A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities
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Abstract (arXiv)
In recent years, multiple noninvasive imaging modalities have been used to develop a better understanding of the human brain functionality, including positron emission tomography, single-photon emission computed tomography, and functional magnetic resonance imaging, all of which provide brain images with millimeter spatial resolutions. Despite good spatial resolution, time resolution of these methods are poor and values are about seconds. Electroencephalography (EEG) is a popular non-invasive electrophysiological technique of relatively very high time resolution which is used to measure electric potential of brain neural activity. Scalp EEG recordings can be used to perform the inverse problem in order to specify the location of the dominant sources of the brain activity. In this paper, EEG source localization research is clustered as follows: solving the inverse problem by statistical method (37.5%), diagnosis of brain abnormalities using common EEG source localization methods (18.33%), improving EEG source localization methods by non-statistical strategies (3.33%), investigating the effect of the head model on EEG source imaging results (12.5%), detection of epileptic seizures by brain activity localization based on EEG signals (20%), diagnosis and treatment of ADHD abnormalities (8.33%). Among the available methods, minimum norm solution has shown to be very promising for sources with different depths. This review investigates diseases that are diagnosed using EEG source localization techniques. In this review we provide enough evidence that the effects of psychiatric drugs on the activity of brain sources have not been enough investigated, which provides motivation for consideration in the future research using EEG source localization methods.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2019.
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Citing papers per year: 2024: 3; 2025: 0; 2026: 1.
4 papers.
- 2026Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flowcited by 2
- 2024A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertaintiescited by 0
- 2024Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelinescited by 0
- 2024High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruningcited by 0
- 2026Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow27 Feb 2026
- 2024High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning19 Nov 2024
- 2024Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines9 Aug 2024
- 2024A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties9 May 2024