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 *

143 related articles for article (PubMed ID: 33637029)

  • 1. Detection of the Intention of Direction Changes During Gait Through EEG Signals.
    Soriano-Segura P; Iáñez E; Ortiz M; Quiles V; Azorín JM
    Int J Neural Syst; 2021 Nov; 31(11):2150015. PubMed ID: 33637029
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of gait intention from pre-movement EEG signals: a feasibility study.
    Shafiul Hasan SM; Siddiquee MR; Atri R; Ramon R; Marquez JS; Bai O
    J Neuroeng Rehabil; 2020 Apr; 17(1):50. PubMed ID: 32299460
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Decoding Brain Signals to Classify Gait Direction Anticipation.
    Vaghei Y; Park EJ; Arzanpour S
    Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():309-312. PubMed ID: 36086221
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A penalized time-frequency band feature selection and classification procedure for improved motor intention decoding in multichannel EEG.
    Peterson V; Wyser D; Lambercy O; Spies R; Gassert R
    J Neural Eng; 2019 Feb; 16(1):016019. PubMed ID: 30623892
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography.
    Wairagkar M; Hayashi Y; Nasuto SJ
    PLoS One; 2018; 13(3):e0193722. PubMed ID: 29509785
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Rhythmic temporal prediction enhances neural representations of movement intention for brain-computer interface.
    Meng J; Zhao Y; Wang K; Sun J; Yi W; Xu F; Xu M; Ming D
    J Neural Eng; 2023 Nov; 20(6):. PubMed ID: 37875107
    [No Abstract]   [Full Text] [Related]  

  • 7. Evaluating classifiers to detect arm movement intention from EEG signals.
    Planelles D; Hortal E; Costa A; Ubeda A; Iáez E; Azorín JM
    Sensors (Basel); 2014 Sep; 14(10):18172-86. PubMed ID: 25268915
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Selection of Spatial, Temporal and Frequency Features to Detect Direction Changes During Gait.
    Soriano-Segura P; Ianez E; Quiles V; Ferrero L; Ortiz M; Azorin JM
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():3835-3838. PubMed ID: 33018837
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Enhance decoding of pre-movement EEG patterns for brain-computer interfaces.
    Wang K; Xu M; Wang Y; Zhang S; Chen L; Ming D
    J Neural Eng; 2020 Jan; 17(1):016033. PubMed ID: 31747642
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Preparatory movement state enhances premovement EEG representations for brain-computer interfaces.
    Zhang Y; Li M; Wang H; Zhang M; Xu G
    J Neural Eng; 2024 Jun; 21(3):. PubMed ID: 38806037
    [No Abstract]   [Full Text] [Related]  

  • 11. Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects.
    Sburlea AI; Montesano L; Minguez J
    J Neural Eng; 2017 Jun; 14(3):036004. PubMed ID: 28291737
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Brain-Computer Interface Channel-Selection Strategy Based on Analysis of Event-Related Desynchronization Topography in Stroke Patients.
    Li C; Jia T; Xu Q; Ji L; Pan Y
    J Healthc Eng; 2019; 2019():3817124. PubMed ID: 31559004
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Hybrid Brain-Computer Interface (BCI) based on the EEG and EOG signals.
    Jiang J; Zhou Z; Yin E; Yu Y; Hu D
    Biomed Mater Eng; 2014; 24(6):2919-25. PubMed ID: 25226998
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A brain-computer interface for single-trial detection of gait initiation from movement related cortical potentials.
    Jiang N; Gizzi L; Mrachacz-Kersting N; Dremstrup K; Farina D
    Clin Neurophysiol; 2015 Jan; 126(1):154-9. PubMed ID: 24910150
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials.
    Shakeel A; Navid MS; Anwar MN; Mazhar S; Jochumsen M; Niazi IK
    Comput Math Methods Med; 2015; 2015():346217. PubMed ID: 26881008
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual reality avatar.
    Luu TP; He Y; Brown S; Nakagame S; Contreras-Vidal JL
    J Neural Eng; 2016 Jun; 13(3):036006. PubMed ID: 27064824
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Online EEG Classification of Covert Speech for Brain-Computer Interfacing.
    Sereshkeh AR; Trott R; Bricout A; Chau T
    Int J Neural Syst; 2017 Dec; 27(8):1750033. PubMed ID: 28830308
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals.
    Pereira J; Kobler R; Ofner P; Schwarz A; Müller-Putz GR
    J Neural Eng; 2021 Jul; 18(4):. PubMed ID: 34130267
    [TBL] [Abstract][Full Text] [Related]  

  • 19. EEG-Based Detection of Starting and Stopping During Gait Cycle.
    Hortal E; Úbeda A; Iáñez E; Azorín JM; Fernández E
    Int J Neural Syst; 2016 Nov; 26(7):1650029. PubMed ID: 27354191
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement.
    Wairagkar M; Hayashi Y; Nasuto SJ
    Front Syst Neurosci; 2019; 13():66. PubMed ID: 31787885
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.