title: Spatio-spectral filters for improving the classification of single trial EEG.
abstract: Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.
authors.affiliation: Department of Intelligent Data Analysis, FIRST Fraunhofer Institute, Berlin, Germany. steven.lemm@first.fhg.de
authors.forename: Steven
authors.initials: S
authors.lastname: Lemm
authors.forename: Benjamin
authors.initials: B
authors.lastname: Blankertz
authors.forename: Gabriel
authors.initials: G
authors.lastname: Curio
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D001921:Brain / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface