EEG Source Imaging: A Practical Review of the Analysis Steps
By C. Michel, Denis Brunet. Frontiers in Neurology, 2019
624 citations recorded by the source corpus.
The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/6c4c322d4fcc2e6aff233f8c08ea8b834050334f
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In Cognitive Neuroscience
Citations in the corpus
- Distributed theta networks support the control of working memory: Evidence from scalp and intracranial EEG (2025, unknown): “Scalp EEG source localization faces inherent limitations, as the inverse problem does not yield a unique solution without additional assumptions or constraints (Grech et al., 2008; Michel & Brunet, 2019; Lai et al., 2018).”
- The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task (2024, methodology): “First, we acknowledge that EEG source localization improves with high electrode density and when using individualized head models (Michel and Brunet, 2019), while here we adopted a template head model and we used 60 electrodes, with data rank further reduced by the procedure for artifact removal.”