Electric Dipole Tracing in the Brain by Means of the Boundary Element Method and Its Accuracy

Cited by (334)

Items 201–300 of 334

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10. Mapping epileptogenic brain using a unified spatial–temporal–spectral source imaging framework
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11. Electroencephalographic source imaging of spikes with concurrent high‐frequency oscillation s is concordant with the clinical ground truth
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12. Estimating underlying neuronal activity from EEG using an iterative sparse technique
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13. Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy
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14. Cortical Potential Imaging of Somatosensory Evoked Potentials by Means of the Boundary Element Method in Pediatric Epilepsy Patients
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19. Estimation of neural architecture in human brain by means of the dipole tracing method
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21. Time-estimation process could cause the disappearence of readiness potential
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22. Deep learning-based EEG source imaging is robust under varying electrode configurations
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23. An improved technique to consider mismatches between fMRI and EEG/MEG sources for fMRI constrained EEG/MEG source imaging
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28. The relationship between conductivity uncertainties and EEG source localization accuracy
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35. Effects of transcranial direct current stimulation on brain activity and cortical functional connectivity in children with autism spectrum disorders
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36. On the concordance between electrical source imaging, anatomical and functional neuroimaging in patients with focal epilepsy
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38. Debiased Estimation and Inference for Spatial–Temporal EEG/MEG Source Imaging
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48. Breathlessness-related Brain Activation: Electroencephalogram Dipole Modeling Analysis
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54. A new electrocardiography inverse solution by means of neural networks
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55. An EEG forward solution using second-order anisotropic FEM
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56. On a Calderón preconditioner for the symmetric formulation of the electroencephalography forward problem without barycentric refinements
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59. ICA-Based EEG Spatio-temporal Dipole Source Localization: A Model Study
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63. Estimating cortical column sensory networks in rodents from micro-electrocorticograph (μECoG) recordings
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73. Spatiotemporal Rhythmic Seizure Sources Can be Imaged by means of Biophysically Constrained Deep Neural Networks
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80. Dynamic seizure imaging in patients with extratemporal lobe epilepsy
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