Individually adapted imagery improves brain-computer interface performance in end-users with disability

PLoS ONE

Volumn 10, Issue 5, 2015, Pages

  Scherer, Reinhold ^a,b,c   Faller, Josef ^a,b   Friedrich, Elisabeth V C ^a,b,d   Opisso, Eloy ^e   Costa, Ursula ^e   Kübler, Andrea ^f   Müller Putz, Gernot R ^a,b  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b BIOTECHMED GRAZ   (Austria)

^c Rehabilitation Center Judendorf Straßengel   (Austria)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e University Institute affiliated with the Autonomous University of Barcelona   (Spain)

^f UNIVERSITY OF WÜRZBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ADULT; ARTICLE; BRAIN COMPUTER INTERFACE; CEREBROVASCULAR ACCIDENT; CLASSIFICATION; CLINICAL ARTICLE; ELECTROENCEPHALOGRAM; FEMALE; HUMAN; IMAGERY; LINGUISTICS; MALE; MATHEMATICAL COMPUTING; MENTAL PERFORMANCE; MENTAL TASK; MIDDLE AGED; MOTOR DYSFUNCTION; MOTOR IMAGERY; NERVOUS SYSTEM INJURY; NEUROMODULATION; PROPRIOCEPTION; SPATIAL ORIENTATION; SPINAL CORD INJURY; YOUNG ADULT; BRAIN; COMPUTER INTERFACE; ELECTROENCEPHALOGRAPHY; GUIDED IMAGERY; IMAGINATION; MOVEMENT (PHYSIOLOGY); PATHOPHYSIOLOGY; PSYCHOLOGY; QUADRIPLEGIA; REPRODUCIBILITY; SPINAL CORD INJURIES; STROKE REHABILITATION;

ADULT; BRAIN; BRAIN-COMPUTER INTERFACES; ELECTROENCEPHALOGRAPHY; FEMALE; HUMANS; IMAGERY (PSYCHOTHERAPY); IMAGINATION; MALE; MIDDLE AGED; MOVEMENT; QUADRIPLEGIA; REPRODUCIBILITY OF RESULTS; SPINAL CORD INJURIES; STROKE; STROKE REHABILITATION; USER-COMPUTER INTERFACE; YOUNG ADULT;

EID: 84930646530     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0123727     Document Type: Article

References (58)

1. Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proceedings of the IEEE. 2001; 89(7):1123-1134. Available from: http://ieeexplore.ieee.org/xpls/abs-all.jsp?arnumber=939829. doi: 10.1109/5.939829
2. Kübler A, Kotchoubey B, Kaiser J, Wolpaw JR, Birbaumer N. Brain-computer communication: unlocking the locked in. Psychological Bulletin. 2001 May; 127(3):358-375. Available from: http://psycnet.apa.org/doi/10.1037/0033-2909.127.3.358. doi: 10.1037/0033-2909.127.3.358 PMID: 11393301
3. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clinical Neurophysiology. 2002 Jun; 113(6):767-791. Available from: http://dx.doi.org/10.1016/S1388-2457(02)00057-3. doi: 10.1016/S1388-2457(02)00057-3 PMID: 12048038
4. Mason S, Bashashati A, Fatourechi M, Navarro K, Birch G. A comprehensive survey of brain interface technology designs. Annals of biomedical engineering. 2007; 35(2):137-169. Available from: http://link.springer.com/article/10.1007/s10439-006-9170-0. doi: 10.1007/s10439-006-9170-0 PMID: 17115262
5. Millán JDR, Rupp R, Müller-Putz GR, Murray-Smith R, Giugliemma C, Tangermann M, et al. Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges. Frontiers in Neuroprosthetics. 2010; 4. Available from: http://dx.doi.org/10.3389/fnins.2010.00161.
6. Nicolas-Alonso LF, Gomez-Gil J. Brain computer interfaces: a review. Sensors. 2012; 12(2):1211-1279. Available from: http://dx.doi.org/10.3390/s120201211. doi: 10.3390/s120201211 PMID: 22438708
7. Millán JdR, Mouriño J. Asynchronous BCI and local neural classifiers: an overview of the Adaptive Brain Interface project. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 2003 Jun; 11(2):159-161. Available from: http://dx.doi.org/10.1109/TNSRE.2003.814435. doi: 10.1109/TNSRE.2003.814435
8. Kübler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H, Schalk G, et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology. 2005; 64(10):1775-1777. Available from: http://www.neurology.org/content/64/10/1775.short. doi: 10.1212/01.WNL.0000158616.43002.6D PMID: 15911809
9. Pfurtscheller G, Müller-Putz GR, Schlögl A, Graimann B, Scherer R, Leeb R, et al. 15 years of BCI research at Graz University of Technology: current projects. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 2006 Jun; 14(2):205-210. Available from: http://dx.doi.org/10.1109/TNSRE.2006.875528. doi: 10.1109/TNSRE.2006.875528
10. Cincotti F, Mattia D, Aloise F, Bufalari S, Schalk G, Oriolo G, et al. Non-invasive brain-computer interface system: towards its application as assistive technology. Brain Research Bulletin. 2008 Apr; 75 (6):796-803. Available from: http://dx.doi.org/10.1016/j.brainresbull.2008.01.007. doi: 10.1016/j.brainresbull.2008.01.007 PMID: 18394526
11. Scherer R, Müller-Putz GR, Pfurtscheller G. Flexibility and practicality: Graz Brain-Computer Interface approach. International review of neurobiology. 2009; 86:119-131. Available from: http://www.sciencedirect.com/science/article/pii/S0074774209860091. doi: 10.1016/S0074-7742(09)86009-1 PMID: 19607995
12. Blankertz B, Sannelli C, Halder S, Hammer EM, Kübler A, Müller KR, et al. Neurophysiological predictor of SMR-based BCI performance. Neuroimage. 2010 Jul; 51(4):1303-1309. Available from: http://dx.doi.org/10.1016/j.neuroimage.2010.03.022. doi: 10.1016/j.neuroimage.2010.03.022 PMID: 20303409
13. McFarland DJ, Sarnacki WA, Wolpaw JR. Electroencephalographic (EEG) control of three-dimensional movement. Journal of Neural Engineering. 2010; 7(3):036007. Available from: http://iopscience.iop.org/1741-2560/7/3/036007. doi: 10.1088/1741-2560/7/3/036007 PMID: 20460690
14. Lotte F, Van Langhenhove A, Lamarche F, Ernest T, Renard Y, Arnaldi B, et al. Exploring large virtual environments by thoughts using a brain-computer interface based on motor imagery and high-level commands. Presence: teleoperators and virtual environments. 2010; 19(1):54-70. Available from: http://www.mitpressjournals.org/doi/abs/10.1162/pres.19.1.54. doi: 10.1162/pres.19.1.54
