Estimating a neutral reference for electroencephalographic recordings: the importance of using a high-density montage and a realistic head model

Abstract Objective. In electroencephalography (EEG) measurements, the signal of each recording electrode is contrasted with a reference electrode or a combination of electrodes. The estimation of a neutral reference is a long-standing issue in EEG data analysis, which has motivated the proposal of different re-referencing methods, among which linked-mastoid re-referencing (LMR), average re-referencing (AR) and reference electrode standardization technique (REST). In this study we quantitatively assessed the extent to which the use of a high-density montage and a realistic head model can impact on the optimal estimation of a neutral reference for EEG recordings. Approach. Using simulated recordings generated by projecting specific source activity over the sensors, we assessed to what extent AR, REST and LMR may distort the scalp topography. We examined the impact electrode coverage has on AR and REST, and how accurate the REST reconstruction is for realistic and less realistic (three-layer and single-layer spherical) head models, and with possible uncertainty in the electrode positions. We assessed LMR, AR and REST also in the presence of typical EEG artifacts that are mixed in the recordings. Finally, we applied them to real EEG data collected in a target detection experiment to corroborate our findings on simulated data. Main results. Both AR and REST have relatively low reconstruction errors compared to LMR, and that REST is less sensitive than AR and LMR to artifacts mixed in the EEG data. For both AR and REST, high electrode density yields low re-referencing reconstruction errors. A realistic head model is critical for REST, leading to a more accurate estimate of a neutral reference compared to spherical head models. With a low-density montage, REST shows a more reliable reconstruction than AR either with a realistic or a three-layer spherical head model. Conversely, with a high-density montage AR yields better results unless precise information on electrode positions is available. Significance. Our study is the first to quantitatively assess the performance of EEG re-referencing techniques in relation to the use of a high-density montage and a realistic head model. We hope our study will help researchers in the choice of the most effective re-referencing approach for their EEG studies.

[1]  R. O’Connell,et al.  A simultaneous ERP/fMRI investigation of the P300 aging effect , 2012, Neurobiology of Aging.

[2]  John J. Foxe,et al.  Crossmodal binding through neural coherence: implications for multisensory processing , 2008, Trends in Neurosciences.

[3]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[4]  C Tomberg,et al.  Inadequacy of the average reference for the topographic mapping of focal enhancements of brain potentials. , 1990, Electroencephalography and clinical neurophysiology.

[5]  Gian Luca Romani,et al.  Improving MEG source localizations: An automated method for complete artifact removal based on independent component analysis , 2008, NeuroImage.

[6]  Liang Wang,et al.  Default mode network as revealed with multiple methods for resting-state functional MRI analysis , 2008, Journal of Neuroscience Methods.

[7]  M. Czisch,et al.  Development of the brain's default mode network from wakefulness to slow wave sleep. , 2011, Cerebral cortex.

[8]  Enzo Tagliazucchi,et al.  Dynamic BOLD functional connectivity in humans and its electrophysiological correlates , 2012, Front. Hum. Neurosci..

[9]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[10]  Abraham Z. Snyder,et al.  Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure , 2013, NeuroImage.

[11]  Li Wang,et al.  The effect of reference choices on the spatio-temporal analysis of brain evoked potentials: The use of infinite reference , 2007, Comput. Biol. Medicine.

[12]  L. Miller,et al.  Optimal spacing of surface electrode arrays for brain–machine interface applications , 2010, Journal of neural engineering.

[13]  P. Brown,et al.  Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance. , 2004, Brain : a journal of neurology.

[14]  I. Fried,et al.  Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex , 2008, Nature Neuroscience.

[15]  Erich Schröger,et al.  Digital filter design for electrophysiological data – a practical approach , 2015, Journal of Neuroscience Methods.

[16]  Lei Ding,et al.  Reconstructing Large-Scale Brain Resting-State Networks from High-Resolution EEG: Spatial and Temporal Comparisons with fMRI , 2016, Brain Connect..

