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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


Scanning two brains simultaneously

Scanning Two Brains Simultaneously


Interactive neurofeedback experimental setup

Interactive NeurofeedbackExperimental Setup


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


Overview

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


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


Overview

From Univariate to Multivariate: Patterns as Points

Voxel 2

Voxel 1

Voxel 1

Voxel 2

Univariate

Multivariate


Overview

From Univariate to Multivariate: Easy Case

Voxel 2

1

1

2

2

1

3

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2

Voxel 1

Voxel 1

Voxel 2

Univariate

Multivariate


Overview

From Univariate to Multivariate: Difficult Case

Voxel 2

1

3

2

2

1

1

1

1

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2

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2

Voxel 1

Voxel 1

Voxel 2

Univariate

Multivariate


Overview

From Univariate to Multivariate: Decision Boundary

Voxel 2

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Voxel 1

Voxel 1

Voxel 2

Univariate

Multivariate


Overview

From Univariate to Multivariate: Classifier

Voxel 2

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Voxel 1

Voxel 1

Voxel 2

Univariate

Multivariate


Overview

From Univariate to Multivariate: Generalization

Voxel 2

1

2

2

1

1,2

Voxel 1

Voxel 1

Voxel 2

Univariate

Multivariate


Hard margin svm

Hard-Margin SVM


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


Training a svm classifier

Training a SVM Classifier


Training a svm classifier1

Training a SVM Classifier


Training a svm classifier2

Training a SVM Classifier


Testing a trained svm classifier

Testing a Trained SVM Classifier


Testing a trained svm classifier1

Testing a Trained SVM Classifier


Testing a trained svm classifier2

Testing a Trained SVM Classifier


Overview

Real-time detection of the locus of attention using SVM


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