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Solving the EEG inverse problem
Published on Apr 03, 20143815 Views
EEG and MEG measure brain electrical activity indirectly from outside the head, where each sensor measures a superposition of activity from the entire brain/cortex rather than only from its closest so
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Solving the EEG inverse problem00:00
Outline00:01
Electroencephalography (EEG)00:55
Volume conduction: attenuation and spatial smearing - 101:54
Volume conduction: attenuation and spatial smearing - 202:59
Volume conduction: superposition of activity - 103:12
Volume conduction: superposition of activity - 203:37
Volume conduction: superposition of activity - 303:41
Volume conduction: difficulties caused by03:54
Illustration: sensor-space analysis - 104:43
Illustration: sensor-space analysis - 205:21
Model for EEG data06:00
The lead field (forward mapping) - 107:20
The lead field (forward mapping) - 208:21
The current density - 109:10
The current density - 309:40
The current density - 209:43
The Inverse Problem - 109:58
The Inverse Problem - 210:21
The Inverse Problem - 310:47
The Inverse Problem - 411:11
The Inverse Problem - 511:20
Inverse methods11:38
Maximum-likelihood and maximum a-posteriori estimation12:09
Spatial constraints13:42
Spatial constraints: smoothness - 113:58
Spatial constraints: smoothness - 214:33
Spatial constraints: sparsity - 114:54
Spatial constraints: sparsity - 215:16
Limitations of smooth and sparse inverses - 115:46
Limitations of smooth and sparse inverses - 216:19
Combining sparsity and smoothness - 116:41
Combining sparsity and smoothness - 216:53
Combining sparsity and smoothness - 317:15
Comparison17:26
Localization of hand areas in somatosensory cortex - 117:51
Localization of hand areas in somatosensory cortex - 218:36
Technicalities18:54
Reconstruction of time series - 120:34
Reconstruction of time series - 220:57
Reconstruction of time series - 321:39
Reconstruction of time series - 421:59
Reconstruction of time series - 522:11
Other source localization paradigms23:37
(Blind) source separation24:53
The factor/component time series27:24
The sensor space activation patterns27:42
Source localization of activation patterns27:53
Forward and backward models28:36
Parameter interpretation31:06
BSS methods - 132:18
BSS methods - 232:47
BSS methods by assumption - 133:10
BSS methods by assumption - 234:38
BSS for oscillations36:14
Spatio-spectral decomposition (SSD)38:35
Common spatial patterns (CSP)40:21
Source power correlation analysis (SPoC)42:02
Extraction of steady-state auditory evoked potentials - 142:51
Extraction of steady-state auditory evoked potentials - 243:55
Summary44:30