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 *

154 related articles for article (PubMed ID: 25732084)

  • 1. Classification of hemodynamic responses associated with force and speed imagery for a brain-computer interface.
    Yin X; Xu B; Jiang C; Fu Y; Wang Z; Li H; Shi G
    J Med Syst; 2015 May; 39(5):53. PubMed ID: 25732084
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching.
    Yin X; Xu B; Jiang C; Fu Y; Wang Z; Li H; Shi G
    J Neural Eng; 2015 Jun; 12(3):036004. PubMed ID: 25834118
    [TBL] [Abstract][Full Text] [Related]  

  • 3. NIRS-based classification of clench force and speed motor imagery with the use of empirical mode decomposition for BCI.
    Yin X; Xu B; Jiang C; Fu Y; Wang Z; Li H; Shi G
    Med Eng Phys; 2015 Mar; 37(3):280-6. PubMed ID: 25640806
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.
    Chiarelli AM; Croce P; Merla A; Zappasodi F
    J Neural Eng; 2018 Jun; 15(3):036028. PubMed ID: 29446352
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Classification of motor imagery and execution signals with population-level feature sets: implications for probe design in fNIRS based BCI.
    Erdoĝan SB; Özsarfati E; Dilek B; Kadak KS; Hanoĝlu L; Akın A
    J Neural Eng; 2019 Apr; 16(2):026029. PubMed ID: 30634177
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI.
    Hong KS; Naseer N; Kim YH
    Neurosci Lett; 2015 Feb; 587():87-92. PubMed ID: 25529197
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface.
    Naseer N; Hong KS
    Neurosci Lett; 2013 Oct; 553():84-9. PubMed ID: 23973334
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Optimal feature selection from fNIRS signals using genetic algorithms for BCI.
    Noori FM; Naseer N; Qureshi NK; Nazeer H; Khan RA
    Neurosci Lett; 2017 Apr; 647():61-66. PubMed ID: 28336339
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain-computer interface.
    Zhang S; Zheng Y; Wang D; Wang L; Ma J; Zhang J; Xu W; Li D; Zhang D
    Neurosci Lett; 2017 Aug; 655():35-40. PubMed ID: 28663052
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.
    Blokland Y; Spyrou L; Thijssen D; Eijsvogels T; Colier W; Floor-Westerdijk M; Vlek R; Bruhn J; Farquhar J
    IEEE Trans Neural Syst Rehabil Eng; 2014 Mar; 22(2):222-9. PubMed ID: 24608682
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification.
    She Q; Chen K; Ma Y; Nguyen T; Zhang Y
    Comput Intell Neurosci; 2018; 2018():9593682. PubMed ID: 30510569
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG.
    Kaiser V; Bauernfeind G; Kreilinger A; Kaufmann T; Kübler A; Neuper C; Müller-Putz GR
    Neuroimage; 2014 Jan; 85 Pt 1():432-44. PubMed ID: 23651839
    [TBL] [Abstract][Full Text] [Related]  

  • 13. CNN-based classification of fNIRS signals in motor imagery BCI system.
    Ma T; Wang S; Xia Y; Zhu X; Evans J; Sun Y; He S
    J Neural Eng; 2021 Apr; 18(5):. PubMed ID: 33761480
    [No Abstract]   [Full Text] [Related]  

  • 14. An fNIRS-Based Motor Imagery BCI for ALS: A Subject-Specific Data-Driven Approach.
    Hosni SM; Borgheai SB; McLinden J; Shahriari Y
    IEEE Trans Neural Syst Rehabil Eng; 2020 Dec; 28(12):3063-3073. PubMed ID: 33206606
    [TBL] [Abstract][Full Text] [Related]  

  • 15. EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine.
    Gao L; Cheng W; Zhang J; Wang J
    Rev Sci Instrum; 2016 Aug; 87(8):085110. PubMed ID: 27587163
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Hemodynamic responses during standing and sitting activities: a study toward fNIRS-BCI.
    Almulla L; Al-Naib I; Althobaiti M
    Biomed Phys Eng Express; 2020 Jul; 6(5):055005. PubMed ID: 33444236
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis.
    Nazeer H; Naseer N; Khan RA; Noori FM; Qureshi NK; Khan US; Khan MJ
    J Neural Eng; 2020 Oct; 17(5):056025. PubMed ID: 33055382
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Classification of electroencephalogram signals using wavelet-CSP and projection extreme learning machine.
    Dai Y; Zhang X; Chen Z; Xu X
    Rev Sci Instrum; 2018 Jul; 89(7):074302. PubMed ID: 30068128
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Toward Comparison of Cortical Activation with Different Motor Learning Methods Using Event-Related Design: EEG-fNIRS Study.
    Jeong H; Song M; Oh S; Kim J; Kim J
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():6339-6342. PubMed ID: 31947292
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery.
    Koo B; Lee HG; Nam Y; Kang H; Koh CS; Shin HC; Choi S
    J Neurosci Methods; 2015 Apr; 244():26-32. PubMed ID: 24797225
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.