Bio-inspired Systems

Engineering Applications of Bio-Inspired Artificial Neural Networks

Volume 1607 of the series Lecture Notes in Computer Science pp 248-254

Date:

EEG-based brain-computer interface using subject-specific spatial filters

  • G. PfurtschellerAffiliated withDepartment of Medical Informatics, Institute of Biomedical Engng. and Ludwig Boltzmann Institute of Medical Informatics and Neuroinformatics, University of Technology, Graz
  • , C. GugerAffiliated withDepartment of Medical Informatics, Institute of Biomedical Engng. and Ludwig Boltzmann Institute of Medical Informatics and Neuroinformatics, University of Technology, Graz
  • , H. RamoserAffiliated withDepartment of Medical Informatics, Institute of Biomedical Engng. and Ludwig Boltzmann Institute of Medical Informatics and Neuroinformatics, University of Technology, Graz

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Sensorimotor EEG rhythms are affected by motor imagery and can, therefore, be used as input signals for an EEG-based brain-computer interface (BCI). Satisfactory classification rates of imagery-related EEG patterns can be activated when multiple EEG recordings and the method of common spatial patterns is used for parameter estimation. Data from 3 BCI experiments with and without feedback are reported.

Key words

Brain-Computer Interface (BCI) single-trial EEG classification common spatial filter motor imagery event-related desynchronization