[17]  G. Ermentrout,et al.  Gamma rhythms and beta rhythms have different synchronization properties. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Lüder Deecke,et al.  Brain potential changes in voluntary and passive movements in humans: readiness potential and reafferent potentials , 2016, Pflügers Archiv - European Journal of Physiology.

[19]  A. Kleinschmidt,et al.  Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Dante Mantini,et al.  Optimal Re-referencing Strategy for the Detection of P300 Sources from High-density EEG , 2014 .

[21]  J. Lurito,et al.  Correlations in Low-Frequency BOLD Fluctuations Reflect Cortico-Cortical Connections , 2000, NeuroImage.

[22]  Bart Vanrumste,et al.  Review on solving the forward problem in EEG source analysis , 2007, Journal of NeuroEngineering and Rehabilitation.

[23]  Determination of observer-rated alpha activity during sleep. , 1995, Sleep.

[24]  M. Raichle,et al.  Rat brains also have a default mode network , 2012, Proceedings of the National Academy of Sciences.

[25]  F. Perrin,et al.  Mapping of scalp potentials by surface spline interpolation. , 1987, Electroencephalography and clinical neurophysiology.

[26]  Jean Gotman,et al.  The influence of electrode location errors on EEG dipole source localization with a realistic head model , 2001, Clinical Neurophysiology.

[27]  Bin Hu,et al.  Nonlinear dynamic analysis of resting EEG alpha activity for heroin addicts , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[28]  S. Kochen,et al.  Approximate average head models for EEG source imaging , 2009, Journal of Neuroscience Methods.

[29]  Rodrigo M. Braga,et al.  Echoes of the Brain within the Posterior Cingulate Cortex , 2012, The Journal of Neuroscience.

[30]  A. Schnider,et al.  Neurofeedback training of alpha-band coherence enhances motor performance , 2015, Clinical Neurophysiology.

[31]  Bin Hu,et al.  User-centered depression prevention: An EEG approach to pervasive healthcare , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[32]  Andrzej Cichocki,et al.  Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis , 2002, Biological Cybernetics.

[33]  Arne D. Ekstrom,et al.  Frequency–specific network connectivity increases underlie accurate spatiotemporal memory retrieval , 2013, Nature Neuroscience.

[34]  Rolf Verleger,et al.  Lateralized EEG components with direction information for the preparation of saccades versus finger movements , 2000, Experimental Brain Research.

[35]  Xavier Tricoche,et al.  Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling , 2006, NeuroImage.

[36]  Á. Pascual-Leone,et al.  Noninvasive human brain stimulation. , 2007, Annual review of biomedical engineering.

[37]  Dezhong Yao,et al.  A study on the reference electrode standardization technique for a realistic head model , 2004, Comput. Methods Programs Biomed..

[38]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[39]  Jing Li,et al.  Effects of holes on EEG forward solutions using a realistic geometry head model , 2007, Journal of neural engineering.

[40]  Yanda Li,et al.  Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach , 2006, Physiological measurement.

[41]  T W Picton,et al.  The P300 Wave of the Human Event‐Related Potential , 1992, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[42]  Cornelis J. Stam,et al.  Investigation of resting-state EEG functional connectivity in frontotemporal lobar degeneration , 2008, Clinical Neurophysiology.

[43]  Dante Mantini,et al.  Emerging Roles of the Brain’s Default Network , 2013, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[44]  W. Sutherling,et al.  Conductivities of Three-Layer Live Human Skull , 2004, Brain Topography.

[45]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[46]  P. Valdés-Sosa,et al.  Detection of event related potentials. , 1989, The International journal of neuroscience.

[47]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[48]  János Horváth,et al.  Attenuation of auditory ERPs to action-sound coincidences is not explained by voluntary allocation of attention. , 2013, Psychophysiology.

[49]  Maurizio Corbetta,et al.  Large-scale brain networks account for sustained and transient activity during target detection , 2009, NeuroImage.

[50]  J. Sarvas Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[51]  D.B. Geselowitz,et al.  The zero of potential , 1998, IEEE Engineering in Medicine and Biology Magazine.

