Neuromuscular electrical stimulation induced brain patterns to decode motor imagery

Clinical Neurophysiology

Volumn 124, Issue 9, 2013, Pages 1824-1834

  Vidaurre, C ^a   Pascual, J ^a   Ramos Murguialday, A ^b,c   Lorenz, R ^a   Blankertz, B ^a,d   Birbaumer, N ^b,e   Muller K R ^a,d,f  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b UNIVERSITY OF TÜBINGEN   (Germany)

^c TECNALIA RESEARCH AND INNOVATION   (Spain)

^d Bernstein Focus: Neurotechnology   (Germany)

^e SAN CAMILLO FORLANINI HOSPITAL   (Italy)

^f Korea University   (South Korea)

Author keywords

Afferent patterns; BCI inefficency; Efferent pattern classification; Motor imagery; Neuromuscular electrical stimulation

Indexed keywords

ARTICLE; BRAIN COMPUTER INTERFACE; CLASSIFIER; CONTROLLED STUDY; ELECTROENCEPHALOGRAM; FOOT; HAND; HUMAN; HUMAN EXPERIMENT; IMAGERY; LIMB; NERVOUS SYSTEM PARAMETERS; NEUROMUSCULAR ELECTRICAL STIMULATION; NORMAL HUMAN; PASSIVE MOVEMENT; PRIORITY JOURNAL; TRAINING;

AFFERENT PATTERNS; BCI-INEFFICENCY; EFFERENT PATTERN CLASSIFICATION; MOTOR IMAGERY; NEUROMUSCULAR ELECTRICAL STIMULATION;

ANALYSIS OF VARIANCE; BRAIN; BRAIN-COMPUTER INTERFACES; CALIBRATION; EFFERENT PATHWAYS; ELECTRIC STIMULATION; ELECTROENCEPHALOGRAPHY; FEEDBACK, SENSORY; HUMANS; IMAGINATION; MODELS, STATISTICAL; MOVEMENT; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84881113245     PISSN: 13882457     EISSN: 18728952     Source Type: Journal    
DOI: 10.1016/j.clinph.2013.03.009     Document Type: Article

References (42)

