Online detection of error-related potentials boosts the performance of mental typewriters
1 Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany
2 Artificial Intelligence Laboratory, Department of Informatics, University of Zurich, Switzerland, Andreasstrasse 15, 8050 Zurich, Switzerland
BMC Neuroscience 2012, 13:19 doi:10.1186/1471-2202-13-19Published: 15 February 2012
Increasing the communication speed of brain-computer interfaces (BCIs) is a major aim of current BCI-research. The idea to automatically detect error-related potentials (ErrPs) in order to veto erroneous decisions of a BCI has been existing for more than one decade, but this approach was so far little investigated in online mode.
In our study with eleven participants, an ErrP detection mechanism was implemented in an electroencephalography (EEG) based gaze-independent visual speller.
Single-trial ErrPs were detected with a mean accuracy of 89.1% (AUC 0.90). The spelling speed was increased on average by 49.0% using ErrP detection. The improvement in spelling speed due to error detection was largest for participants with low spelling accuracy.
The performance of BCIs can be increased by using an automatic error detection mechanism. The benefit for patients with motor disorders is potentially high since they often have rather low spelling accuracies compared to healthy people.