PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Berlin Brain-Computer Interface: EEG-based communication without subject training
Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Klaus-Robert Müller, Volker Kunzmann, Florian Losch and Gabriel Curio
IEEE Trans. Neural Sys. Rehab. Eng. Volume 14, Number 2, pp. 147-152, 2006.

Abstract

The Berlin Brain-Computer Interface (BBCI) project develops a non-invasive BCI system whose key features are (1) the use of well-established motor competences as control paradigms, (2) high-dimensional features from 128-channel EEG and (3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left vs. right hand movements in healthy subjects. A more recent study showed that the RP similarily accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach oscillatory features are used to discriminate imagined movements (left hand vs. right hand vs. foot). In a recent feedback study with 6 healthy subjects with no or very little experience with BCI control, 3 subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:2254
Deposited By:Benjamin Blankertz
Deposited On:11 October 2006