[52]  Alan C. Evans,et al.  Enhancement of MR Images Using Registration for Signal Averaging , 1998, Journal of Computer Assisted Tomography.

[53]  Joseph Dien,et al.  Issues in the application of the average reference: Review, critiques, and recommendations , 1998 .

[54]  G. Fink,et al.  Dorsal and Ventral Attention Systems : Distinct Neural Circuits but Collaborative Roles , 2013 .

[55]  S. Makeig,et al.  Improved EEG source analysis using low‐resolution conductivity estimation in a four‐compartment finite element head model , 2009, Human brain mapping.

[56]  Jaakko Malmivuo,et al.  Effect of electrode density and measurement noise on the spatial resolution of cortical potential distribution , 2004, IEEE Transactions on Biomedical Engineering.

[57]  Helmut Laufs,et al.  Endogenous brain oscillations and related networks detected by surface EEG‐combined fMRI , 2008, Human brain mapping.

[58]  G. Orban,et al.  Default Mode of Brain Function in Monkeys , 2011, The Journal of Neuroscience.

[59]  E. Halgren,et al.  Cancellation of EEG and MEG signals generated by extended and distributed sources , 2009, Human brain mapping.

[60]  Richard Coppola,et al.  Group differences in MEG-ICA derived resting state networks: Application to major depressive disorder , 2015, NeuroImage.

[61]  Seppo P. Ahlfors,et al.  Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates , 2006, NeuroImage.

[62]  E. Somersalo,et al.  Visualization of Magnetoencephalographic Data Using Minimum Current Estimates , 1999, NeuroImage.

[63]  D. Tucker,et al.  EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. , 1997, Electroencephalography and clinical neurophysiology.

[64]  Barbara F. Händel,et al.  Cortical representations of confidence in a visual perceptual decision , 2014, Nature Communications.

[65]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[66]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[67]  M. Corbetta,et al.  Common Blood Flow Changes across Visual Tasks: II. Decreases in Cerebral Cortex , 1997, Journal of Cognitive Neuroscience.

[68]  C. Stam,et al.  Heritability of “small‐world” networks in the brain: A graph theoretical analysis of resting‐state EEG functional connectivity , 2008, Human brain mapping.

[69]  Christoph M. Michel,et al.  Towards the utilization of EEG as a brain imaging tool , 2012, NeuroImage.

[70]  David Poeppel,et al.  Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates , 2002, IEEE Transactions on Biomedical Engineering.

[71]  Roberto D. Pascual-Marqui,et al.  Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization , 2007, 0710.3341.

[72]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[73]  O Bertrand,et al.  A theoretical justification of the average reference in topographic evoked potential studies. , 1985, Electroencephalography and clinical neurophysiology.

[74]  H. Kornhuber,et al.  Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale , 1965, Pflüger's Archiv für die gesamte Physiologie des Menschen und der Tiere.

[75]  R H Bayford,et al.  Multi-frequency electrical impedance tomography (EIT) of the adult human head: initial findings in brain tumours, arteriovenous malformations and chronic stroke, development of an analysis method and calibration , 2006, Physiological measurement.

[76]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[77]  W. Singer,et al.  Abnormal neural oscillations and synchrony in schizophrenia , 2010, Nature Reviews Neuroscience.

[78]  D. Stegeman,et al.  Investigation of tDCS volume conduction effects in a highly realistic head model , 2014, Journal of neural engineering.

[79]  E. Maris,et al.  Orienting Attention to an Upcoming Tactile Event Involves a Spatially and Temporally Specific Modulation of Sensorimotor Alpha- and Beta-Band Oscillations , 2011, The Journal of Neuroscience.

[80]  Peng Xu,et al.  A comparative study of different references for EEG default mode network: The use of the infinity reference , 2010, Clinical Neurophysiology.

[81]  Joerg F. Hipp,et al.  Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG , 2016, NeuroImage.

