Assessing the dependence of the number of EEG channels in the brain networks’ modulations

Aim of the study was to evaluate the influence of the EEG channels number on the brain networks' analysis, to establish whether and how much higher density EEG actually contributes to add supplementary information to brain networks analyses. 59 electrodes EEGs were recorded in 20 healthy subjects in eyes open and closed condition. For each condition, we analyzed the recording dataset of 59 channels, and three sub-datasets obtained by the selection of 44, 30, 19 channels from the 59 ones. Then we computed the EEG sources of current density and evaluated the SW index in the four EEGs data montages. Results showed that in the eyes open condition the number of recording channels influences more the SW index modulation respect that in the eyes closed condition. Conversely, in the eyes closed condition the brain activity is less affected by specific brain regions' activations and the signal's generators produced not significant variations on EEG data and consequently the small world network measure is not affected by the recording channels number. We can conclude that in the eyes closed condition, the 19 EEG channels is an acceptable montage to study brain networks' modulations, to both detect the higher and the lower brain waves' frequencies.

[1]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[2]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[3]  Paolo Maria Rossini,et al.  Small World Index in Default Mode Network Predicts Progression from Mild Cognitive Impairment to Dementia , 2020, Int. J. Neural Syst..

[4]  N. Wenderoth,et al.  Detecting large‐scale networks in the human brain using high‐density electroencephalography , 2017, Human brain mapping.

[5]  Robert Oostenveld,et al.  The five percent electrode system for high-resolution EEG and ERP measurements , 2001, Clinical Neurophysiology.

[6]  P. Rossini,et al.  Electroencephalography-Derived Sensory and Motor Network Topology in Multiple Sclerosis Fatigue , 2017, Neurorehabilitation and neural repair.

[7]  Roberto D. Pascual-Marqui,et al.  Instantaneous and lagged measurements of linear and nonlinear dependence between groups of multivariate time series: frequency decomposition , 2007, 0711.1455.

[8]  P. Rossini,et al.  Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals , 2004, Clinical Neurophysiology.

[9]  Andrea Krott,et al.  Removing speech artifacts from electroencephalographic recordings during overt picture naming , 2015, NeuroImage.

[10]  R. Barry,et al.  EEG differences between eyes-closed and eyes-open resting conditions , 2007, Clinical Neurophysiology.

[11]  M S Buchsbaum,et al.  Computed EEG topography of response to visual and auditory stimuli. , 1986, Electroencephalography and clinical neurophysiology.

[12]  P. Pasqualetti,et al.  Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E , 2018, Annals of neurology.

[13]  P. Rossini,et al.  Non-Ceruloplasmin Copper Distinguishes A Distinct Subtype of Alzheimer's Disease: A Study of EEG-Derived Brain Activity. , 2016, Current Alzheimer research.

[14]  A. Longoni,et al.  Problems in the Assessment of Hand Preference , 1985, Cortex.

[15]  Marco Ganzetti,et al.  Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization , 2018, Front. Neuroinform..

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