This study investigates the electrophysiological signatures of resting state networks (RSNs) in the human brain using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). By applying independent component analysis (ICA) to fMRI data, six widely distributed RSNs were identified. These RSNs were correlated with EEG power variations in delta, theta, alpha, beta, and gamma rhythms, revealing specific electrophysiological signatures for each network. The results support the hypothesis that multiple brain rhythms coexist within large-scale brain networks, as suggested by biophysical studies. The study shows that resting state networks are organized in spatiotemporal patterns and fluctuate at low frequencies (0.01–0.1 Hz). These fluctuations are associated with various brain functions, including sensory-motor, visual, auditory, attention, language, and default networks. The analysis of simultaneous EEG/fMRI data revealed that different frequency bands are coupled to mediate brain operations, and that each RSN has a unique combination of EEG rhythms. The study identified six RSNs: the default-mode network, the dorsal attention network, the visual network, the auditory-phonological network, the somato-motor network, and the self-referential network. These networks were associated with specific EEG rhythms, with the default-mode network showing positive correlations with alpha and beta rhythms, the visual network with all rhythms except gamma, the auditory network with delta, theta, and beta rhythms, the somato-motor network with beta rhythms, and the self-referential network with gamma rhythms. The study also found that the BOLD signal time-course corresponding to each RSN was correlated with the EEG reference waveforms of various frequency bands. The results showed that the correlation between BOLD signal RSNs and EEG power was robust and reliable. The study further demonstrated that these RSNs can be separated based on their specific EEG power profiles, indicating that they represent distinct functional entities. The study highlights the importance of analyzing multiple frequency bands simultaneously rather than focusing on a single band. It also suggests that the slow fluctuations of BOLD and EEG signals may reflect a dynamic baseline of inter-areal temporal interaction, rather than a basic neurophysiological mechanism unrelated to functional neuronal communication. The findings contribute to the understanding of the functional role of spontaneous brain activity and the mechanisms underlying intrinsic brain activity.This study investigates the electrophysiological signatures of resting state networks (RSNs) in the human brain using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). By applying independent component analysis (ICA) to fMRI data, six widely distributed RSNs were identified. These RSNs were correlated with EEG power variations in delta, theta, alpha, beta, and gamma rhythms, revealing specific electrophysiological signatures for each network. The results support the hypothesis that multiple brain rhythms coexist within large-scale brain networks, as suggested by biophysical studies. The study shows that resting state networks are organized in spatiotemporal patterns and fluctuate at low frequencies (0.01–0.1 Hz). These fluctuations are associated with various brain functions, including sensory-motor, visual, auditory, attention, language, and default networks. The analysis of simultaneous EEG/fMRI data revealed that different frequency bands are coupled to mediate brain operations, and that each RSN has a unique combination of EEG rhythms. The study identified six RSNs: the default-mode network, the dorsal attention network, the visual network, the auditory-phonological network, the somato-motor network, and the self-referential network. These networks were associated with specific EEG rhythms, with the default-mode network showing positive correlations with alpha and beta rhythms, the visual network with all rhythms except gamma, the auditory network with delta, theta, and beta rhythms, the somato-motor network with beta rhythms, and the self-referential network with gamma rhythms. The study also found that the BOLD signal time-course corresponding to each RSN was correlated with the EEG reference waveforms of various frequency bands. The results showed that the correlation between BOLD signal RSNs and EEG power was robust and reliable. The study further demonstrated that these RSNs can be separated based on their specific EEG power profiles, indicating that they represent distinct functional entities. The study highlights the importance of analyzing multiple frequency bands simultaneously rather than focusing on a single band. It also suggests that the slow fluctuations of BOLD and EEG signals may reflect a dynamic baseline of inter-areal temporal interaction, rather than a basic neurophysiological mechanism unrelated to functional neuronal communication. The findings contribute to the understanding of the functional role of spontaneous brain activity and the mechanisms underlying intrinsic brain activity.