Neuroimaging-based approaches in the brain-computer interface

Trends in Biotechnology

Volumn 28, Issue 11, 2010, Pages 552-560

  Min, Byoung Kyong ^a   Marzelli, Matthew J ^b   Yoo, Seung Schik ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED DATA; BI-DIRECTIONAL COMMUNICATION; BRAIN INTERFACES; BRAIN MACHINE INTERFACE; DIRECT COMMUNICATIONS; GAINING MOMENTUM; MACHINE LEARNING ALGORITHMS; METHODOLOGICAL APPROACH; NEURO-IMAGING; NEUROMODULATION; POTENTIAL APPLICATIONS; RELAY INFORMATION;

CONSUMER ELECTRONICS; INTERFACES (COMPUTER); LEARNING ALGORITHMS; NEUROPHYSIOLOGY;

BRAIN COMPUTER INTERFACE;

ALGORITHM; AUTOMATION; BRAIN COMPUTER INTERFACE; COMMUNICATION SOFTWARE; DOPPLER ECHOGRAPHY; ELECTROENCEPHALOGRAM; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL TRANSCRANIAL DOPPLER SONOGRAPHY; HUMAN; INFORMATION PROCESSING; MAGNETOENCEPHALOGRAPHY; NEAR INFRARED SPECTROSCOPY; NEUROIMAGING; NEUROMODULATION; PRIORITY JOURNAL; REVIEW; SIGNAL DETECTION;

ARTIFICIAL INTELLIGENCE; AUTOMATION; COMPUTER SYSTEMS; HUMANS; MAN-MACHINE SYSTEMS; PATTERN RECOGNITION, AUTOMATED; USER-COMPUTER INTERFACE;

EID: 77957771433     PISSN: 01677799     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tibtech.2010.08.002     Document Type: Review

References (87)

