Non-invasive Brain-Computer Interfaces: Enhanced Gaming and Robotic Control
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.
KeywordsLinear Discriminant Analysis Motor Imagery Humanoid Robot Common Spatial Pattern Radial Basis Function Model
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