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Machine Learning for Multimodal Neuroimaging
Published on 2014-04-033425 Views
The combination of multiple neuroimaging modalities has become an important field of research. While the technical challenges associated with multimodal neuroimaging have been mastered more than a dec
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Presentation
Machine Learning for Multimodal Neuroimaging00:00
Acknowledgements00:21
Motivation for Multimodal Neuroimaging02:25
Electromagnetic Field Changes - 102:53
Electromagnetic Field Changes - 204:25
Neurovascular Coupling05:21
Blood-Oxygen Level Dependent Signal - 107:45
Blood-Oxygen Level Dependent Signal - 209:27
Blood-Oxygen Level Dependent Signal - 310:33
Multimodal Neuroimaging: Benefits and Challenges11:19
Outline13:16
Multimodal Neuroimaging14:10
Analysis of Multimodal Neuroimaging Data14:27
Analysis of Multimodal Neuroimaging Data - 114:30
Analysis of Multimodal Neuroimaging Data - 215:13
Supervised Analyses - 117:35
Supervised Analyses - 217:37
Supervised Analysis: General Linear Models18:15
EEG Bandpower as Label for fMRI GLM19:14
Supervised Analyses - 319:58
Supervised Analyses - 420:21
Supervised Analyses - 520:53
Unsupervised Analyses22:17
Principal Component Analysis - 123:46
Principal Component Analysis - 224:32
Principal Component Analysis - 325:23
Principal Component Analysis - 426:05
Principal Component Analysis - 526:43
Principal Component Analysis - 627:12
Principal Component Analysis - 727:52
Principal Component Analysis - 829:02
Kernel Principal Component Analysis29:07
Principal Component Analysis - 930:16
Unimodal Unsupervised Analyses31:11
Multimodal Unsupervised Analyses32:42
Classical Approach To Multimodal Neuroimaging32:50
When Unimodal Methods Fail: CCA vs. PCA33:14
Canonical Correlation Analysis - 133:39
Canonical Correlation Analysis - 235:47
Derivation CCA36:01
CCA and Other Projection Methods37:12
Why You Should Not Interpret Filter Coefficients38:51
Transforming Filters into Patterns40:59
CCA Filters and CCA Patterns41:42
Information Theoretic Measures42:46
CCA for Multiple Modalities - 144:44
CCA for Multiple Modalities - 245:39
Unimodal and Multimodal Analyses46:38
Problems of Standard CCA48:34
Kernel CCA - 148:58
Kernel CCA - 250:24
Problems of Standard Kernel CCA51:36
Temporal Kernel CCA - 153:12
Temporal Kernel CCA - 253:49
Temporal Kernel CCA - 354:21
Non-instantaneous Coupling: CCA vs. tkCCA54:48
Multimodal Unsupervised EEG-fMRI Source Estimation - 155:42
Multimodal Unsupervised EEG-fMRI Source Estimation - 257:49
EEG-fMRI Source Estimation - 159:09
EEG-fMRI Source Estimation - 201:00:36
EEG-fMRI Source Estimation - 301:01:15
Multimodal Source Power Correlation Analysis - 101:02:03
Multimodal Source Power Correlation Analysis - 201:02:47
Multimodal Source Power Correlation Analysis - 301:03:12
Multimodal Source Power Correlation Analysis - 401:03:59
Multimodal Source Power Correlation Analysis - 501:04:15
Multimodal Source Power Correlation Analysis - 601:04:38
Applications - 101:04:53
Applications - 201:04:58
Hybrid BCIs Improve Mental State Detection - 101:05:42
Hybrid BCIs Improve Mental State Detection - 201:05:44
Hybrid BCIs Improve Mental State Detection - 301:05:48
Hybrid BCIs Improve Mental State Detection - 401:06:05
Hybrid BCIs Improve Mental State Detection - 501:06:40
Summary - 101:07:11
Hybrid BCIs Improve Mental State Detection - 601:07:31
PCA For Artifact Removal in Multimodal Recordings01:09:29
Simultaneous Neural and Hemodynamic Measurements01:09:38
Online Recording System01:10:53
Scanner Gradient Artifact Removal - 101:11:29
Scanner Gradient Artifact Removal - 201:12:13
Empirical Criteria for Artifact Removal01:14:10
Summary - 201:16:14
Decoding Neural Information from fMRI Signals01:19:19
Standard Model of Neurovascular Coupling01:19:21
When Canonical HRF Models Fail01:19:40
Is Spatiotemporal HRF Variability Neural Information?01:21:07
Decoding Neural Information: Workflow01:21:09
Predicting Neural Amplitude from fMRI01:21:24
Temporal Dynamics Extracted01:21:43
TkCCA vs Canonical HRF Models - 101:21:50
TkCCA vs Canonical HRF Models - 201:22:18
Optimal Preprocessing for fMRI Decoding01:22:33
How Much Neural Information is in fMRI signals?01:22:36
Mutual Information Estimates01:22:38
Mutual Information Estimates: EEG-fMRI01:22:45
Summary - 301:22:57
Multimodal Analyses for Hyperscanning - 101:23:01
Multimodal Analyses for Hyperscanning - 201:23:16
Hyperscanning01:25:00
Common Activation Patterns - 101:25:17
Common Activation Patterns - 201:26:51
Summary - 401:27:07
Applications01:27:45
Thank you01:28:37