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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
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
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
bold brain pong experimental logic
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 !

subject pretraining of graded control
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
pretraining of graded control results

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

neurofeedback training with brain pong
Neurofeedback-Training with “Brain Pong“

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

two voxels

?

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

slide13

From Univariate to Multivariate: Patterns as Points

Voxel 2

Voxel 1

Voxel 1

Voxel 2

Univariate

Multivariate

slide14

From Univariate to Multivariate: Easy Case

Voxel 2

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Voxel 1

Voxel 1

Voxel 2

Univariate

Multivariate

slide15

From Univariate to Multivariate: Difficult Case

Voxel 2

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Voxel 1

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Univariate

Multivariate

slide16

From Univariate to Multivariate: Decision Boundary

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Univariate

Multivariate

slide17

From Univariate to Multivariate: Classifier

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Voxel 1

Voxel 1

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Univariate

Multivariate

slide18

From Univariate to Multivariate: Generalization

Voxel 2

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Voxel 1

Voxel 1

Voxel 2

Univariate

Multivariate

hard margin svm1
Hard-Margin SVM

Principle: Large-Margin Separation

soft margin svm
Soft-Margin SVM

Principle: Large-Margin Separation Tolerating Misclassification

Minimize: ½||w||2 + C ∑ξi

soft margin svm1
Soft-Margin SVM

Role of SVM parameter “C”: Larger margin vs less errors in training data

Find best value for C using cross-validation

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