15. Vidaurre C, Kawanabe M, von Bünau P, Blankertz B, Müller KR. Toward unsupervised adaptation of LDA for brain-computer interfaces. Biomedical Engineering, IEEE Transactions on. 2011 Mar; 58 (3):587-597. Available from: http://dx.doi.org/10.1109/TBME.2010.2093133. doi: 10.1109/TBME.2010.2093133
16. LaFleur K, Cassady K, Doud A, Shades K, Rogin E, He B. Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface. Journal of Neural Engineering. 2013 Aug; 10(4):046003. Available from: http://dx.doi.org/10.1088/1741-2560/10/4/046003. doi: 10.1088/1741-2560/10/4/046003 PMID: 23735712
17. Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology. 1999; 110(11):1842-1857. Available from: http://www.sciencedirect.com/science/article/pii/S1388245799001418. doi: 10.1016/S1388-2457(99)00141-8 PMID: 10576479
18. Halder S, Agorastos D, Veit R, Hammer EM, Lee S, Varkuti B, et al. Neural mechanisms of brain-computer interface control. Neuroimage. 2011 Apr; 55(4):1779-1790. Available from: http://dx.doi.org/10.1016/j.neuroimage.2011.01.021. doi: 10.1016/j.neuroimage.2011.01.021 PMID: 21256234
19. Curran EA, Stokes MJ. Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems. Brain and cognition. 2003; 51(3):326-336. Available from: http://www.sciencedirect.com/science/article/pii/S0278262603000368. doi: 10.1016/S0278-2626(03)00036-8 PMID: 12727187
20. Toppi J, Risetti M, Quitadamo LR, Petti M, Bianchi L, Salinari S, et al. Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery. Journal of Neural Engineering. 2014 Jun; 11(3):035010. Available from: http://dx.doi.org/10.1088/1741-2560/11/3/035010. doi: 10.1088/1741-2560/11/3/035010 PMID: 24835634
21. Allison BZ, Neuper C. Could anyone use a BCI? In: Brain-computer interfaces. Springer; 2010. p. 35-54. Available from: http://dx.doi.org/10.1007/978-1-84996-272-8-3.
22. Vidaurre C, Pascual J, Ramos-Murguialday A, Lorenz R, Blankertz B, Birbaumer N, et al. Neuromuscular electrical stimulation induced brain patterns to decode motor imagery. Clinical Neurophysiology. 2013 Sep; 124(9):1824-1834. Available from: http://dx.doi.org/10.1016/j.clinph.2013.03.009. doi: 10.1016/j.clinph.2013.03.009 PMID: 23642833
23. Lotte F, Larrue F, Mühl C. Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design. Frontiers in Human Neuroscience. 2013; 7:568. Available from: http://dx.doi.org/10.3389/fnhum.2013.00568. doi: 10.3389/fnhum.2013.00568 PMID: 24062669
24. Kübler A, Neumann N, Wilhelm B, Hinterberger T, Birbaumer N. Predictability of Brain-Computer Communication. Journal of Psychophysiology. 2004; 18(2-3):121. Available from: http://dx.doi.org/10.1027/0269-8803.18.23.121.
25. Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B, et al. A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering. 2007; 4. Available from: http://iopscience.iop.org/1741-2552/4/2/R01. doi: 10.1088/1741-2560/4/2/R01 PMID: 17409472
26. Bashashati A, Fatourechi M, Ward RK, Birch GE. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. Journal of Neural Engineering. 2007; 4(2):R32-R57. Available from: http://iopscience.iop.org/1741-2560/4/2/R03. doi: 10.1088/1741-2560/4/2/R03 PMID: 17409474
27. Fatourechi M, Bashashati A, Ward RK, Birch GE. EMG and EOG artifacts in brain computer interface systems: A survey. Clinical Neurophysiology. 2007; 118(3):480-494. Available from: http://www.sciencedirect.com/science/article/pii/S1388245706015124. doi: 10.1016/j.clinph.2006.10.019 PMID: 17169606
28. Vidaurre C, Blankertz B. Towards a cure for BCI illiteracy. Brain Topography. 2010 Jun; 23(2):194-198. Available from: http://dx.doi.org/10.1007/s10548-009-0121-6. doi: 10.1007/s10548-009-0121-6 PMID: 19946737
29. Faller J, Vidaurre C, Solis-Escalante T, Neuper C, Scherer R. Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 2012; 20(3):313-319. Available from: http://ieeexplore.ieee.org/xpls/abs-all.jsp?arnumber=6177271. doi: 10.1109/TNSRE.2012.2189584
