Table 4 Applications of source localization analyses
From: The applied principles of EEG analysis methods in neuroscience and clinical neurology
References | Data type | Subjects | Method | Disease/state | Application | Effect evaluation |
|---|---|---|---|---|---|---|
Toole et al. [124] | EEG | Epilepsy (n = 9) | MNE | Seizure location | Investigated the specificity of epileptic patients using the minimum norm | HFA observed in tEEG was found to be localized to the surface of subject-specific cortical models, occurring predominantly at seizure onset, as per the assertions in this article |
Galaris et al. [125] | EEG | Epilepsy (boys = 10, girls = 11) | MNE | Seizure location | Conducted an EEG source localization analysis during a visual working memory task in children with epilepsy using the minimum norm | The spatio-temporal patterns of differences between groups of epileptic and control children were claimed to be consistent across all three methods, according to this article |
Lee et al. [126] | EEG/MEG | Normal (n = 1) | MNE | ERP location | Employed the minimum norm to study the source localization of auditory stimuli | Utilizing individual anatomical MRI data, this article asserted the possibility of establishing a relationship between sensor information and dipole activation on the cortex |
Sperli et al. [127] | EEG | Epilepsy (males = 11, females = 19) | MNE | Seizure location | Examined the application of source localization algorithms in pediatric epilepsy using the minimum norm | The ESI was claimed to compare favorably to other imaging techniques, achieving a success rate of 90%, positioning it as a valuable tool for epilepsy surgery planning in children, as stated in this article |
Plummer et al. [128] | EEG | Epilepsy (children = 8) | FOCUSS | Seizure location | Performed source localization analysis of EEG during seizures in patients with focal epilepsy using the FOCUSS algorithm | The clinical utility of routine work-up for unilateral BFEC and unilateral MTLE secondary to hippocampal sclerosis was demonstrated using distributed source modeling in this article |
Wei et al. [129] | EEG | Epilepsy (n = 1) | FOCUSS | Seizure location | Combined the FOCUSS algorithm with the LORETA algorithm for epileptic focus localization | The article suggested the potential use of estimated source energy trends for predicting epileptic seizures, showcasing the algorithm’s application in both localization and prediction aspects |
Ye et al. [130] | EEG | Normal (n = 2) | FOCUSS | ERP location | Reconstructed MRI images with the FOCUSS algorithm | The new algorithm’s successful application for synthetic data and in vivo brain imaging obtained by an under-sampled radial spin echo sequence was claimed in this article |
Saletu et al. [131] | EEG | Depressed menopausal syndrome (females = 60) Menopausal syndrome (females = 30) Normal (females = 30) | LORETA | Pharmacotherapy of depression | Used the LORETA algorithm to study the effects of drugs on patients with depression before and after treatment | EEG activity in the theta band was claimed to be increased in anatomically meaningful patterns in patients, differing from the distribution in healthy individuals, according to this article |
Clemens et al. [132] | EEG | Epilepsy (n = 40) Normal (n = 14) | LORETA | Seizure location | Applied spectral analysis and LORETA to investigate and localize the sources of spontaneous theta activity in patients with partial epilepsy, distinguishing between untreated and treated groups, as well as healthy individuals | Untreated partial epilepsy patients were reported to display bilateral theta maxima in specific brain areas, while treated patients showed increased theta activity across the scalp with shifting abnormality centers in certain areas, as revealed in this article |
Kopřivová et al. [133] | EEG | OCD (n = 50) Normal (n = 50) | LORETA | OCD | Utilized sLORETA and normative ICA to assess intracortical EEG sources in 50 patients with OCD, revealing increased low-frequency activity in the medial frontal cortex compared to matched controls | Low-frequency power excess in the medial frontal cortex of OCD patients was indicated through sLORETA and group ICA methods, providing consistent evidence for medial frontal hyperactivation in OCD, as reported in this article |
Shao et al. [134] | EEG | Normal (n = 26) | LORETA | Acute tonic pain | Conducted a brain source localization analysis of tonic cold pain with the LORETA algorithm | Changes in cortical source power across different frequency bands in multiple brain regions were demonstrated as potential electrocortical indices of acute tonic pain, correlating with subjective pain ratings, in this article |
Loughrey et al. [135] | EEG | Normal (n = 14) Hearing loss (n = 44) | sLORETA | Hearing loss | Used the sLORETA method to study the relationship between age-related hearing loss and visual working memory | Greater activity in networks modulated by frontoparietal and temporal regions was indicated through sLORETA analyses in this article |
