Walking by Thinking: The Brainwaves Are Crucial, Not the Muscles!

Author:

Leeb Robert,Keinrath Claudia1,Friedman Doron2,Guger Christoph3,Scherer Reinhold1,Neuper Christa4,Garau Maia,Antley Angus,Steed Anthony2,Slater Mel5,Pfurtscheller Gert1

Affiliation:

1. Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria.

2. Department of Computer Science, University College London, London, UK.

3. g.tec—Guger Technologies, OEG Graz, Austria.

4. Department of Psychology, University of Graz, Graz, Austria.

5. Department of Computer Science, University College London, London, UK and ICREA, Universitat Politècnica de Catalunya, Barcelona, Spain.

Abstract

Healthy participants are able to move forward within a virtual environment (VE) by the imagination of foot movement. This is achieved by using a brain-computer interface (BCI) that transforms thought-modulated electroencephalogram (EEG) recordings into a control signal. A BCI establishes a communication channel between the human brain and the computer. The basic principle of the Graz-BCI is the detection and classification of motor-imagery-related EEG patterns, whereby the dynamics of sensorimotor rhythms are analyzed. A BCI is a closed-loop system and information is visually fed back to the user about the success or failure of an intended movement imagination. Feedback can be realized in different ways, from a simple moving bar graph to navigation in VEs. The goals of this work are twofold: first, to show the influence of different feedback types on the same task, and second, to demonstrate that it is possible to move through a VE (e.g., a virtual street) without any muscular activity, using only the imagination of foot movement. In the presented work, data from BCI feedback displayed on a conventional monitor are compared with data from BCI feedback in VE experiments with a head-mounted display (HMD) and in a high immersive projection environment (Cave). Results of three participants are reported to demonstrate the proof-of-concept. The data indicate that the type of feedback has an influence on the task performance, but not on the BCI classification accuracy. The participants achieved their best performances viewing feedback in the Cave. Furthermore the VE feedback provided motivation for the subjects.

Publisher

MIT Press - Journals

Subject

Computer Vision and Pattern Recognition,Human-Computer Interaction,Control and Systems Engineering,Software

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