30. Samek W, Meinecke FC, Müller KR. Transferring Subspaces Between Subjects in Brain-Computer Interfacing. Biomedical Engineering, IEEE Transactions on. 2013 Aug; 60(8):2289-2298. Available from: http://dx.doi.org/10.1109/TBME.2013.2253608. doi: 10.1109/TBME.2013.2253608
31. Faller J, Scherer R, Costa U, Opisso E, Medina J, Müller-Putz GR. A co-adaptive brain-computer interface for end users with severe motor impairment. PLoS One. 2014; 9(7):e101168. Available from: http://dx.doi.org/10.1371/journal.pone.0101168. doi: 10.1371/journal.pone.0101168 PMID: 25014055
32. Neuper C, Scherer R, Reiner M, Pfurtscheller G. Imagery of motor actions: Differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Cognitive Brain Research. 2005; 25 (3):668-677. Available from: http://www.sciencedirect.com/science/article/pii/S0926641005002533. doi: 10.1016/j.cogbrainres.2005.08.014 PMID: 16236487
33. Scherer R. Towards practical Brain-Computer Interfaces: Self-paced operation and reduction of the number of EEG sensors. Ph.D. Thesis, Graz University of Technology; 2008.
34. Müller-Putz G, Scherer R, Pfurtscheller G, Neuper C. Temporal coding of brain patterns for direct limb control in humans. Frontiers in Neuroprosthetics. 2010; 4:34. Available from: http://dx.doi.org/10.3389/fnins.2010.00034.
35. Obermaier B, Neuper C, Guger C, Pfurtscheller G. Information transfer rate in a five-classes brain-computer interface. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 2001 Sep; 9 (3):283-288. Available from: http://dx.doi.org/10.1109/7333.948456. doi: 10.1109/7333.948456
36. Curran E, Sykacek P, Stokes M, Roberts SJ, Penny W, Johnsrude I, et al. Cognitive tasks for driving a brain-computer interfacing system: a pilot study. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 2004 Mar; 12(1):48-54. Available from: http://dx.doi.org/10.1109/TNSRE.2003.821372. doi: 10.1109/TNSRE.2003.821372
37. Friedrich EVC, Neuper C, Scherer R. Whatever works: a systematic user-centered training protocol to optimize brain-computer interfacing individually. PLoS One. 2013; 8(9):e76214. Available from: http://dx.doi.org/10.1371/journal.pone.0076214. doi: 10.1371/journal.pone.0076214 PMID: 24086710
38. Friedrich EVC, Scherer R, Neuper C. The effect of distinct mental strategies on classification performance for brain-computer interfaces. International Journal of Psychophysiology. 2012 Apr; 84(1):86-94. Available from: http://dx.doi.org/10.1016/j.ijpsycho.2012.01.014. doi: 10.1016/j.ijpsycho.2012.01.014 PMID: 22289414
39. Scherer R, Friedrich EC, Allison B, Pröll M, Chung M, Cheung W, et al. Non-invasive brain-computer interfaces: Enhanced gaming and robotic control. In: Advances in Computational Intelligence. Springer; 2011. p. 362-369. Available from: http://dx.doi.org/10.1007/978-3-642-21501-8-45.
40. Scherer R, Faller J, Balderas D, Friedrich EV, Pröll M, Allison B, et al. Brain-computer interfacing: more than the sum of its parts. Soft Computing. 2013; 17(2):317-331. Available from: http://dx.doi.org/10.1007/s00500-012-0895-4. doi: 10.1007/s00500-012-0895-4
41. Friedrich EVC, Scherer R, Neuper C. Long-term evaluation of a 4-class imagery-based brain-computer interface. Clinical Neurophysiology. 2013 May; 124(5):916-927. Available from: http://dx.doi.org/10.1016/j.clinph.2012.11.010. doi: 10.1016/j.clinph.2012.11.010 PMID: 23290926
42. Neuper C, Müller G, Kübler A, Birbaumer N, Pfurtscheller G. Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment. Clinical Neurophysiology. 2003; 114(3):399-409. Available from: http://www.sciencedirect.com/science/article/pii/S1388245702003875. doi: 10.1016/S1388-2457(02)00387-5 PMID: 12705420
43. Müller-Putz GR, Scherer R, Pfurtscheller G, Rupp R. EEG-based neuroprosthesis control: a step towards clinical practice. Neuroscience Letters. 2005; 382(1):169-174. Available from: http://www.sciencedirect.com/science/article/pii/S0304394005003009. doi: 10.1016/j.neulet.2005.03.021 PMID: 15911143
