Simultaneous EEG-fMRI Techniques in Neuroimaging

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combine the millisecond-scale temporal resolution of EEG with the millimetre-scale spatial resolution of fMRI. By acquiring both modalities in the same session, researchers can study neural dynamics through electrical potentials alongside haemodynamic responses reflected in the blood oxygenation level-dependent (BOLD) signal. This hybrid approach addresses critical questions in cognitive neuroscience, clinical neurology and sleep research, offering insights into epileptic discharges, attention networks and sensory processing. Core challenges include attenuation and correction of gradient and pulse artefacts in EEG data recorded inside the MR scanner, ensuring electromagnetic compatibility of EEG hardware, and synchronising acquisition clocks. Advances in hardware design, real-time artefact rejection algorithms and integrated analysis pipelines now enable more reliable characterisation of brain oscillations, cross-modal coupling and network interactions. Practical applications range from mapping epileptogenic zones in presurgical evaluation to tracking rapid fluctuations in resting-state networks, thus advancing both fundamental science and clinical diagnostics.

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A recent open-access dataset provides simultaneous EEG-fMRI recordings from healthy adults viewing naturalistic stimuli. This resource includes raw and preprocessed data for resting state, flickering checkerboard paradigms and short movies, along with physiological and behavioural measures. It underpins optimisation of preprocessing pipelines and facilitates studies correlating EEG power fluctuations with BOLD signals during complex visual tasks.

Another study combined pupillometry with EEG and fMRI during an auditory oddball paradigm to investigate salience processing. By modelling effective connectivity and analysing target-evoked pupillary responses, researchers revealed interactions between the salience network and the locus coeruleus–norepinephrine system in resetting and switching cortical networks. This work highlights how multimodal measures can elucidate the dynamic interplay between electrical activity, autonomic markers and haemodynamic coupling in cognition.

In a large cohort study of resting-state high-density EEG-fMRI, spatio-spectral source-space decomposition was applied to band-limited EEG to extract reproducible patterns and compare them with BOLD signatures. The analysis identified robust EEG components – including occipital alpha dynamics – and demonstrated that while EEG and BOLD patterns show partial spatial overlap, they capture complementary aspects of low-frequency neural dynamics. This approach advances our understanding of how frequency-specific electrical activity maps onto large-scale functional networks.

Simultaneous EEG-fMRI Techniques in Neuroimaging publication trend

The graph below shows the total number of articles in simultaneous eeg-fmri techniques in neuroimaging across all publications each year (not limited to Nature Index journals).

Technical terms

BOLD signal: Blood oxygenation level-dependent contrast measured by fMRI, reflecting changes in deoxyhaemoglobin concentration linked to neural activity.

Gradient artefact: Voltage distortion in EEG recordings caused by rapidly switching magnetic field gradients during MR image acquisition.

Pulse artefact: Ballistocardiogram-related noise in EEG due to cardiac-driven head motion and pulsatile blood flow within the scanner.

Spatio-spectral decomposition: Analytical technique that separates EEG data into components characterised by both spatial distribution and frequency content.

Source reconstruction: Computational method to estimate the intracranial origins of EEG signals based on scalp recordings and head models.

References

1. An open-access dataset of naturalistic viewing using simultaneous EEG-fMRI. Scientific Data (2023).
2. Pupillary response is associated with the reset and switching of functional brain networks during salience processing. PLOS Computational Biology (2023).
3. Spatial (mis)match between EEG and fMRI signal patterns revealed by spatio-spectral source-space EEG decomposition. Frontiers in Neuroscience (2025).