Dubová et al. [136] | EEG | Normal (males = 5, females = 5) | sLORETA | Mirrored touch | Used the sLORETA method for the brain projection of mirrored touch | The summation of stimuli secured by interpersonal haptic contact modified by mirror illusion was claimed to activate brain areas integrating motor, sensory, and cognitive functions, as well as areas related to communication and understanding processes, including the mirror neuron system, according to this article |
Liu et al. [137] | EEG | Vestibular migraine (females = 33) Normal (females = 20) | sLORETA | Vestibular migraine | Studied visual evoked potentials in patients with vestibular migraine using the sLORETA method | This article suggested that abnormalities in vestibular cortical fields might be a pathophysiological mechanism of vestibular migraine |
Yoshinaga et al. [138] | EEG | Epilepsy (boys = 4, girls = 4) | Dipole | Panayiotopoulos syndrome | Analyzed EEG signals in patients with panayiotopoulos syndrome, a form of benign childhood partial epilepsy, using the dipole method | A potential pathogenetic link between panayiotopoulos syndrome and rolandic epilepsy was suggested in this article |
Ebersole [139] | iEEG | Epilepsy (n = 10) | Dipole | Seizure location | Used dipole models for the non-invasive localization of epileptogenic foci | Patients with lateral temporal cortical seizures were claimed to have spikes and ictal activity modeled principally by radial dipoles, as reported in this article |
Nakajima et al. [140] | EEG | Stroke (n = 1) | Dipole | Cerebral infarction | Employed the dipole method to track and analyze brain potentials in patients with stroke | The dipole equivalent of the slow wave was reported to be approximately located in the frontal part of the left cingulate gyrus in this article |
Verhellen et al. [141] | EEG/iEEG | Epilepsy | Dipole | Seizure location | Explored the localization of refractory temporal lobe epilepsy through the dipole method | Dipole localizations and intracerebral fields recorded with depth electrodes were compared in this article |
Ntolkeras et al. [142] | EEG/MEG/iEEG | Epilepsy (boys = 7, girls = 4) | Dipole | Seizure location | Conducted a comparison and validation analysis of epileptic patients before and after surgical resection using the dipole method | Magnetic and ESI dipole clustering was claimed to help localize the seizure onset zone and irritative zone, facilitating the prognostic assessment of MRI-negative patients with drug-resistant epilepsy |
Knyazev et al. [143] | EEG | Normal (males = 19, females = 36) | Beamforming | Depression | Conducted a beamforming analysis on the EEG signals of depressed patients when they completed different tasks | Emotional circuits were asserted to be more strongly connected with DMN than TPN in this article |
Neugebauer et al. [144] | EEG/MEG | Epilepsy (male = 1, female = 1) | Beamforming | Seizure location | Utilized the beamforming method to explore epileptogenic zones in focal cortical dysplasia | The beamformer was claimed to localize better than the standard dipole scan for appropriate regularization parameter choices in this article |
Ward et al. [145] | EEG | Epilepsy (n = 4) | Beamforming | Seizure location | Analyzed deep epileptic form activity using beamforming techniques | The beamformer was demonstrated to enhance signals from deep foci, improving SNR and showing promise in the detection of epileptiform events in this article |
Kouchaki et al. [146] | EEG | Normal (n = 17) | Beamforming | Brain fatigue | Employed the beamforming approach to explore brain changes from non-fatigued to fatigued states | The proposed MVB-based feature, applied to SVM classification, achieved a remarkable 97.06% accuracy in differentiating between non-fatigue and fatigue mental states, significantly outperforming conventional EEG features, as highlighted in this study |
Vergallo et al. [147] | EEG | Simulated signals | Beamforming | Seizure location | Adopted the beamforming method to diagnose epilepsy | Simple geometry, simulations, and results demonstrating the performance of several algorithms were considered in this article |
Ponomarev et al. [148] | EEG | ADHD (females = 46, males = 50) Normal (males = 167, females = 209) | CSD | ADHD | Analyzed EEG signals in patients with ADHD using CSD | The spectral power of local EEG activity isolated by gICA or CSD in fronto-central areas was suggested as a suitable marker for discriminating ADHD patients and healthy adults in this article |
Stewart et al. [149] | EEG | Normal (males = 95, females = 211) | CSD | Major depressive disorder | Utilized CSD for analyzing resting-state and task-evoked prefrontal EEG asymmetry in patients with depression | CSD-transformed data was claimed to be a more robust indicator of trait frontal EEG asymmetry, according to this article |
Grin-Yatsenko et al. [150] | EEG | SZ (males = 36, females = 12) Normal (males = 217, females = 286) | CSD | SZ | Employed the CSD method to analyze brain activity in schizophrenic patients | Differences in the delta and theta range were claimed to be described mainly by local components, and those in the beta range mostly by spatially widely distributed ones, in this article |
Kamarajan et al. [151] | EEG | Alcoholics (males = 38) Normal (males = 38) | CSD | Alcohol | Probed alcohol dependence using the CSD approach | Decreased power and a weaker, more diffuse CSD in alcoholics were claimed to be due to dysfunctional neural reward circuitry, as suggested in this article |