44. Horki P, Bauernfeind G, Klobassa DS, Pokorny C, Pichler G, Schippinger W, et al. Detection of mental imagery and attempted movements in patients with disorders of consciousness using EEG. Frontiers in Human Neuroscience. 2014; 8:1009. Available from: http://dx.doi.org/10.3389/fnhum.2014.01009. doi: 10.3389/fnhum.2014.01009 PMID: 25566029
45. Scherer R, Billinger M, Wagner J, Schwarz A, Hettich DT, Bolinger E, et al. Thought-based row-column scanning communication board for individuals with cerebral palsy. Annals of Physical and Rehabilitation Medicine. 2015; 58(1):14-22. Available from: http://dx.doi.org/10.1016/j.rehab.2014.11.005. doi: 10.1016/j.rehab.2014.11.005 PMID: 25661447
46. Pfurtscheller G, Linortner P, Winkler R, Korisek G, Müller-Putz G. Discrimination of motor imagery-induced EEG patterns in patients with complete spinal cord injury. Computational Intelligence and Neuroscience. 2009; Available from: http://dx.doi.org/10.1155/2009/104180. doi: 10.1155/2009/104180
47. Shenoy P, Krauledat M, Blankertz B, Rao RP, Müller KR. Towards adaptive classification for BCI. Journal of Neural Engineering. 2006; 3(1):R13-R23. Available from: http://iopscience.iop.org/1741-2552/3/1/R02. doi: 10.1088/1741-2560/3/1/R02 PMID: 16510936
48. Friedrich E, Scherer R, Faller J, Neuper C. Do user-related factors of motor impaired and able-bodied participants correlate with classification accuracy. In: Proceedings of the 5th International Brain-Computer Interface Conference 2011. Verlag der Technischen Universität Graz, Austria; 2011. p. 156-159. Available from: https://bci.tugraz.at/ProcBCIConf2011.pdf.
49. Alimardani M, Nishio S, Ishiguro H. Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators. Scientific Reports. 2013; 3:2396. Available from: http://dx.doi.org/10.1038/srep02396. doi: 10.1038/srep02396 PMID: 23928891
50. Alimardani M, Nishio S, Ishiguro H. Effect of biased feedback on motor imagery learning in BCI-teleoperation system. Frontiers in Systems Neuroscience. 2014; 8:52. Available from: http://dx.doi.org/10.3389/fnsys.2014.00052. doi: 10.3389/fnsys.2014.00052 PMID: 24782721
51. Pichiorri F, De Vico Fallani F, Cincotti F, Babiloni F, Molinari M, Kleih SC, et al. Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness. Journal of Neural Engineering. 2011 Apr; 8(2):025020. Available from: http://dx.doi.org/10.1088/1741-2560/8/2/025020. doi: 10.1088/1741-2560/8/2/025020 PMID: 21436514
52. Cincotti F, Pichiorri F, Aricò P, Aloise F, Leotta F, de Vico Fallani F, et al. EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb. In: Proceedings of the Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. 2012; p. 4112-4115. Available from: http://dx.doi.org/10.1109/EMBC.2012.6346871.
53. Ang KK, Guan C, Phua KS, Wang C, Zhou L, Tang KY, et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Frontiers in Neuroengineering. 2014; 7:30. Available from: http://dx.doi.org/10.3389/fneng.2014.00030. doi: 10.3389/fneng.2014.00030 PMID: 25120465
54. Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. Rehabilitation Engineering, IEEE Transactions on. 2000 Dec; 8(4):441-446. Available from: http://ieeexplore.ieee.org/xpls/abs-all.jsp?arnumber=895946. doi: 10.1109/86.895946
55. Müller-Gerking J, Pfurtscheller G, Flyvbjerg H. Designing optimal spatial filters for single-trial EEG classification in a movement task. Clinical Neurophysiology. 1999; 110(5):787-798. Available from: http://www.sciencedirect.com/science/article/pii/S1388245798000388. doi: 10.1016/S1388-2457(98)00038-8 PMID: 10400191
56. Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. Signal Processing Magazine, IEEE. 2008; 25(1):41-56. Available from: http://ieeexplore.ieee.org/xpls/abs-all.jsp?arnumber=4408441. doi: 10.1109/MSP.2008.4408441
57. Duda RO, Hart PE, Stork DG. Pattern classification. John Wiley & Sons; 2000.
58. Scherer R, Pfurtscheller G, Neuper C. Motor imagery induced changes in oscillatory EEG components: Speed vs. accuracy. In: Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course 2008. Verlag der Technischen Universität Graz, Austria; 2008. p. 186-190. Available from: https://bci.tugraz.at/ProcBCIConf2008.pdf.