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Existing models of EEG have mainly focused on relations to network dynamics characterized by firing rates [L. de Arcangelis, H.J. Herrmann, C. Perrone-Capano, Activity-dependent brain model explaining EEG spectra, arXiv:q-bio.NC/0411043 v1, 23 Nov 2004; D.T. Liley, D.M. Alexander, J.J. Wright, M.D. Aldous, Alpha rhythm emerges from large-scale networks of realistically coupled multicompartmental model cortical neurons, Network 10(1) (1999) 79-92; O. David, J.K. Friston, A neural mass model for MEG/EEG: coupling and neuronal dynamics, NeuroImage 20 (2003) 1743-1755]. Generally, these models assume that there exists a linear mapping between network firing rates and EEG states. However, firing rate is only one of several descriptors for network activity states. Other relevant descriptors are synchrony and irregularity of firing patterns [N. Brunel, Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons, J. Comput. Neurosci. 8(3) (2000) 183-208]. To develop a better understanding of the EEG we need to relate these state descriptors to EEG states. Here, we try to go beyond the firing rate based approaches described in [D.T. Liley, D.M. Alexander, J.J. Wright, M.D. Aldous, Alpha rhythm emerges from large-scale networks of realistically coupled multicompartmental model cortical neurons, Network 10(1) (1999) 79-92; O. David, J.K. Friston, A neural mass model for MEG/EEG: coupling and neuronal dynamics, NeuroImage 20 (2003) 1743-1755] and relate synchronicity and irregularity in the network to EEG states. We show that the transformation between network activity and EEG can be approximately mediated by linear kernel with the shape of an @a- or @c-function, allowing us a comparison between EEG states and network activity space. We find that the simulated EEG generated from asynchronous irregular type network activity is closely related to the human EEG recorded in the awake state, evaluated using power spectral density characteristics.