Combined optimization of spatial and temporal filters for improving brain-computer interfacing

Guido Dornhege, Benjamin Blankertz, Matthias Krauledat, Florian Losch, Gabriel Curio, Klaus Muller

Research output: Contribution to journalArticle

228 Citations (Scopus)

Abstract

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.

Original languageEnglish
Pages (from-to)2274-2281
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Volume53
Issue number11
DOIs
Publication statusPublished - 2006 Nov 1
Externally publishedYes

Fingerprint

Brain-Computer Interfaces
Brain
Brain computer interface
Communication
Efferent Pathways
Information Storage and Retrieval
Computer Systems
Electroencephalography
Muscle
Technology
Equipment and Supplies
Muscles
Experiments

Keywords

  • Brain-computer interface
  • Common spatial patterns
  • EEG
  • Event-related desynchronization
  • Single-trial-analysis

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Combined optimization of spatial and temporal filters for improving brain-computer interfacing. / Dornhege, Guido; Blankertz, Benjamin; Krauledat, Matthias; Losch, Florian; Curio, Gabriel; Muller, Klaus.

In: IEEE Transactions on Biomedical Engineering, Vol. 53, No. 11, 01.11.2006, p. 2274-2281.

Research output: Contribution to journalArticle

Dornhege, Guido ; Blankertz, Benjamin ; Krauledat, Matthias ; Losch, Florian ; Curio, Gabriel ; Muller, Klaus. / Combined optimization of spatial and temporal filters for improving brain-computer interfacing. In: IEEE Transactions on Biomedical Engineering. 2006 ; Vol. 53, No. 11. pp. 2274-2281.
@article{4936b731e96b4aae9d465b2586647613,
title = "Combined optimization of spatial and temporal filters for improving brain-computer interfacing",
abstract = "Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11{\%}. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.",
keywords = "Brain-computer interface, Common spatial patterns, EEG, Event-related desynchronization, Single-trial-analysis",
author = "Guido Dornhege and Benjamin Blankertz and Matthias Krauledat and Florian Losch and Gabriel Curio and Klaus Muller",
year = "2006",
month = "11",
day = "1",
doi = "10.1109/TBME.2006.883649",
language = "English",
volume = "53",
pages = "2274--2281",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "IEEE Computer Society",
number = "11",

}

TY - JOUR

T1 - Combined optimization of spatial and temporal filters for improving brain-computer interfacing

AU - Dornhege, Guido

AU - Blankertz, Benjamin

AU - Krauledat, Matthias

AU - Losch, Florian

AU - Curio, Gabriel

AU - Muller, Klaus

PY - 2006/11/1

Y1 - 2006/11/1

N2 - Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.

AB - Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.

KW - Brain-computer interface

KW - Common spatial patterns

KW - EEG

KW - Event-related desynchronization

KW - Single-trial-analysis

UR - http://www.scopus.com/inward/record.url?scp=33746833141&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33746833141&partnerID=8YFLogxK

U2 - 10.1109/TBME.2006.883649

DO - 10.1109/TBME.2006.883649

M3 - Article

C2 - 17073333

AN - SCOPUS:33746833141

VL - 53

SP - 2274

EP - 2281

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 11

ER -