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A. Schlogl, “A new linear classification method for an EEG-based brain-computer interface,” unpublished.

has been cited by the following article:

• TITLE: Pattern Recognition of Motor Imagery EEG using Wavelet Transform

  AUTHORS: Baoguo Xu, Aiguo Song

  KEYWORDS: Brain-computer interface (BCI); Motor imagery; Wavelet Coefficients; Autoregressive Model

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.1 No.1, June 6, 2008

  ABSTRACT: Brain-computer interface (BCI) provides new communication and control channels that do not depend on the brain’s normal output of peripheral nerves and muscles. In this paper, we report on results of developing a single trial online motor imagery feature extraction method for BCI. The wavelet coefficients and autoregressive parameter model was used to extraction the features from the motor imagery EEG and the linear discriminant analysis based on mahalanobis distance was utilized to classify the pattern of left and right hand movement imagery. The performance was tested by the Graz dataset for BCI competition 2003 and satisfactory results are obtained with an error rate as low as 10.0%.