PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Time domain parameters as a feature for EEG-based brain computer interfaces
Carmen Vidaurre, Nicole Krämer, Benjamin Blankertz and Alois Schlögl
Neural Networks Volume 22, pp. 1313-1319, 2009.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6486
Deposited By:Stefan Haufe
Deposited On:26 February 2010