1. Vidal J.J. Toward direct brain-computer communication. Annu. Rev. Biophys. Bioeng. 1973, 2:157-180.
2. Stanley G.B., et al. Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus. J. Neurosci. 1999, 19:8036-8042.
3. Butts D.A., et al. Temporal precision in the neural code and the timescales of natural vision. Nature 2007, 449:92-95.
4. Hochberg L.R., et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006, 442:164-171.
5. Taylor D.M., et al. Direct cortical control of 3D neuroprosthetic devices. Science 2002, 296:1829-1832.
6. Shenoy P., et al. Generalized features for electrocorticographic BCIs. IEEE Trans. Biomed. Eng. 2008, 55:273-280.
7. Vansteensel M.J., et al. Brain-computer interfacing based on cognitive control. Ann. Neurol. 2010, 67:809-816.
8. Hill N.J., et al. Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. IEEE Trans. Neural Syst. Rehabil. Eng. 2006, 14:183-186.
9. Niedermeyer E., Lopes da Silva F.H. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields 1999, Lippincott Williams & Wilkins.
10. Babiloni C., et al. Fundamentals of electroencephalography, magnetoencephalography, and functional magnetic resonance imaging. Int. Rev. Neurobiol. 2009, 86:67-80.
11. Grech R., et al. Review on solving the inverse problem in EEG source analysis. J. Neuroeng. Rehabil. 2008, 5:25.
12. Chauveau N., et al. Effects of skull thickness, anisotropy, and inhomogeneity on forward EEG/ERP computations using a spherical three-dimensional resistor mesh model. Hum. Brain Mapp. 2004, 21:86-97.
13. Murakami S., Okada Y. Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals. J. Physiol. 2006, 575:925-936.
14. Nunez P.L. The brain's magnetic field: some effects of multiple sources on localization methods. Electroencephalogr. Clin. Neurophysiol. 1986, 63:75-82.
15. Bradshaw L.A., et al. Spatial filter approach for comparison of the forward and inverse problems of electroencephalography and magnetoencephalography. Ann. Biomed. Eng. 2001, 29:214-226.
16. Belliveau J.W., et al. Functional mapping of the human visual cortex by magnetic resonance imaging. Science 1991, 254:716-719.
17. Gati J.S., et al. Experimental determination of the BOLD field strength dependence in vessels and tissue. Magn. Reson. Med. 1997, 38:296-302.
18. Friston K., et al. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 1995, 2:189-210.
19. Esposito F., et al. Real-time independent component analysis of fMRI time-series. Neuroimage 2003, 20:2209-2224.
20. Yoo S.S., et al. Head motion analysis during cognitive fMRI examination: application in patients with schizophrenia. Neurosci. Res. 2005, 53:84-90.
21. Okada E., et al. Theoretical and experimental investigation of near-infrared light propagation in a model of the adult head. Appl. Opt. 1997, 36:21-31.
22. Taga G., et al. Effects of source-detector distance of near infrared spectroscopy on the measurement of the cortical hemodynamic response in infants. Neuroimage 2007, 38:452-460.
23. Wabnitz H., et al. Time-resolved near-infrared spectroscopy and imaging of the adult human brain. Adv. Exp. Med. Biol. 2010, 662:143-148.
24. Birbaumer N., et al. Physiological regulation of thinking: brain-computer interface (BCI) research. Prog. Brain Res. 2006, 159:369-391.
25. Bishop D.V., et al. An efficient and reliable method for measuring cerebral lateralization during speech with functional transcranial Doppler ultrasound. Neuropsychologia 2009, 47:587-590.
26. Müller K.R., et al. Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. J. Neurosci. Methods 2008, 167:82-90.
27. Blankertz B., et al. The Berlin Brain-Computer Interface: accurate performance from first-session in BCI-naive subjects. IEEE Trans. Biomed. Eng. 2008, 55:2452-2462.
28. deCharms R.C., et al. Control over brain activation and pain learned by using real-time functional MRI. Proc. Natl. Acad. Sci. U. S. A. 2005, 102:18626-18631.
29. Yoo S.S., et al. Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery. Int. J. Imaging Syst. Technol. 2008, 18:69-78.
30. Gevensleben H., et al. Distinct EEG effects related to neurofeedback training in children with ADHD: a randomized controlled trial. Int. J. Psychophysiol. 2009, 74:149-157.
31. Trudeau D.L. The treatment of addictive disorders by brain wave biofeedback: a review and suggestions for future research. Clin. Electroencephalogr. 2000, 31:13-22.
32. Donchin E., Coles M.G.H. Is the P300 component a manifestation of context updating?. Behav. Brain Sci. 1988, 11:357-374.
33. Farwell L.A., Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 1988, 70:510-523.
34. Birbaumer N., et al. Slow potentials of the cerebral cortex and behavior. Physiol. Rev. 1990, 70:1-41.
35. Vialatte F.B., et al. Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog. Neurobiol. 2010, 90:418-438.
36. Pfurtscheller G., et al. Foot and hand area mu rhythms. Int. J. Psychophysiol. 1997, 26:121-135.
37. Babiloni C., et al. Alpha event-related desynchronization preceding a go/no-go task: a high-resolution EEG study. Neuropsychology 2004, 18:719-728.
38. Pfurtscheller G., et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 2006, 31:153-159.
39. Besserve M., et al. Classification methods for ongoing EEG and MEG signals. Biol. Res. 2007, 40:415-437.
40. Lotte F., et al. A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural. Eng. 2007, 4:R1-R13.
41. Iversen I., et al. Conditional associative learning examined in a paralyzed patient with amyotrophic lateral sclerosis using brain-computer interface technology. Behav. Brain Funct. 2008, 4:53.
42. Müller K.R., et al. Machine learning techniques for brain-computer interfaces. Biomed. Eng. (Biomedizinische Technik) 2004, 49:11-22.
43. Blankertz B., et al. The Berlin Brain-Computer Interface: EEG-based communication without subject training. IEEE Trans. Neural Syst. Rehabil. Eng. 2006, 14:147-152.
44. Pfurtscheller G., Lopes da Silva F.H. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 1999, 110:1842-1857.
