BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

437 related articles for article (PubMed ID: 16317224)

  • 1. Characterization of four-class motor imagery EEG data for the BCI-competition 2005.
    Schlögl A; Lee F; Bischof H; Pfurtscheller G
    J Neural Eng; 2005 Dec; 2(4):L14-22. PubMed ID: 16317224
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces.
    Vidaurre C; Schlögl A; Cabeza R; Scherer R; Pfurtscheller G
    IEEE Trans Biomed Eng; 2007 Mar; 54(3):550-6. PubMed ID: 17355071
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A time-series prediction approach for feature extraction in a brain-computer interface.
    Coyle D; Prasad G; McGinnity TM
    IEEE Trans Neural Syst Rehabil Eng; 2005 Dec; 13(4):461-7. PubMed ID: 16425827
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A fully on-line adaptive BCI.
    Vidaurre C; Schlögl A; Cabeza R; Scherer R; Pfurtscheller G
    IEEE Trans Biomed Eng; 2006 Jun; 53(6):1214-9. PubMed ID: 16761852
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis.
    Faradji F; Ward RK; Birch GE
    J Neurosci Methods; 2009 Jun; 180(2):330-9. PubMed ID: 19439361
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time-frequency tilings.
    Ince NF; Arica S; Tewfik A
    J Neural Eng; 2006 Sep; 3(3):235-44. PubMed ID: 16921207
    [TBL] [Abstract][Full Text] [Related]  

  • 7. An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain-computer interface.
    Wang T; He B
    J Neural Eng; 2004 Mar; 1(1):1-7. PubMed ID: 15876616
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy.
    Kamousi B; Amini AN; He B
    J Neural Eng; 2007 Jun; 4(2):17-25. PubMed ID: 17409476
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification.
    Herman P; Prasad G; McGinnity TM; Coyle D
    IEEE Trans Neural Syst Rehabil Eng; 2008 Aug; 16(4):317-26. PubMed ID: 18701380
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier.
    Boostani R; Moradi MH
    J Neural Eng; 2004 Dec; 1(4):212-7. PubMed ID: 15876641
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Adaptive on-line classification for EEG-based brain computer interfaces with AAR parameters and band power estimates.
    Vidaurre C; Schlögl A; Cabeza R; Scherer R; Pfurtscheller G
    Biomed Tech (Berl); 2005 Nov; 50(11):350-4. PubMed ID: 16370147
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A comparison of common spatial patterns with complex band power features in a four-class BCI experiment.
    Townsend G; Graimann B; Pfurtscheller G
    IEEE Trans Biomed Eng; 2006 Apr; 53(4):642-51. PubMed ID: 16602570
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Multi-channel linear descriptors for event-related EEG collected in brain computer interface.
    Pei XM; Zheng CX; Xu J; Bin GY; Wang HW
    J Neural Eng; 2006 Mar; 3(1):52-8. PubMed ID: 16510942
    [TBL] [Abstract][Full Text] [Related]  

  • 14. The use of EEG modifications due to motor imagery for brain-computer interfaces.
    Cincotti F; Mattia D; Babiloni C; Carducci F; Salinari S; Bianchi L; Marciani MG; Babiloni F
    IEEE Trans Neural Syst Rehabil Eng; 2003 Jun; 11(2):131-3. PubMed ID: 12899254
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design.
    Huan NJ; Palaniappan R
    J Neural Eng; 2004 Sep; 1(3):142-50. PubMed ID: 15876633
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Different classification techniques considering brain computer interface applications.
    Rezaei S; Tavakolian K; Nasrabadi AM; Setarehdan SK
    J Neural Eng; 2006 Jun; 3(2):139-44. PubMed ID: 16705270
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An empirical bayesian framework for brain-computer interfaces.
    Lei X; Yang P; Yao D
    IEEE Trans Neural Syst Rehabil Eng; 2009 Dec; 17(6):521-9. PubMed ID: 19622442
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Continuous EEG classification during motor imagery--simulation of an asynchronous BCI.
    Townsend G; Graimann B; Pfurtscheller G
    IEEE Trans Neural Syst Rehabil Eng; 2004 Jun; 12(2):258-65. PubMed ID: 15218939
    [TBL] [Abstract][Full Text] [Related]  

  • 19. BCI competition III: dataset II- ensemble of SVMs for BCI P300 speller.
    Rakotomamonjy A; Guigue V
    IEEE Trans Biomed Eng; 2008 Mar; 55(3):1147-54. PubMed ID: 18334407
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Studying the use of fuzzy inference systems for motor imagery classification.
    Fabien L; Anatole L; Fabrice L; Bruno A
    IEEE Trans Neural Syst Rehabil Eng; 2007 Jun; 15(2):322-4. PubMed ID: 17601202
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 22.