These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

166 related articles for article (PubMed ID: 25830903)

  • 1. Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.
    Steyrl D; Scherer R; Faller J; Müller-Putz GR
    Biomed Tech (Berl); 2016 Feb; 61(1):77-86. PubMed ID: 25830903
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces.
    Vidaurre C; Schlögl A
    Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():173-6. PubMed ID: 19162621
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Pre-stimulus sensorimotor rhythms influence brain-computer interface classification performance.
    Maeder CL; Sannelli C; Haufe S; Blankertz B
    IEEE Trans Neural Syst Rehabil Eng; 2012 Sep; 20(5):653-62. PubMed ID: 22801528
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. A novel deep learning approach for classification of EEG motor imagery signals.
    Tabar YR; Halici U
    J Neural Eng; 2017 Feb; 14(1):016003. PubMed ID: 27900952
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain-computer interface.
    Siuly S; Li Y
    IEEE Trans Neural Syst Rehabil Eng; 2012 Jul; 20(4):526-38. PubMed ID: 22287252
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Neurophysiological predictors and spectro-spatial discriminative features for enhancing SMR-BCI.
    Robinson N; Thomas KP; Vinod AP
    J Neural Eng; 2018 Dec; 15(6):066032. PubMed ID: 30277219
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces.
    Bashashati H; Ward RK; Birch GE; Bashashati A
    PLoS One; 2015; 10(6):e0129435. PubMed ID: 26090799
    [TBL] [Abstract][Full Text] [Related]  

  • 10. An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter.
    Zhang D; Huang B; Wu W; Li S
    Int J Neural Syst; 2015 Nov; 25(7):1550030. PubMed ID: 26246229
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.
    Lu J; Xie K; McFarland DJ
    IEEE Trans Neural Syst Rehabil Eng; 2014 Jul; 22(4):847-57. PubMed ID: 24723632
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery.
    Toppi J; Risetti M; Quitadamo LR; Petti M; Bianchi L; Salinari S; Babiloni F; Cincotti F; Mattia D; Astolfi L
    J Neural Eng; 2014 Jun; 11(3):035010. PubMed ID: 24835634
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Generalized features for electrocorticographic BCIs.
    Shenoy P; Miller KJ; Ojemann JG; Rao RP
    IEEE Trans Biomed Eng; 2008 Jan; 55(1):273-80. PubMed ID: 18232371
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Conditional random fields as classifiers for three-class motor-imagery brain-computer interfaces.
    Hasan BA; Gan JQ
    J Neural Eng; 2011 Apr; 8(2):025013. PubMed ID: 21436518
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Embedded grey relation theory in Hopfield neural network: application to motor imagery EEG recognition.
    Hsu WY
    Clin EEG Neurosci; 2013 Oct; 44(4):257-64. PubMed ID: 23536381
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Dynamically weighted ensemble classification for non-stationary EEG processing.
    Liyanage SR; Guan C; Zhang H; Ang KK; Xu J; Lee TH
    J Neural Eng; 2013 Jun; 10(3):036007. PubMed ID: 23574821
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Application of support vector machines to reliability-based automatic repeat request for Brain-Computer Interfaces.
    Takahashi H; Yoshikawa T; Furuhashi T
    Annu Int Conf IEEE Eng Med Biol Soc; 2009; 2009():6457-60. PubMed ID: 19964432
    [TBL] [Abstract][Full Text] [Related]  

  • 19. On the control of brain-computer interfaces by users with cerebral palsy.
    Daly I; Billinger M; Laparra-Hernández J; Aloise F; García ML; Faller J; Scherer R; Müller-Putz G
    Clin Neurophysiol; 2013 Sep; 124(9):1787-97. PubMed ID: 23684128
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A novel Bayesian framework for discriminative feature extraction in Brain-Computer Interfaces.
    Suk HI; Lee SW
    IEEE Trans Pattern Anal Mach Intell; 2013 Feb; 35(2):286-99. PubMed ID: 22431526
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.