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Overview

Overview. Real-Time Brain-Computer Interfaces: Using online data for control - Brain Pong Decoding mental states in real-time using multiple ROIs Decoding mental states using real-time multi-voxel pattern classifiers. Hyper-Scanning and Neurofeedback. Is it possible to couple two brains ?

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Overview

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  1. Overview Real-Time Brain-Computer Interfaces: • Using online data for control - Brain Pong • Decoding mental states in real-time using multiple ROIs • Decoding mental states using real-time multi-voxel pattern classifiers

  2. Hyper-Scanning and Neurofeedback • Is it possible to couple two brains ? • Can two subjects exchange informationbased on ongoing fMRI measurements? • How difficult is it to learn to handle the hemodynamic delay? To what extent does this delay limit brain-brain interactions? • Proof of concept -> BOLDBrain Pong

  3. BOLD Brain Pong Experimental Logic Subjects control vertical position of racket bythe amplitude of theBOLD response in modulated brain area Subject 2 Subject 1 Up-and-down movement of racket requires graded control !

  4. Subject Pretraining of Graded Control Neurofeedback display • “Thermometer” visualization of target level and ROI activity • Easy to interpret by subjects • Continously updated gradual feedback • Immediate feedback max. 1 second after data acquisition

  5. low target level medium target level 1.4 high target level 1.2 1 0.8 0.6 Pretraining of Graded ControlResults Single episode Group analysis (n = 5): Beta weights All subjects were able to learn to activate spatially localized brain regions to different target levels

  6. Scanning Two Brains Simultaneously

  7. Interactive NeurofeedbackExperimental Setup

  8. Neurofeedback-Training with “Brain Pong“ Moderator Dennis Wilms (“W wie Wissen”, ARD) spielt das erste Mal “Brain Pong” mit dem fMRT-BCI

  9. “Brain Writing” fMRI Brain Computer InterfaceSorger et al (submitted)

  10. ? Two voxels • Are the two sites connected? • Do they interact? • Do they jointly encode the stimulus? Stimulus Addressing any of these questions requires multivariate analysis. Courtesy of Niko Kriegeskorte

  11. From Univariate to Multivariate: Patterns as Points Voxel 2 Voxel 1 Voxel 1 Voxel 2 Univariate Multivariate

  12. From Univariate to Multivariate: Easy Case Voxel 2 1 1 2 2 1 3 3 2 3 2 1 1 3 3 3 1 2 2 Voxel 1 Voxel 1 Voxel 2 Univariate Multivariate

  13. From Univariate to Multivariate: Difficult Case Voxel 2 1 3 2 2 1 1 1 1 2 2 3 3 3 3 1 3 2 2 Voxel 1 Voxel 1 Voxel 2 Univariate Multivariate

  14. From Univariate to Multivariate: Decision Boundary Voxel 2 1 3 2 2 1 1 1 1 2 2 3 3 3 3 1 3 2 2 Voxel 1 Voxel 1 Voxel 2 Univariate Multivariate

  15. From Univariate to Multivariate: Classifier Voxel 2 1 3 2 2 1 1 1 1 2 2 3 3 3 3 1 3 2 2 Voxel 1 Voxel 1 Voxel 2 Univariate Multivariate

  16. From Univariate to Multivariate: Generalization Voxel 2 1 2 2 1 1,2 Voxel 1 Voxel 1 Voxel 2 Univariate Multivariate

  17. Hard-Margin SVM

  18. Hard-Margin SVM Principle: Large-Margin Separation

  19. Soft-Margin SVM Principle: Large-Margin Separation Tolerating Misclassification Minimize: ½||w||2 + C ∑ξi

  20. Soft-Margin SVM Role of SVM parameter “C”: Larger margin vs less errors in training data Find best value for C using cross-validation

  21. Training a SVM Classifier

  22. Training a SVM Classifier

  23. Training a SVM Classifier

  24. Testing a Trained SVM Classifier

  25. Testing a Trained SVM Classifier

  26. Testing a Trained SVM Classifier

  27. Real-time detection of the locus of attention using SVM

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