Non-invasive Brain-Computer Interfaces: Enhanced Gaming and Robotic Control

Lecture notes in computer sciencePublished 1 January 2011Open access

Reinhold Scherer, Elisabeth V. C. Friedrich, Brendan Z. Allison, Markus Pröll, Mike Chung, Willy Cheung

Citations28

TL;DR

An enhancement of the 3-class self-paced Graz-BCI is presented that allows interaction with the massive multiplayer online role playing game World of Warcraft and a scalable, adaptive learning framework is described, which allows users to teach the BCI new skills on-the-fly.

Abstract

The performance of non-invasive electroencephalogram-based (EEG) brain-computer interfacing (BCI) has improved significantly in recent years. However, remaining challenges include the non-stationarity and the low signal-to-noise ratio (SNR) of the EEG, which limit the bandwidth and hence the available applications. In this paper, we review ongoing research in our labs and introduce novel concepts and applications. First, we present an enhancement of the 3-class self-paced Graz-BCI that allows interaction with the massive multiplayer online role playing game World of Warcraft. Second, we report on the long-term stability and robustness of detection of oscillatory components modulated by distinct mental tasks. Third, we describe a scalable, adaptive learning framework, which allows users to teach the BCI new skills on-the-fly. Using this hierarchical BCI, we successfully train and control a humanoid robot in a virtual home environment.

Keywords

NeuroscienceEngineering

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