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 *

424 related articles for article (PubMed ID: 30215610)

  • 21. The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential.
    Ma T; Li H; Deng L; Yang H; Lv X; Li P; Li F; Zhang R; Liu T; Yao D; Xu P
    J Neural Eng; 2017 Apr; 14(2):026015. PubMed ID: 28145274
    [TBL] [Abstract][Full Text] [Related]  

  • 22. 3D hand motion trajectory prediction from EEG mu and beta bandpower.
    Korik A; Sosnik R; Siddique N; Coyle D
    Prog Brain Res; 2016; 228():71-105. PubMed ID: 27590966
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System.
    Gao Q; Dou L; Belkacem AN; Chen C
    Biomed Res Int; 2017; 2017():8316485. PubMed ID: 28660211
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Deep Channel-Correlation Network for Motor Imagery Decoding From the Same Limb.
    Ma X; Qiu S; Wei W; Wang S; He H
    IEEE Trans Neural Syst Rehabil Eng; 2020 Jan; 28(1):297-306. PubMed ID: 31725383
    [TBL] [Abstract][Full Text] [Related]  

  • 25. A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control.
    Cao L; Li J; Ji H; Jiang C
    J Neurosci Methods; 2014 May; 229():33-43. PubMed ID: 24713576
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials.
    Waytowich N; Lawhern VJ; Garcia JO; Cummings J; Faller J; Sajda P; Vettel JM
    J Neural Eng; 2018 Dec; 15(6):066031. PubMed ID: 30279309
    [TBL] [Abstract][Full Text] [Related]  

  • 27. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces.
    Lawhern VJ; Solon AJ; Waytowich NR; Gordon SM; Hung CP; Lance BJ
    J Neural Eng; 2018 Oct; 15(5):056013. PubMed ID: 29932424
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Enhancing performance of a motor imagery based brain-computer interface by incorporating electrical stimulation-induced SSSEP.
    Yi W; Qiu S; Wang K; Qi H; Zhao X; He F; Zhou P; Yang J; Ming D
    J Neural Eng; 2017 Apr; 14(2):026002. PubMed ID: 28004644
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network.
    Zhang K; Robinson N; Lee SW; Guan C
    Neural Netw; 2021 Apr; 136():1-10. PubMed ID: 33401114
    [TBL] [Abstract][Full Text] [Related]  

  • 30. 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]  

  • 31. A brain-actuated robotic arm system using non-invasive hybrid brain-computer interface and shared control strategy.
    Cao L; Li G; Xu Y; Zhang H; Shu X; Zhang D
    J Neural Eng; 2021 May; 18(4):. PubMed ID: 33862607
    [No Abstract]   [Full Text] [Related]  

  • 32. NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework.
    Cho JH; Jeong JH; Lee SW
    IEEE Trans Cybern; 2022 Dec; 52(12):13279-13292. PubMed ID: 34748509
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network.
    Zhang T; Liu T; Li F; Li M; Liu D; Zhang R; He H; Li P; Gong J; Luo C; Yao D; Xu P
    Neuroimage; 2016 Jul; 134():475-485. PubMed ID: 27103137
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Investigating the effects of visual distractors on the performance of a motor imagery brain-computer interface.
    Emami Z; Chau T
    Clin Neurophysiol; 2018 Jun; 129(6):1268-1275. PubMed ID: 29677690
    [TBL] [Abstract][Full Text] [Related]  

  • 35. A comparison of three brain-computer interfaces based on event-related desynchronization, steady state visual evoked potentials, or a hybrid approach using both signals.
    Brunner C; Allison BZ; Altstätter C; Neuper C
    J Neural Eng; 2011 Apr; 8(2):025010. PubMed ID: 21436538
    [TBL] [Abstract][Full Text] [Related]  

  • 36. An embedded implementation based on adaptive filter bank for brain-computer interface systems.
    Belwafi K; Romain O; Gannouni S; Ghaffari F; Djemal R; Ouni B
    J Neurosci Methods; 2018 Jul; 305():1-16. PubMed ID: 29738806
    [TBL] [Abstract][Full Text] [Related]  

  • 37. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update.
    Lotte F; Bougrain L; Cichocki A; Clerc M; Congedo M; Rakotomamonjy A; Yger F
    J Neural Eng; 2018 Jun; 15(3):031005. PubMed ID: 29488902
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Benefits of deep learning classification of continuous noninvasive brain-computer interface control.
    Stieger JR; Engel SA; Suma D; He B
    J Neural Eng; 2021 Jun; 18(4):. PubMed ID: 34038873
    [No Abstract]   [Full Text] [Related]  

  • 39. A novel Morse code-inspired method for multiclass motor imagery brain-computer interface (BCI) design.
    Jiang J; Zhou Z; Yin E; Yu Y; Liu Y; Hu D
    Comput Biol Med; 2015 Nov; 66():11-9. PubMed ID: 26340647
    [TBL] [Abstract][Full Text] [Related]  

  • 40. MOABB: trustworthy algorithm benchmarking for BCIs.
    Jayaram V; Barachant A
    J Neural Eng; 2018 Dec; 15(6):066011. PubMed ID: 30177583
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 22.