1 / 35

Event Related Potentials (ERP)

Event Related Potentials (ERP). Analysis of the Electrical Brain Activity. EEG Sapling. Head and Source Model Definition. Temporal Averaging and/or Filtering. Spetial Filtering / Laplasian. Spatio-temporal Signal Decomposition. Source Localization of each Component .

landers
Download Presentation

Event Related Potentials (ERP)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Event Related Potentials (ERP) Analysis of the Electrical Brain Activity

  2. EEG Sapling Head and Source Model Definition Temporal Averaging and/or Filtering Spetial Filtering / Laplasian Spatio-temporal Signal Decomposition Source Localization of each Component Spatio-Temporal Flow Activity Pattern Recognition Neuro-Psychological Pattern Correction and Translation The General Scheme of Spatio-Temporal EEG Analysis

  3. Deblurring:Alan Gevins

  4. Sources of EEG Signals

  5. The Forward (direct) Problem Physics - propagation of currents through voltage conductance Lead Field (projection) Matrix - K

  6. G/W Matter Surface from MRI

  7. Distribute Sources on fMRI Scalp Skull Brain CSF

  8. The Inverse Problems Forward problem: Find K that translates dipoles J to Scalp potentials Φ. Inverse problem: Find function F that transforms Scalp potentials Φ to dipoles J. Model F Ne measurements Nv unknowns Ill posed for Ne < Nv

  9. Over Determined Discrete Dipoles Models Ne > Nv

  10. BESA: Michael Scherg

  11. Under Determined Models Instantaneous Distributed Discrete Linear ! Forward problem: Find K that translates dipoles J to Scalp potentials Φ. Inverse problem: Find T that translates Scalp potentials Φ to dipoles J. Ideally: But:

  12. Linear Solutions The electrode data is of much lower dimension then the sources. Forward problem: Find K that translates dipoles J to Scalp potentials Φ. Infinite number of source patterns J that produce a given set of measurements. Inverse problem: Find T that translates Scalp potentials Φ to dipoles J. If J is of full rank Jcannot be exactly reconstructed - not enough data on the scalp. Ideally: If the sources are highly dependant in time and space, reconstruction of the observable portion of J is possible. NΦ≈102 NJ≈105-106

  13. LORETA (LOW RESOLUTION BRAIN ELECTROMAGNETIC TOMOGRAPHY):Roberto Pascual-Marqui • Relatively effective EEG source localization methods were developed • Their results are relatively established • Precision is in process of improvement

  14. Linear Solution The solution: J = TΦ, T = W-1KT[K W-1KT]-1, W=ATA It is easy to see that Φ = KJ = KTΦ = Φ. In cases of noisy data or inaccurate model, a regularization term is introduced: T = W-1KT[K W-1KT+αI]-1 Smaller α Bigger α ||AJ|| ||Φ-KJ|| Misfit Interdependency

  15. Instantaneous Linear Solutions Minimum Norm (min spatial independence): Weighted Minimum Norm: LORETA (min neighbor difference): sLORETA: Standardization: • Same independent solution for each time point: J(t) = TΦ(t), T = W-1KT[K W-1KT+αI]-1, W=ATA A: Spatial Filter NJ x NJ T: NJ x NФ

  16. LORETA(LOW RESOLUTION BRAIN ELECTROMAGNETIC TOMOGRAPHY): Roberto Pascual-Marqui • Relatively effective EEG source localization methods were developed • Their results are relatively established • Precision is in process of improvement

  17. LORETA (LOW RESOLUTION BRAIN ELECTROMAGNETIC TOMOGRAPHY):

  18. SToMP (Spatio-TempOral Matching Pursuit):Amir Geva

  19. Functional brain areas It is accepted that the brain is divisible to functional units

  20. Flow within synchronizations

  21. Flow between synchronizations

More Related