Literature DB >> 17071244

Motor imagery and EEG-based control of spelling devices and neuroprostheses.

Christa Neuper1, Gernot R Müller-Putz, Reinhold Scherer, Gert Pfurtscheller.   

Abstract

A brain-computer interface (BCI) transforms signals originating from the human brain into commands that can control devices or applications. With this, a BCI provides a new non-muscular communication channel, which can be used to assist patients who have highly compromised motor functions. The Graz-BCI uses motor imagery and associated oscillatory EEG signals from the sensorimotor cortex for device control. As a result of research in the past 15 years, the classification of ERD/ERS patterns in single EEG trials during motor execution and motor imagery forms the basis of this sensorimotor-rhythm controlled BCI. The major frequency bands of cortical oscillations considered here are the 8-13 and 15-30 Hz bands. This chapter describes the basic methods used in Graz-BCI research and outlines possible clinical applications.

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Year:  2006        PMID: 17071244     DOI: 10.1016/S0079-6123(06)59025-9

Source DB:  PubMed          Journal:  Prog Brain Res        ISSN: 0079-6123            Impact factor:   2.453


  31 in total

1.  Seeing touch and pain in a stranger modulates the cortical responses elicited by somatosensory but not auditory stimulation.

Authors:  Elia Valentini; Meng Liang; Salvatore Maria Aglioti; Gian Domenico Iannetti
Journal:  Hum Brain Mapp       Date:  2012-01-10       Impact factor: 5.038

2.  Error potential detection during continuous movement of an artificial arm controlled by brain-computer interface.

Authors:  Alex Kreilinger; Christa Neuper; Gernot R Müller-Putz
Journal:  Med Biol Eng Comput       Date:  2012-01-01       Impact factor: 2.602

Review 3.  Brain-computer interfaces in medicine.

Authors:  Jerry J Shih; Dean J Krusienski; Jonathan R Wolpaw
Journal:  Mayo Clin Proc       Date:  2012-02-10       Impact factor: 7.616

4.  Joint spatial-spectral feature space clustering for speech activity detection from ECoG signals.

Authors:  Vasileios G Kanas; Iosif Mporas; Heather L Benz; Kyriakos N Sgarbas; Anastasios Bezerianos; Nathan E Crone
Journal:  IEEE Trans Biomed Eng       Date:  2014-04       Impact factor: 4.538

5.  Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training.

Authors:  Christian Enzinger; Stefan Ropele; Franz Fazekas; Marisa Loitfelder; Faton Gorani; Thomas Seifert; Gudrun Reiter; Christa Neuper; Gert Pfurtscheller; Gernot Müller-Putz
Journal:  Exp Brain Res       Date:  2008-07-01       Impact factor: 1.972

6.  Cortical and subcortical mechanisms of brain-machine interfaces.

Authors:  Silvia Marchesotti; Roberto Martuzzi; Aaron Schurger; Maria Laura Blefari; José R Del Millán; Hannes Bleuler; Olaf Blanke
Journal:  Hum Brain Mapp       Date:  2017-03-21       Impact factor: 5.038

7.  Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects.

Authors:  Joseph N Mak; Jonathan R Wolpaw
Journal:  IEEE Rev Biomed Eng       Date:  2009

8.  Novel hold-release functionality in a P300 brain-computer interface.

Authors:  R E Alcaide-Aguirre; J E Huggins
Journal:  J Neural Eng       Date:  2014-11-07       Impact factor: 5.379

9.  Automated classification of fMRI data employing trial-based imagery tasks.

Authors:  Jong-Hwan Lee; Matthew Marzelli; Ferenc A Jolesz; Seung-Schik Yoo
Journal:  Med Image Anal       Date:  2009-01-16       Impact factor: 8.545

10.  A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People.

Authors:  Giuseppe Placidi; Andrea Petracca; Matteo Spezialetti; Daniela Iacoviello
Journal:  J Med Syst       Date:  2015-11-14       Impact factor: 4.460

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