[82]  Bin Hu,et al.  A study on validity of cortical alpha connectivity for schizophrenia , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[83]  Bin Hu,et al.  An EEG based nonlinearity analysis method for schizophrenia diagnosis , 2012, BioMed 2012.

[84]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[85]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

[86]  J. Matias Palva,et al.  Infra-slow fluctuations in electrophysiological recordings, blood-oxygenation-level-dependent signals, and psychophysical time series , 2012, NeuroImage.

[87]  D. Narayana Dutt,et al.  Application of LMS adaptive predictive filtering for muscle artifact (noise) cancellation from EEG signals , 1996 .

[88]  D. Yao,et al.  A method to standardize a reference of scalp EEG recordings to a point at infinity , 2001, Physiological measurement.

[89]  F. Offner,et al.  The EEG as potential mapping: the value of the average monopolar reference. , 1950, Electroencephalography and clinical neurophysiology.

[90]  Yu Ping Wang,et al.  Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference , 2014, Physiological measurement.

[91]  Christoph M. Michel,et al.  Epileptic source localization with high density EEG: how many electrodes are needed? , 2003, Clinical Neurophysiology.

[92]  Quanying Liu,et al.  A real-time EEG-based BCI system for attention recognition in ubiquitous environment , 2011, UAAII '11.

[93]  Eung Je Woo,et al.  Electrical Tissue Property Imaging at Low Frequency Using MREIT , 2014, IEEE Transactions on Biomedical Engineering.

[94]  M. Corbetta,et al.  Electrophysiological signatures of resting state networks in the human brain , 2007, Proceedings of the National Academy of Sciences.

[95]  Moritz Dannhauer,et al.  Modeling of the human skull in EEG source analysis , 2011, Human brain mapping.

[96]  Christian Seifert,et al.  Single-trial coupling of EEG and fMRI reveals the involvement of early anterior cingulate cortex activation in effortful decision making , 2008, NeuroImage.

[97]  Dante Mantini,et al.  The impact of using a high-density montage and a realistic head model for EEG signal re-referencing , 2015 .

[98]  Thom F. Oostendorp,et al.  The conductivity of the human skull: results of in vivo and in vitro measurements , 2000, IEEE Transactions on Biomedical Engineering.

[99]  Jiang Xu,et al.  Language in context: emergent features of word, sentence, and narrative comprehension , 2005, NeuroImage.

[100]  Silvia Brem,et al.  Automated detection and labeling of high-density EEG electrodes from structural MR images , 2016, Journal of neural engineering.

[101]  Seppo P. Ahlfors,et al.  Sensitivity of MEG and EEG to Source Orientation , 2010, Brain Topography.

[102]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[103]  N. Wenderoth,et al.  High-density electroencephalography permits the detection of resting state networks , 2016 .

[104]  Kensuke Sekihara,et al.  Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction , 2005, NeuroImage.

[105]  Gerald S. Russell,et al.  Geodesic photogrammetry for localizing sensor positions in dense-array EEG , 2005, Clinical Neurophysiology.

[106]  G. Pfurtscheller,et al.  On the existence of different types of central beta rhythms below 30 Hz. , 1997, Electroencephalography and clinical neurophysiology.

[107]  M. Czisch,et al.  Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study. , 2006, Brain : a journal of neurology.

[108]  L. Liu,et al.  Improve Affective Learning with EEG Approach , 2010, Comput. Informatics.

[109]  Juliane Britz,et al.  EEG microstate sequences in healthy humans at rest reveal scale-free dynamics , 2010, Proceedings of the National Academy of Sciences.

[110]  Vince D. Calhoun,et al.  Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: A concurrent EEG-fMRI study , 2010, NeuroImage.

[111]  P. Rossini,et al.  Hand somatosensory subcortical and cortical sources assessed by functional source separation: An EEG study , 2009, Human brain mapping.

[112]  D. Lehmann,et al.  Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review. , 2002, Methods and findings in experimental and clinical pharmacology.

[113]  Dezhong Yao,et al.  Why do we need to use a zero reference? Reference influences on the ERPs of audiovisual effects. , 2013, Psychophysiology.

