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Virtual Reality in Brain- Computer Interface Research F. Lee 1, R. Scherer 2, H. Bischof 1, G. Pfurtscheller 2 1) Institute for Computer Graphics and Vision.

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Presentation on theme: "Virtual Reality in Brain- Computer Interface Research F. Lee 1, R. Scherer 2, H. Bischof 1, G. Pfurtscheller 2 1) Institute for Computer Graphics and Vision."— Presentation transcript:

1 Virtual Reality in Brain- Computer Interface Research F. Lee 1, R. Scherer 2, H. Bischof 1, G. Pfurtscheller 2 1) Institute for Computer Graphics and Vision 2) Institute for Human-Computer Interfaces

2 Outline Overview of Brain Computer Interface (BCI) Problems in BCI research Studierstube combined with BCI Experiments Further work

3 Brain Computer Interface A Brain-Computer Interface is a communication system that do not depend on peripheral nerves and muscles Applications: Patient with severe movement disorders: Control external devices (neuroprosthesis, wheelchair) by mental focus Entertainment (games) Military Use (fighter pilots)

4 Graz BCI system Graz-BCI system based on classification of motor imagery related brain activity changes in ongoing EEG Electrodes placed on the scalp Movement imagination (left hand, right hand, foot or tongue) Homunculus Primary Somato Sensory Cortex Primary Motor Cortex

5 Motor execution vs. Movement imagination Imagination Execution Subject g3, 16-20 HzSubject f7, 20-24 Hz ERD ERS 500ms time

6 Simplified model of the BCI system

7 Problems in BCI Research Time-consuming training Which kind of movement imagination is better? Impact of visual stimulus on the motor cortex activity and resulting EEG Can lead to deterioration of motor imagery related EEG pattern Correct visual feedback should help user to get more control on the own brain activity

8 Goal Design an accurate BCI system which needs only few training sessions Investigation of different classification methods Presentation of „better“ visual feedback  Studierstube (stereoscopic 3-D scene, tracking)

9 BCI combined with Studierstube

10 Experiments Rotate the cube by mental focus Observation of Movement Feature extraction: adaptive autoregressive parameter Feature mapping: PCA Feedback: rotation, size, color of cube

11 Further work Virtual flat Navigastion: e.g moving into bathroom

12 Thank you for your attention


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