295 related articles for article (PubMed ID: 21682906)
1. Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study.
Holper L; Wolf M
J Neuroeng Rehabil; 2011 Jun; 8():34. PubMed ID: 21682906
[TBL] [Abstract][Full Text] [Related]
2. Testing the potential of a virtual reality neurorehabilitation system during performance of observation, imagery and imitation of motor actions recorded by wireless functional near-infrared spectroscopy (fNIRS).
Holper L; Muehlemann T; Scholkmann F; Eng K; Kiper D; Wolf M
J Neuroeng Rehabil; 2010 Dec; 7():57. PubMed ID: 21122154
[TBL] [Abstract][Full Text] [Related]
3. Understanding inverse oxygenation responses during motor imagery: a functional near-infrared spectroscopy study.
Holper L; Shalóm DE; Wolf M; Sigman M
Eur J Neurosci; 2011 Jun; 33(12):2318-28. PubMed ID: 21631608
[TBL] [Abstract][Full Text] [Related]
4. A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study.
Basso Moro S; Bisconti S; Muthalib M; Spezialetti M; Cutini S; Ferrari M; Placidi G; Quaresima V
Neuroimage; 2014 Jan; 85 Pt 1():451-60. PubMed ID: 23684867
[TBL] [Abstract][Full Text] [Related]
5. Extension of mental preparation positively affects motor imagery as compared to motor execution: a functional near-infrared spectroscopy study.
Holper L; Scholkmann F; Shalóm DE; Wolf M
Cortex; 2012 May; 48(5):593-603. PubMed ID: 21377666
[TBL] [Abstract][Full Text] [Related]
6. Subject-Specific feature selection for near infrared spectroscopy based brain-computer interfaces.
Aydin EA
Comput Methods Programs Biomed; 2020 Oct; 195():105535. PubMed ID: 32534382
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Comparing Features for Classification of MEG Responses to Motor Imagery.
Halme HL; Parkkonen L
PLoS One; 2016; 11(12):e0168766. PubMed ID: 27992574
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Neuronal Activation Detection Using Vector Phase Analysis with Dual Threshold Circles: A Functional Near-Infrared Spectroscopy Study.
Zafar A; Hong KS
Int J Neural Syst; 2018 Dec; 28(10):1850031. PubMed ID: 30045647
[TBL] [Abstract][Full Text] [Related]
11. Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study.
Zimmermann R; Marchal-Crespo L; Edelmann J; Lambercy O; Fluet MC; Riener R; Wolf M; Gassert R
J Neuroeng Rehabil; 2013 Jan; 10():4. PubMed ID: 23336819
[TBL] [Abstract][Full Text] [Related]
12. Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS.
Batula AM; Mark JA; Kim YE; Ayaz H
Comput Intell Neurosci; 2017; 2017():5491296. PubMed ID: 28546809
[TBL] [Abstract][Full Text] [Related]
13. Task complexity relates to activation of cortical motor areas during uni- and bimanual performance: a functional NIRS study.
Holper L; Biallas M; Wolf M
Neuroimage; 2009 Jul; 46(4):1105-13. PubMed ID: 19306929
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Unimanual Versus Bimanual Motor Imagery Classifiers for Assistive and Rehabilitative Brain Computer Interfaces.
Vuckovic A; Pangaro S; Finda P
IEEE Trans Neural Syst Rehabil Eng; 2018 Dec; 26(12):2407-2415. PubMed ID: 30371375
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Effector-independent representations of simple and complex imagined finger movements: a combined fMRI and TMS study.
Kuhtz-Buschbeck JP; Mahnkopf C; Holzknecht C; Siebner H; Ulmer S; Jansen O
Eur J Neurosci; 2003 Dec; 18(12):3375-87. PubMed ID: 14686911
[TBL] [Abstract][Full Text] [Related]
18. Trial-to-trial variability differentiates motor imagery during observation between low versus high responders: a functional near-infrared spectroscopy study.
Holper L; Kobashi N; Kiper D; Scholkmann F; Wolf M; Eng K
Behav Brain Res; 2012 Apr; 229(1):29-40. PubMed ID: 22227507
[TBL] [Abstract][Full Text] [Related]
19. 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; 6(4):046005. PubMed ID: 19556679
[TBL] [Abstract][Full Text] [Related]
20. Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study.
Anwar AR; Muthalib M; Perrey S; Galka A; Granert O; Wolff S; Heute U; Deuschl G; Raethjen J; Muthuraman M
Brain Topogr; 2016 Sep; 29(5):645-60. PubMed ID: 27438589
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]