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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

1049 related items for PubMed ID: 24401829

  • 1. Effect of real-time cortical feedback in motor imagery-based mental practice training.
    Bai O, Huang D, Fei DY, Kunz R.
    NeuroRehabilitation; 2014; 34(2):355-63. PubMed ID: 24401829
    [Abstract] [Full Text] [Related]

  • 2. Event-related desynchronization reflects downregulation of intracortical inhibition in human primary motor cortex.
    Takemi M, Masakado Y, Liu M, Ushiba J.
    J Neurophysiol; 2013 Sep; 110(5):1158-66. PubMed ID: 23761697
    [Abstract] [Full Text] [Related]

  • 3. Muscle-selective disinhibition of corticomotor representations using a motor imagery-based brain-computer interface.
    Takemi M, Maeda T, Masakado Y, Siebner HR, Ushiba J.
    Neuroimage; 2018 Dec; 183():597-605. PubMed ID: 30172003
    [Abstract] [Full Text] [Related]

  • 4. Brain oscillatory signatures of motor tasks.
    Ramos-Murguialday A, Birbaumer N.
    J Neurophysiol; 2015 Jun 01; 113(10):3663-82. PubMed ID: 25810484
    [Abstract] [Full Text] [Related]

  • 5. Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface.
    Neuper C, Scherer R, Wriessnegger S, Pfurtscheller G.
    Clin Neurophysiol; 2009 Feb 01; 120(2):239-47. PubMed ID: 19121977
    [Abstract] [Full Text] [Related]

  • 6. Assessing motor imagery in brain-computer interface training: Psychological and neurophysiological correlates.
    Vasilyev A, Liburkina S, Yakovlev L, Perepelkina O, Kaplan A.
    Neuropsychologia; 2017 Mar 01; 97():56-65. PubMed ID: 28167121
    [Abstract] [Full Text] [Related]

  • 7. A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior.
    Bai O, Lin P, Vorbach S, Floeter MK, Hattori N, Hallett M.
    J Neural Eng; 2008 Mar 01; 5(1):24-35. PubMed ID: 18310808
    [Abstract] [Full Text] [Related]

  • 8. Ipsilateral EEG mu rhythm reflects the excitability of uncrossed pathways projecting to shoulder muscles.
    Hasegawa K, Kasuga S, Takasaki K, Mizuno K, Liu M, Ushiba J.
    J Neuroeng Rehabil; 2017 Aug 25; 14(1):85. PubMed ID: 28841920
    [Abstract] [Full Text] [Related]

  • 9. EEG-based classification of imaginary left and right foot movements using beta rebound.
    Hashimoto Y, Ushiba J.
    Clin Neurophysiol; 2013 Nov 25; 124(11):2153-60. PubMed ID: 23757379
    [Abstract] [Full Text] [Related]

  • 10. Mental practice with motor imagery: evidence for motor recovery and cortical reorganization after stroke.
    Butler AJ, Page SJ.
    Arch Phys Med Rehabil; 2006 Dec 25; 87(12 Suppl 2):S2-11. PubMed ID: 17140874
    [Abstract] [Full Text] [Related]

  • 11. Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study.
    Zich C, Debener S, De Vos M, Frerichs S, Maurer S, Kranczioch C.
    Neuroimage; 2015 Aug 01; 116():80-91. PubMed ID: 25979668
    [Abstract] [Full Text] [Related]

  • 12. Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control.
    Huang D, Lin P, Fei DY, Chen X, Bai O.
    J Neural Eng; 2009 Aug 01; 6(4):046005. PubMed ID: 19556679
    [Abstract] [Full Text] [Related]

  • 13. A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface.
    Ang KK, Guan C, Chua KS, Ang BT, Kuah CW, Wang C, Phua KS, Chin ZY, Zhang H.
    Clin EEG Neurosci; 2011 Oct 01; 42(4):253-8. PubMed ID: 22208123
    [Abstract] [Full Text] [Related]

  • 14. On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up.
    Onose G, Grozea C, Anghelescu A, Daia C, Sinescu CJ, Ciurea AV, Spircu T, Mirea A, Andone I, Spânu A, Popescu C, Mihăescu AS, Fazli S, Danóczy M, Popescu F.
    Spinal Cord; 2012 Aug 01; 50(8):599-608. PubMed ID: 22410845
    [Abstract] [Full Text] [Related]

  • 15. Neurofeedback-based motor imagery training for brain-computer interface (BCI).
    Hwang HJ, Kwon K, Im CH.
    J Neurosci Methods; 2009 Apr 30; 179(1):150-6. PubMed ID: 19428521
    [Abstract] [Full Text] [Related]

  • 16. Precise estimation of human corticospinal excitability associated with the levels of motor imagery-related EEG desynchronization extracted by a locked-in amplifier algorithm.
    Takahashi K, Kato K, Mizuguchi N, Ushiba J.
    J Neuroeng Rehabil; 2018 Nov 01; 15(1):93. PubMed ID: 30384845
    [Abstract] [Full Text] [Related]

  • 17. Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients.
    Bai O, Lin P, Huang D, Fei DY, Floeter MK.
    Clin Neurophysiol; 2010 Aug 01; 121(8):1293-303. PubMed ID: 20347612
    [Abstract] [Full Text] [Related]

  • 18. Performance of motor imagery brain-computer interface based on anodal transcranial direct current stimulation modulation.
    Wei P, He W, Zhou Y, Wang L.
    IEEE Trans Neural Syst Rehabil Eng; 2013 May 01; 21(3):404-15. PubMed ID: 23475381
    [Abstract] [Full Text] [Related]

  • 19. Improving motor imagery through a mirror box for BCI users.
    Gómez DMC, Braidot AAA.
    J Neurophysiol; 2024 May 01; 131(5):832-841. PubMed ID: 38323330
    [Abstract] [Full Text] [Related]

  • 20. The comparison of motor learning performance with and without feedback.
    Orand A, Ushiba J, Tomita Y, Honda S.
    Somatosens Mot Res; 2012 May 01; 29(3):103-10. PubMed ID: 22746218
    [Abstract] [Full Text] [Related]

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