & Vitiello, G. (2009) · Journal of Physics Conference Series · doi:10.1088/1742-6596/174/1/012011 · Cited by 25
Self-citationErgün, E., & Aydemir, Ö. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3028785 · Cited by 25
Intentional systems: Review of neurodynamics, modeling, and robotics implementation
Kozma, R. (2007) · Physics of Life Reviews · doi:10.1016/j.plrev.2007.10.002 · Cited by 26
“Phase transitions between chaotic states constitute the dynamics that explains how brains perform such remarkable feats as abstraction of the essentials of figures from complex, unknown and unpredictable backgrounds, generalization over examples of recurring objects, reliable assignment to classes that lead to appropr…”
BackgroundMethodRossberg, A. G., Bartholomé, K., & Timmer, J. (2004) · Physical Review E · doi:10.1103/physreve.69.016216 · Cited by 25
Riding the (brain) waves! Using neural oscillations to inform bilingualism research
Rossi, E., Soares, S. M. P., Prystauka, Y., Nakamura, M. S., & Rothman, J. (2022) · Bilingualism Language and Cognition · doi:10.1017/s1366728922000451 · Cited by 27
“…& Malach, 2015; Voytek, Kramer, Case, Lepage, Tempesta, Knight & Gazzaley, 2015), whereby power decreases exponentially as a function of frequency (f) (Donoghue, Haller, Peterson, Varma, Sebastian, Gao, Noto, Lara, Wallis, Knight, Shestyuk & Voytek, 2020; Freeman, Burke & Holmes, 2003; He, 2014).”
Cross-frequency coupling in mesiotemporal EEG recordings of epileptic patients
Villa, A. E. P., & Tetko, I. V. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.11.024 · Cited by 24
“, 2007) and to cognitive studies of areal interactions (Freeman et al., 2003; Jensen and Colgin, 2007).”
Investigation of chaotic resonance in Type-I and Type-II Morris-Lecar neurons
Baysal, V., Solmaz, R., & Ma, J. (2023) · Applied Mathematics and Computation · doi:10.1016/j.amc.2023.127940 · Cited by 23
The place of ‘codes’ in nonlinear neurodynamics
Freeman, W. J. (2007) · Progress in brain research · doi:10.1016/s0079-6123(06)65028-0 · Cited by 22
“We have also discovered their traces in electrical fields at the surface of the human scalp (Freeman et al., 2003), but we cannot yet read them, because we do not yet know how to encode their patterns in terms of information and symbols adequately to correlate them with behavioral measurements that…”
BackgroundSelf-citationTopography, independent component analysis and dipole source analysis of movement related potentials
Pockett, S., Whalen, S. J., McPhail, A. V. H., & Freeman, W. J. (2007) · Cognitive Neurodynamics · doi:10.1007/s11571-007-9024-y · Cited by 20
“While most of these studies describe synchrony only between activity recorded at widely separated but discrete sites, Freeman et al. (2003a), using a high density curvilinear array of electrodes, actually find synchrony in the human brain that is continuous over a distance of 189 mm, a significant…”
BackgroundSelf-citationA guideline for linking brain wave findings to the various aspects of discrete perception
Menétrey, M. Q., Vogelsang, L., & Herzog, M. H. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15349 · Cited by 19
Inverse chaotic resonance in Hodgkin–Huxley neuronal system
Yu, D., Wu, Y., Ye, Z., Xiao, F., & Jia, Y. (2022) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-022-00629-z · Cited by 18
Neuropsychiatry and quantitative EEG in the 21st Century
Thatcher, R. W. (2011) · Neuropsychiatry · doi:10.2217/npy.11.45 · Cited by 19
Lehmann, D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.033 · Cited by 18
“Incidentally, discontinuous behavior of brain activity during no-task conditions has also been observed with very different analysis approaches (Breakspear et al., 2004; Freeman et al., 2003; Kitzbichler et al. 2009).”
BackgroundReflections on a giant of brain science
Kozma, R. (2016) · Cognitive Neurodynamics · doi:10.1007/s11571-016-9403-3 · Cited by 18
“According to Freeman, the cortical code consists of repetitive spatial frames of metastable amplitude modulation (AM) patterns (Freeman 2000a, b, 2003, 2004a, b, 2005, 2006b; Freeman et al. 2000, 2003a, b; Freeman and Rogers 2002).”
BackgroundChaotic Signal Induced Delay Decay in Hodgkin-Huxley Neuron
Baysal, V., & Yılmaz, E. (2021) · Applied Mathematics and Computation · doi:10.1016/j.amc.2021.126540 · Cited by 17
Evolving neurodynamic controllers for autonomous robots
Harter, D. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1555819 · Cited by 17
Greenberg, A., & Dickson, C. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.034 · Cited by 18
“The Hilbert transform can be used to extract “instantaneous”measures of phase and amplitude of a signal and is considered to be a useful method for tracking changes in phase with high temporal resolution (Freeman et al., 2003; Le Van Quyen and Bragin, 2007).”
Metadata
- Authors
- Freeman, W. J., Burke, B. C., & Holmes, M. D.
- Affiliations
- University of California, Berkeley (US)
- Harborview Medical Center (US)
- Corresponding author
- Freeman, Walter J.
- ORCID
- Holmes, Mark D.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2003-06-26
- Volume
- 19
- Issue
- 4
- Pages
- 248-272
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6871824
- OpenAlex
- W2161350892
- Cited by
- 214
- References
- 56
- Citations by year
- 2026: 7 · 2025: 4 · 2024: 4 · 2023: 6 · 2022: 8 · 2021: 6 · 2020: 5 · 2019: 3 · 2018: 4 · 2017: 6 · 2016: 6 · 2015: 10 · 2014: 10 · 2013: 15 · 2012: 11
- FWCI
- 8.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation (0130352)
- National Aeronautics and Space Administration (NCC21244)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- beta-gamma oscillations, Hilbert transform, instantaneous frequency, state transitions, electromyography
- MeSH terms
- Alpha Rhythm, Cerebral Cortex, Cognition, Cortical Synchronization, Electroencephalography, Electromyography, Female, Humans, Male, Muscles, Scalp, Theta Rhythm
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review