[114]  H. Kornhuber,et al.  [CHANGES IN THE BRAIN POTENTIAL IN VOLUNTARY MOVEMENTS AND PASSIVE MOVEMENTS IN MAN: READINESS POTENTIAL AND REAFFERENT POTENTIALS]. , 1965, Pflugers Archiv fur die gesamte Physiologie des Menschen und der Tiere.

[115]  Maquet,et al.  Functional neuroimaging of normal human sleep by positron emission tomography , 2000, Journal of sleep research.

[116]  H. Kennedy,et al.  Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas , 2016, Neuron.

[117]  N. Schaul,et al.  The fundamental neural mechanisms of electroencephalography. , 1998, Electroencephalography and clinical neurophysiology.

[118]  D. A. Driscoll,et al.  Current Distribution in the Brain From Surface Electrodes , 1968, Anesthesia and analgesia.

[119]  Bin Hu,et al.  A Humanoid Robot Used as an Assistive Intervention Tool for Children with Autism Spectrum Disorder: A Preliminary Research , 2013, Brain and Health Informatics.

[120]  P. Nunez,et al.  Steady state visually evoked potential (SSVEP) topography in a graded working memory task. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[121]  M. Raichle,et al.  Cortical network functional connectivity in the descent to sleep , 2009, Proceedings of the National Academy of Sciences.

[122]  W. Hackbusch,et al.  Efficient Computation of Lead Field Bases and Influence Matrix for the FEM-based EEG and MEG Inverse Problem. Part I: Complexity Considerations , 2003 .

[123]  N Jon Shah,et al.  Altered resting‐state connectivity in Huntington's Disease , 2014, Human brain mapping.

[124]  Chris R. Johnson,et al.  Volume Currents in Forward and Inverse Magnetoencephalographic Simulations Using Realistic Head Models , 2004, Annals of Biomedical Engineering.

[125]  Ramesh Srinivasan,et al.  Estimating the spatial Nyquist of the human EEG , 1998 .

[126]  Jürgen Kayser,et al.  In search of the Rosetta Stone for scalp EEG: Converging on reference-free techniques , 2010, Clinical Neurophysiology.

[127]  Pablo Barttfeld,et al.  Functional Connectivity and Temporal Variability of Brain Connections in Adults with Attention Deficit/Hyperactivity Disorder and Bipolar Disorder , 2014, Neuropsychobiology.

[128]  P. Nunez,et al.  A theoretical basis for standing and traveling brain waves measured with human EEG with implications for an integrated consciousness , 2006, Clinical Neurophysiology.

[129]  V. Calhoun,et al.  Selective changes of resting-state networks in individuals at risk for Alzheimer's disease , 2007, Proceedings of the National Academy of Sciences.

[130]  G. Shepherd The Synaptic Organization of the Brain , 1979 .

[131]  Adrian K. C. Lee,et al.  Attention Drives Synchronization of Alpha and Beta Rhythms between Right Inferior Frontal and Primary Sensory Neocortex , 2015, The Journal of Neuroscience.

[132]  J. Martinerie,et al.  The brainweb: Phase synchronization and large-scale integration , 2001, Nature Reviews Neuroscience.

[133]  M. Corbetta,et al.  Evolutionarily Novel Functional Networks in the Human Brain? , 2013, The Journal of Neuroscience.

[134]  R. O’Connell,et al.  Pupil diameter covaries with BOLD activity in human locus coeruleus , 2014, Human brain mapping.

[135]  Stefan Everling,et al.  Stable long-range interhemispheric coordination is supported by direct anatomical projections , 2015, Proceedings of the National Academy of Sciences.

[136]  Robert T. Thibault,et al.  The self-regulating brain and neurofeedback: Experimental science and clinical promise , 2016, Cortex.

[137]  Anthony J. Rissling,et al.  Electroencephalography (EEG) and Event‐Related Potentials (ERPs) with Human Participants , 2010, Current protocols in neuroscience.