45. van Gerven M., Jensen O. Attention modulations of posterior alpha as a control signal for two-dimensional brain-computer interfaces. J. Neurosci. Methods 2009, 179:78-84.
46. Zhdanov A., et al. Inferring functional brain States using temporal evolution of regularized classifiers. Comput. Intell. Neurosci. 2007, 52609.
47. Montazeri N., et al. MEG based classification of wrist movement. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 2009:986-989.
48. Battapady H., et al. Single trial detection of human movement intentions from SAM-filtered MEG signals for a high performance two-dimensional BCI. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 2009:524-527.
49. Birbaumer N., Cohen L.G. Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 2007, 579:621-636.
50. Salmelin R., et al. Functional segregation of movement-related rhythmic activity in the human brain. Neuroimage 1995, 2:237-243.
51. Braun C., et al. Differential activation in somatosensory cortex for different discrimination tasks. J. Neurosci. 2000, 20:446-450.
52. Mellinger J., et al. An MEG-based brain-computer interface (BCI). Neuroimage 2007, 36:581-593.
53. Weiskopf N., et al. Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans Biomed Eng. 2004, 51:966-970.
54. Yoo S.S., Jolesz F.A. Functional MRI for neurofeedback: feasibility study on a hand motor task. Neuroreport 2002, 13:1377-1381.
55. Yoo S.S., et al. Functional magnetic resonance imaging-mediated learning of increased activity in auditory areas. Neuroreport 2007, 18:1915-1920.
56. Yoo S.S., et al. Increasing cortical activity in auditory areas through neurofeedback functional magnetic resonance imaging. Neuroreport 2006, 17:1273-1278.
57. Yoo S.S., et al. Reproducibility of trial-based functional MRI on motor imagery. Int. J. Neurosci. 2007, 117:215-227.
58. deCharms R.C., et al. Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 2004, 21:436-443.
59. Lee J.H., et al. Brain-machine interface via real-time fMRI: preliminary study on thought-controlled robotic arm. Neurosci. Lett. 2009, 450:1-6.
60. Cox D.D., Savoy R.L. Functional magnetic resonance imaging (fMRI) 'brain reading': detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 2003, 19:261-270.
61. Lee J.H., et al. Automated classification of fMRI data employing trial-based imagery tasks. Med. Image Anal. 2009, 13:392-404.
62. Mourao-Miranda J., et al. Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. Neuroimage 2005, 28:980-995.
63. Sitaram R., et al. Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface. Neuroimage 2007, 34:1416-1427.
64. Coyle S., et al. On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. Physiol. Meas. 2004, 25:815-822.
65. Kennan R.P., et al. Simultaneous recording of event-related auditory oddball response using transcranial near infrared optical topography and surface EEG. Neuroimage 2002, 16:587-592.
66. Luu S., Chau T. Decoding subjective preference from single-trial near-infrared spectroscopy signals. J. Neural. Eng. 2009, 6:016003.
67. Bauernfeind G., et al. Development, set-up and first results for a one-channel near-infrared spectroscopy system. Biomed. Tech. (Berl.) 2008, 53:36-43.
68. Diehl B., et al. Mapping of spikes, slow waves, and motor tasks in a patient with malformation of cortical development using simultaneous EEG and fMRI. Magn. Reson. Imaging 2003, 21:1167-1173.
69. Bonmassar G., et al. Motion and ballistocardiogram artifact removal for interleaved recording of EEG and EPs during MRI. Neuroimage 2002, 16:1127-1141.
70. Briselli E., et al. An independent component analysis-based approach on ballistocardiogram artifact removing. Magn. Reson. Imaging 2006, 24:393-400.
71. Hoy K.E., Fitzgerald P.B. Brain stimulation in psychiatry and its effects on cognition. Nat. Rev. Neurol. 2010, 6:267-275.
72. Fregni F., et al. Recent advances in the treatment of chronic pain with non-invasive brain stimulation techniques. Lancet Neurol. 2007, 6:188-191.
73. Jolesz F.A., et al. MR imaging-controlled focused ultrasound ablation: a noninvasive image-guided surgery. Magn. Reson. Imaging Clin. N. Am. 2005, 13:545-560.
74. Gavrilov L.R., et al. Application of focused ultrasound for the stimulation of neural structures. Ultrasound Med. Biol. 1996, 22:179-192.
75. Yoo, S.S. et al. (2009) Non-invasive regional modulation of brain function by focused ultrasound. In: Society for Neuroscience, pp. 1719, #105.111.
76. AIUM Clinical Standards Committee (2004) How to interpret the ultrasound output display standard for higher acoustic output diagnostic ultrasound devices: version 2. J. Ultrasound Med. 23, 723-726.
77. Nijboer F., et al. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin. Neurophysiol. 2008, 119:1909-1916.
78. Kübler A., et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 2005, 64:1775-1777.
79. Rebsamen, B. et al. (2010) A brain controlled wheelchair to navigate in familiar environments. IEEE Trans. Neural Syst. Rehab. Eng. 2010 May 10. [Epub ahead of print].
80. Broetz, D. et al. (2010) Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil. Neural Repair 2010 Jun 2. [Epub ahead of print].
81. Debove C., et al. The Rilutek ® (riluzole) Global Early Access Programme: an open-label safety evaluation in the treatment of amyotrophic lateral sclerosis. Amyotrop. Lateral Scler. Other Motor Neuron Disord. 2001, 2:153-158.
82. WHO (2007) World Health Report (from the World Health Organization).
83. Walker J.E. Using QEEG-guided neurofeedback for epilepsy versus standardized protocols: enhanced effectiveness?. Appl. Psychophysiol. Biofeedback 2010, 35:29-30.
84. deCharms R.C. Applications of real-time fMRI. Nat. Rev. Neurosci. 2008, 9:720-729.
85. Arns M., et al. Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clin. EEG Neurosci. 2009, 40:180-189.
86. Hammond D.C. Neurofeedback with anxiety and affective disorders. Child Adolesc. Psychiatr. Clin. N. Am. 2005, 14:105-123. vii.
87. Vaughan T.M., et al. The Wadsworth BCI Research and Development Program: at home with BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 2006, 14:229-233.