BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

222 related articles for article (PubMed ID: 23372028)

  • 21. Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines.
    Singla R; Khosla A; Jha R
    J Med Eng Technol; 2014 Apr; 38(3):125-34. PubMed ID: 24533888
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Enhanced mu rhythm extraction using blind source separation and wavelet transform.
    Ng SC; Raveendran P
    IEEE Trans Biomed Eng; 2009 Aug; 56(8):2024-34. PubMed ID: 19457744
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Probability mapping based artifact detection and removal from single-channel EEG signals for brain-computer interface applications.
    Islam MK; Ghorbanzadeh P; Rastegarnia A
    J Neurosci Methods; 2021 Aug; 360():109249. PubMed ID: 34139268
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, Kurtosis, and wavelet-ICA.
    Mahajan R; Morshed BI
    IEEE J Biomed Health Inform; 2015 Jan; 19(1):158-65. PubMed ID: 24968340
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.
    Bascil MS; Tesneli AY; Temurtas F
    Australas Phys Eng Sci Med; 2016 Sep; 39(3):665-76. PubMed ID: 27376723
    [TBL] [Abstract][Full Text] [Related]  

  • 26. [Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis].
    Song H; Xu S; Liu G; Liu J; Xiong P
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2022 Dec; 39(6):1074-1081. PubMed ID: 36575075
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.
    Zhang C; Tong L; Zeng Y; Jiang J; Bu H; Yan B; Li J
    Biomed Res Int; 2015; 2015():720450. PubMed ID: 26380294
    [TBL] [Abstract][Full Text] [Related]  

  • 28. sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.
    Jrad N; Congedo M; Phlypo R; Rousseau S; Flamary R; Yger F; Rakotomamonjy A
    J Neural Eng; 2011 Oct; 8(5):056004. PubMed ID: 21817778
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Single-trial motor imagery classification using asymmetry ratio, phase relation, wavelet-based fractal, and their selected combination.
    Hsu WY
    Int J Neural Syst; 2013 Apr; 23(2):1350007. PubMed ID: 23578057
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Detection and classification of subject-generated artifacts in EEG signals using autoregressive models.
    Lawhern V; Hairston WD; McDowell K; Westerfield M; Robbins K
    J Neurosci Methods; 2012 Jul; 208(2):181-9. PubMed ID: 22634706
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG.
    Acharyya A; Jadhav PN; Bono V; Maharatna K; Naik GR
    Comput Methods Programs Biomed; 2018 May; 158():123-133. PubMed ID: 29544778
    [TBL] [Abstract][Full Text] [Related]  

  • 32. New KF-PP-SVM classification method for EEG in brain-computer interfaces.
    Yang B; Han Z; Zan P; Wang Q
    Biomed Mater Eng; 2014; 24(6):3665-73. PubMed ID: 25227081
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Robust classification of motor imagery EEG signals using statistical time-domain features.
    Khorshidtalab A; Salami MJ; Hamedi M
    Physiol Meas; 2013 Nov; 34(11):1563-79. PubMed ID: 24152422
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Fuzzy support vector machine for classification of EEG signals using wavelet-based features.
    Xu Q; Zhou H; Wang Y; Huang J
    Med Eng Phys; 2009 Sep; 31(7):858-65. PubMed ID: 19487151
    [TBL] [Abstract][Full Text] [Related]  

  • 35. [Pretreatment Research Based on Left and Right Hand Motor Imagery for Single-channel Electroencephalogram].
    Li S; Fu Y; Yang Q; Liu C; Wun H
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2016 Oct; 33(5):862-6. PubMed ID: 29714933
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Automatic removal of various artifacts from EEG signals using combined methods.
    Gao J; Yang Y; Sun J; Yu G
    J Clin Neurophysiol; 2010 Oct; 27(5):312-20. PubMed ID: 20844440
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms.
    Liu R; Zhang Z; Duan F; Zhou X; Meng Z
    Comput Intell Neurosci; 2017; 2017():2727856. PubMed ID: 28874909
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.
    Yedukondalu J; Sharma LD
    Sensors (Basel); 2023 Jan; 23(3):. PubMed ID: 36772275
    [No Abstract]   [Full Text] [Related]  

  • 39. SNOAR: a new regression approach for the removal of ocular artifact from multi-channel electroencephalogram signals.
    Juyal R; Muthusamy H; Kumar N
    Med Biol Eng Comput; 2022 Dec; 60(12):3567-3583. PubMed ID: 36245020
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Automatic artefact removal in a self-paced hybrid brain- computer interface system.
    Yong X; Fatourechi M; Ward RK; Birch GE
    J Neuroeng Rehabil; 2012 Jul; 9():50. PubMed ID: 22838499
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 12.