161 related articles for article (PubMed ID: 21436524)
21. Co-adaptive calibration to improve BCI efficiency.
Vidaurre C; Sannelli C; Müller KR; Blankertz B
J Neural Eng; 2011 Apr; 8(2):025009. PubMed ID: 21436515
[TBL] [Abstract][Full Text] [Related]
22. Bispectrum-based feature extraction technique for devising a practical brain-computer interface.
Shahid S; Prasad G
J Neural Eng; 2011 Apr; 8(2):025014. PubMed ID: 21436530
[TBL] [Abstract][Full Text] [Related]
23. Distinct brain activation patterns for human maximal voluntary eccentric and concentric muscle actions.
Fang Y; Siemionow V; Sahgal V; Xiong F; Yue GH
Brain Res; 2004 Oct; 1023(2):200-12. PubMed ID: 15374746
[TBL] [Abstract][Full Text] [Related]
24. A new statistical test based on the wavelet cross-spectrum to detect time-frequency dependence between non-stationary signals: application to the analysis of cortico-muscular interactions.
Bigot J; Longcamp M; Dal Maso F; Amarantini D
Neuroimage; 2011 Apr; 55(4):1504-18. PubMed ID: 21256224
[TBL] [Abstract][Full Text] [Related]
25. Online control of a brain-computer interface using phase synchronization.
Brunner C; Scherer R; Graimann B; Supp G; Pfurtscheller G
IEEE Trans Biomed Eng; 2006 Dec; 53(12 Pt 1):2501-6. PubMed ID: 17153207
[TBL] [Abstract][Full Text] [Related]
26. Classification of the intention to generate a shoulder versus elbow torque by means of a time-frequency synthesized spatial patterns BCI algorithm.
Deng J; Yao J; Dewald JP
J Neural Eng; 2005 Dec; 2(4):131-8. PubMed ID: 16317237
[TBL] [Abstract][Full Text] [Related]
27. P300-based brain-computer interface for environmental control: an asynchronous approach.
Aloise F; Schettini F; Aricò P; Leotta F; Salinari S; Mattia D; Babiloni F; Cincotti F
J Neural Eng; 2011 Apr; 8(2):025025. PubMed ID: 21436520
[TBL] [Abstract][Full Text] [Related]
28. A new discriminative common spatial pattern method for motor imagery brain-computer interfaces.
Thomas KP; Guan C; Lau CT; Vinod AP; Ang KK
IEEE Trans Biomed Eng; 2009 Nov; 56(11 Pt 2):2730-3. PubMed ID: 19605314
[TBL] [Abstract][Full Text] [Related]
29. Support vector channel selection in BCI.
Lal TN; Schröder M; Hinterberger T; Weston J; Bogdan M; Birbaumer N; Schölkopf B
IEEE Trans Biomed Eng; 2004 Jun; 51(6):1003-10. PubMed ID: 15188871
[TBL] [Abstract][Full Text] [Related]
30. A public data hub for benchmarking common brain-computer interface algorithms.
Zander TO; Ihme K; Gärtner M; Rötting M
J Neural Eng; 2011 Apr; 8(2):025021. PubMed ID: 21436533
[TBL] [Abstract][Full Text] [Related]
31. Selection and parameterization of cortical neurons for neuroprosthetic control.
Wahnoun R; He J; Helms Tillery SI
J Neural Eng; 2006 Jun; 3(2):162-71. PubMed ID: 16705272
[TBL] [Abstract][Full Text] [Related]
32. Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch.
Borisoff JF; Mason SG; Bashashati A; Birch GE
IEEE Trans Biomed Eng; 2004 Jun; 51(6):985-92. PubMed ID: 15188869
[TBL] [Abstract][Full Text] [Related]
33. Brain-computer interface research comes of age: traditional assumptions meet emerging realities.
Wolpaw JR
J Mot Behav; 2010 Nov; 42(6):351-3. PubMed ID: 21184352
[TBL] [Abstract][Full Text] [Related]
34. Exploring virtual environments with an EEG-based BCI through motor imagery.
Leeb R; Scherer R; Keinrath C; Guger C; Pfurtscheller G
Biomed Tech (Berl); 2005 Apr; 50(4):86-91. PubMed ID: 15884704
[TBL] [Abstract][Full Text] [Related]
35. Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis.
Blankertz B; Dornhege G; Schäfer C; Krepki R; Kohlmorgen J; Müller KR; Kunzmann V; Losch F; Curio G
IEEE Trans Neural Syst Rehabil Eng; 2003 Jun; 11(2):127-31. PubMed ID: 12899253
[TBL] [Abstract][Full Text] [Related]
36. How many people are able to control a P300-based brain-computer interface (BCI)?
Guger C; Daban S; Sellers E; Holzner C; Krausz G; Carabalona R; Gramatica F; Edlinger G
Neurosci Lett; 2009 Oct; 462(1):94-8. PubMed ID: 19545601
[TBL] [Abstract][Full Text] [Related]
37. Sparse linear regression for reconstructing muscle activity from human cortical fMRI.
Ganesh G; Burdet E; Haruno M; Kawato M
Neuroimage; 2008 Oct; 42(4):1463-72. PubMed ID: 18634889
[TBL] [Abstract][Full Text] [Related]
38. The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.
Blankertz B; Dornhege G; Krauledat M; Müller KR; Curio G
Neuroimage; 2007 Aug; 37(2):539-50. PubMed ID: 17475513
[TBL] [Abstract][Full Text] [Related]
39. Offline decoding of end-point forces using neural ensembles: application to a brain-machine interface.
Gupta R; Ashe J
IEEE Trans Neural Syst Rehabil Eng; 2009 Jun; 17(3):254-62. PubMed ID: 19497832
[TBL] [Abstract][Full Text] [Related]
40. Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials.
Trejo LJ; Rosipal R; Matthews B
IEEE Trans Neural Syst Rehabil Eng; 2006 Jun; 14(2):225-9. PubMed ID: 16792300
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]