2009 Special Issue: Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces

Authors:
Carmen Vidaurre;Nicole Krämer;Benjamin Blankertz;Alois Schlögl
Affiliations:
Machine Learning Group, Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany and IDA at Fraunhofer FIRST, Kekulestr. 7, 12489 Berlin, Germany;Machine Learning Group, Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany;Machine Learning Group, Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany and IDA at Fraunhofer FIRST, Kekulestr. 7, 12489 Berlin, Germany;Institute for Human-Computer Interfaces, University of Technology Graz, Krengasse 37/I, 8010 Graz, Austria
Venue:
Neural Networks
Year:
2009

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Abstract

Several feature types have been used with EEG-based Brain-Computer Interfaces. Among the most popular are logarithmic band power estimates with more or less subject-specific optimization of the frequency bands. In this paper we introduce a feature called Time Domain Parameter that is defined by the generalization of the Hjorth parameters. Time Domain Parameters are studied under two different conditions. The first setting is defined when no data from a subject is available. In this condition our results show that Time Domain Parameters outperform all band power features tested with all spatial filters applied. The second setting is the transition from calibration (no feedback) to feedback, in which the frequency content of the signals can change for some subjects. We compare Time Domain Parameters with logarithmic band power in subject-specific bands and show that these features are advantageous in this situation as well.