1. Blankertz B., Dornhege G., Krauledat M., Müller K.R., Curio G. The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 2007, 37:539-550.
2. Blankertz B., Sannelli C., Halder S., Hammer E.M., Kübler A., Müller K.R., et al. Neurophysiological predictor of SMR-based BCI performance. Neuroimage 2010, 51:1303-1309.
3. Blankertz B., Tomioka R., Lemm S., Kawanabe M., Müller K.R. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 2008, 25:41-56.
4. Chatterjee A., Vikram A., Ramos A., Acharya A., Thakor N. A brain-computer interface with vibrotactile biofeedback for haptic information. J Neuroeng Rehabil 2007, 4:40.
5. Cho W, Vidaurre C, Hoffman U, Birbaumer N, Ramos-Murguialday A. Afferent and efferent activity control in the design of brain computer interfaces for motor rehabilitation. In: EMBS conference (EMBC) proceedings; 2011. p. 7310-15.
6. Cohen B.H. Explaining psychological statistics 2004, Wiley, USA.
7. Toward brain-computer interfacing 2007, MIT Press, Cambridge, MA. G. Dornhege, J. Millán, T. Hinterberger, D. McFarland, K.R. Müller (Eds.).
8. Gomez Rodriguez M., Peters J., Hill J., Schölkopf B., Gharabaghi A., Grosse-Wentrup M. Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. J Neural Eng 2011, 8:1-12.
9. Graimann B., Huggins J.E., Levine S.P., Pfurtscheller G. Visualization of significant ERD/ERS patterns in multichannel EEG and EcoG data. Clin Neurophysiol 2002, 113:43-47.
10. Kaiser V., Kreilinger A., Müller-Putz G.R., Neuper C. First steps towards a motor-imagery based stroke BCI: new strategy to set up a classifier. Front Neurosci 2011, 5:86.
11. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 1999, 29:169-195.
12. Krauledat M., Tangermann M., Blankertz B., Müller K.R. Towards zero training for brain-computer interfacing. PLoS One 2008, 3:e2967.
13. Krausz G., Scherer R., Korisek G., Pfurtscheller G. Critical decision-speed and information transfer in the "Graz Brain-Computer Interface". Appl Psychophysiol Biofeedback 2003, 28:233-240.
14. Kübler A., Birbaumer N. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients?. Clin Neurophysiol 2008, 119:2658-2666.
15. Kübler A., Kotchoubey B., Kaiser J., Wolpaw J., Birbaumer N. Brain-computer communication: unlocking the locked in. Psychol Bull 2001, 127:358-375.
16. Kübler A., Müller K.R. An introduction to brain computer interfacing. Toward brain-computer interfacing 2007, 1-25. MIT press, Cambridge, MA. G. Dornhege, J. del R. Millán, T. Hinterberger, D. McFarland, K.R. Müller (Eds.).
17. Kübler A., Neumann N., Wilhelm B., Hinterberger T., Birbaumer N. Predictability of brain-computer communication. Int J Psychophysiol 2004, 18:121-129.
18. Lemm S., Blankertz B., Dickhaus T., Müller K.R. Introduction to machine learning for brain imaging. Neuroimage 2011, 56:387-399.
19. Lotte F., Congedo M., Lécuyer A., Lamarche F., Arnaldi B. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng 2007, 4:R1.
20. McFarland D.J., McCane L.M., David S.V., Wolpaw J.R. Spatial filter selection for EEG-based communication. Electroencephalogr Clin Neurophysiol 1997, 103:386-394.
21. Millán J., Renkens F., Mouriño J., Gerstner W. Brain-actuated interaction. Artif Intell 2004, 159:241-259.
22. Millán J., Renkens F., Mouriño J., Gerstner W. Non-invasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Biomed Eng 2004, 51:1026-1033.
23. Müller K.R., Anderson C.W., Birch G.E. Linear and non-linear methods for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 2003, 11:165-169.
24. Müller-Gerking J., Pfurtscheller G., Flyvbjerg H. Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol 1999, 110:787-798.
25. Müller-Putz G.R., Scherer R., Pfurtscheller G., Rupp R. EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci Lett 2005, 382:169-174.
26. Pascual J, Ramos A, Vidaurre C. Classifying motor imagery with FES induced EEG patterns. In: Neuroscience letters, SAN conference 2011 Proceedings, vol. 500; 2011. p. e48.
27. Pfurtscheller G., Neuper C., Birbaumer N. Human brain-computer interface. Motor cortex in voluntary movements 2005, 367-401. CRC Press, New York. A. Riehle, E. Vaadia (Eds.).
28. Pfurtscheller G., Lopes da Silva F.H. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999, 110:1842-1857.
29. Popović M., Popović D. Cloning biological synergies improved control of elbow neuroprostheses. IEEE EMB Mag 2001, 20:74-81.
30. Qin L., He B. A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications. J Neural Eng 2005, 2:65-72.
31. Ramos-Murguialday A., Hill J., Bensch M., Martens S., Halder S., Nijboer F., et al. Transition from the locked-in to the completely locked-in state: a physiological analysis. Clin Neurophysiol 2011, 122:925-933.
32. Ramos-Murguialday A., Schürholz M., Caggiano V., Wildgruber M., Caria A., Hammer E.M., et al. Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses. PLoS One 2012, 7:e47048.
33. Sannelli C., Dickhaus T., Halder S., Hammer E.M., Müller K.R., Blankertz B. On optimal channel configurations for SMR-based brain-computer interfaces. Brain Topogr 2010, 23:186-193.
34. Sannelli C., Vidaurre C., Müller K.R., Blankertz B. Common spatial pattern patches - an optimized filter ensemble for adaptive brain-computer interfaces. J Neural Eng 2011, 8:025012.
35. Shenoy P., Krauledat M., Blankertz B., Rao R.P.N., Müller K.R. Towards adaptive classification for BCI. J Neural Eng 2006, 3:R13-R23.
36. Tavella M, Leeb R, Rupp R, Millán J. Towards natural non-invasive hand neuroprostheses for daily living. In: 32nd Annual international conference of the IEEE engineering in medicine and biology society (EMBC'10); 2010. p. 4343-46.
37. Vidaurre C., Kawanabe M., von Bünau P., Blankertz B., Müller K.R. Toward unsupervised adaptation of LDA for brain-computer interfaces. IEEE Trans Biomed Eng 2011, 58:587-597.
38. Vidaurre C., Krämer N., Blankertz B., Schlögl A. Time domain parameters as a feature for EEG-based brain computer interfaces. Neural Netw 2009, 22:1313-1319.
39. Vidaurre C., Sannelli C., Müller K.R., Blankertz B. Co-adaptive calibration to improve BCI efficiency. J Neural Eng 2011, 8:025009.
40. Vidaurre C., Sannelli C., Müller K.R., Blankertz B. Machine-learning based co-adaptive calibration. Neural Comput 2011, 23:791-816.
41. Vidaurre C., Scherer R., Cabeza R., Schlögl A., Pfurtscheller G. Study of discriminant analysis applied to motor imagery bipolar data. Med Biol Eng Comput 2007, 45:61-68.
42. Brain-computer interfaces: principles and practice 2012, Oxford University Press, USA. J. Wolpaw, E.W. Wolpaw (Eds.).