[138]  Robert W Thatcher,et al.  Coherence, Phase Differences, Phase Shift, and Phase Lock in EEG/ERP Analyses , 2012, Developmental neuropsychology.

[139]  Ahmad Khodayari-Rostamabad,et al.  A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder , 2013, Clinical Neurophysiology.

[140]  Sabine Van Huffel,et al.  Bayesian model selection of template forward models for EEG source reconstruction , 2014, NeuroImage.

[141]  Barry D. Van Veen Minimum variance beamforming with soft response constraints , 1991, IEEE Trans. Signal Process..

[142]  C. Joyce,et al.  The face-sensitive N170 and VPP components manifest the same brain processes: The effect of reference electrode site , 2005, Clinical Neurophysiology.

[143]  Adam Gazzaley,et al.  Measuring functional connectivity during distinct stages of a cognitive task , 2004, NeuroImage.

[144]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[145]  A. Engel,et al.  Neuronal Synchronization along the Dorsal Visual Pathway Reflects the Focus of Spatial Attention , 2008, Neuron.

[146]  Simon K. Warfield,et al.  EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model , 2009, NeuroImage.

[147]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[148]  C. Miniussi,et al.  New insights into rhythmic brain activity from TMS–EEG studies , 2009, Trends in Cognitive Sciences.

[149]  J. Haueisen,et al.  Influence of head models on EEG simulations and inverse source localizations , 2006, Biomedical engineering online.

[150]  D. Tucker,et al.  EEG source localization: Sensor density and head surface coverage , 2015, Journal of Neuroscience Methods.

[151]  D. Lehmann,et al.  Topographic maps, source localization inference, and the reference electrode: comments on a paper by Desmedt et al. , 1993, Electroencephalography and clinical neurophysiology.

[152]  Leonardo L. Gollo,et al.  Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays , 2008, Proceedings of the National Academy of Sciences.

[153]  Robert Oostenveld,et al.  A comparative study of different references for EEG spectral mapping: the issue of the neutral reference and the use of the infinity reference , 2005, Physiological measurement.

[154]  W. Singer,et al.  Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology , 2006, Neuron.

[155]  Jaakko Malmivuo,et al.  Effect of measurement noise and electrode density on the spatial resolution of cortical potential distribution with different resistivity values for the skull , 2006, IEEE Transactions on Biomedical Engineering.

[156]  Maurizio Corbetta,et al.  A Signal-Processing Pipeline for Magnetoencephalography Resting-State Networks , 2011, Brain Connect..

[157]  Rolando J. Biscay-Lirio,et al.  Assessing interactions in the brain with exact low-resolution electromagnetic tomography , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[158]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[159]  Tülay Adali,et al.  Estimating the number of independent components for functional magnetic resonance imaging data , 2007, Human brain mapping.

[160]  Jürgen Kayser,et al.  Reference-independent ERP old/new effects of auditory and visual word recognition memory: Joint extraction of stimulus- and response-locked neuronal generator patterns. , 2007, Psychophysiology.

[161]  R R Edelman,et al.  Silent functional magnetic resonance imaging demonstrates focal activation in rapid eye movement sleep , 1999, Neurology.

[162]  Bin Hu,et al.  Towards an Efficient and Accurate EEG Data Analysis in EEG-Based Individual Identification , 2010, UIC.

[163]  Stephen M. Smith,et al.  The future of FMRI connectivity , 2012, NeuroImage.

[164]  Guido Nolte,et al.  The use of standardized infinity reference in EEG coherency studies , 2007, NeuroImage.

[165]  Claude Tomberg,et al.  Topographic analysis in brain mapping can be compromised by the average reference , 2005, Brain Topography.

[166]  Yang Li,et al.  An Alpha resting EEG study on nonlinear dynamic analysis for schizophrenia , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[167]  I. Lemahieu,et al.  Influence of measurement noise and electrode mislocalisation on EEG dipole-source localisation , 2000, Medical and Biological Engineering and Computing.

[168]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.