Steady-state movement related potentials for brain-computer interfacing

Kianoush Nazarpour, Peter Praamstra, R Chris Miall, Saeid Sanei

Research output: Contribution to journalArticlepeer-review

Abstract

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible.

Original languageEnglish
Pages (from-to)2104-13
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume56
Issue number8
Early online date28 Apr 2009
DOIs
Publication statusPublished - 1 Aug 2009

Keywords

  • Brain Mapping
  • Electroencephalography
  • Evoked Potentials, Visual/physiology
  • Female
  • Fingers/physiology
  • Humans
  • Male
  • Movement/physiology
  • Pattern Recognition, Automated/methods
  • User-Computer Interface

Fingerprint

Dive into the research topics of 'Steady-state movement related potentials for brain-computer interfacing'. Together they form a unique fingerprint.

Cite this