Grundlagen und Anwendung von Brain-Machine Interfaces (BMI)^[*]

Zusammenfassung

Brain-Machine Interfaces (BMI) sind, seit ihrem Aufkommen vor 50 Jahren, immer stärker in den Fokus der neurowissenschaftlichen Forschung gerückt. Die neuronale Aktivität des Gehirns wird zur Steuerung eines Effektors genutzt, z. B. eines Computers oder einer Prothese. Zusätzlich zu den potentiellen klinischen Möglichkeiten, die sich besonders für Patienten mit motorischen Defiziten bieten, entstand auch ein neues neurowissenschaftliches Werkzeug, das es ermöglicht, Einblicke in neuronale Funktionen zu erlangen. Dementsprechend ist die BMI-Forschung an der Schnittstelle zwischen angewandter und Grundlagenforschung angesiedelt. BMIs können nach mehreren Eigenschaften unterschieden werden: Zum Ersten hinsichtlich der Methode, mit der die neuronale Aktivität gemessen wird, zum Zweiten hinsichtlich der Datenanalyse und zum Dritten nach der Anwendung.

Ziel dieses Artikels ist es, eine kurze Zusammenfassung der BMI-Forschung der vergangenen Dekaden und einen Überblick über die aktuelle BMI-Forschung zu geben. Zusätzlich diskutieren wir die Herausforderungen, die sich hieraus insbesondere für die klinische Anwendung ergeben haben. Wir legen dabei den Schwerpunkt auf BMIs zur Unterstützung und zum Ersatz motorischer Funktionen und Kommunikation. Abschließend geben wir einen kurzen Ausblick auf neue Entwicklungen der BMI Forschung.

Abstract

Brain-Machine Interfaces (BMI) emerged about 50 years ago, and since then they have increasingly moved into the focus of neuroscientific research. In BMI the neuronal activity of the brain is used to control an external effector such as a computer or prosthesis. Besides potential clinical applications, especially for patients with motor disabilities, BMIs offer a new neuroscientific tool to better understand brain function. Therefore, BMI research is at the interface between basic and applied research. The particular implementation of BMI-systems varies greatly regarding the methods used for assessment of brain activation, the data analysis methods, and the targeted application.

In this review we will provide a brief overview over the development of BMI-research during the past decades and summarize recent research. We will focus on BMI development in the context of motor function and communication and discuss, in particular, challenges arising in clinical applications. Finally, we will briefly discuss future trends arising in current BMI research.

Schlüsselwörter

Brain-Machine Interfaces - Neuroprothesen

Key words

brain-machine interfaces - neuroprosthesis

• Literatur

• 1 Vidal JJ. Toward direct brain-computer communication. Annu Rev Biophys Bioeng 1973; 2: 157-180
• 2 Vidal JJ. Real-time detection of brain events in EEG. Proceedings of the IEEE 1977; 65: 633-641
• 3 Farwell LA, 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
• 4 Guger C, Ramoser H, Pfurtscheller G. Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). IEEE Trans Rehabil Eng 2000; 8: 447-456
• 5 Guger C, Daban S, Sellers E et al. How many people are able to control a P300-based brain-computer interface (BCI)?. Neurosci Lett 2009; 462: 94-98
• 6 Mak JN, Arbel Y, Minett JW et al. Optimizing the P300-based brain-computer interface: current status, limitations and future directions. Journal of Neural Engineering 2011; 8: 025003
• 7 Wolpaw JR, McFarland DJ, Neat GW et al. An EEG-based brain-computer interface for cursor control. Electroencephalogr Clin Neurophysiol 1991; 78: 252-259
• 8 Royer AS, Doud AJ, Rose ML et al. EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies. IEEE Trans Neural Syst Rehabil Eng 2010; 18: 581-589
• 9 Fetz EE. Operant Conditioning of Cortical Unit Activity. Science 1969; 163: 955-958
• 10 Schmidt EM. Single neuron recording from motor cortex as a possible source of signals for control of external devices. Ann Biomed Eng 1980; 8: 339-349
• 11 Georgopoulos AP, Schwartz AB, Kettner RE. Neuronal population coding of movement direction. Science 1986; 233: 1416-1419
• 12 Carmena JM, Lebedev MA, Crist RE et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol 2003; 1: E42
• 13 Santhanam G, Ryu SI, Yu BM et al. A high-performance brain-computer interface. Nature 2006; 442: 195-198
• 14 Nicolelis MAL, Dimitrov D, Carmena JM et al. Chronic, multisite, multielectrode recordings in macaque monkeys. Proceedings of the National Academy of Sciences 2003; 100: 11041-11046
• 15 Chapin JK, Moxon KA, Markowitz RS et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat Neurosci 1999; 2: 664-670
• 16 Farwell LA, 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
• 17 Hohne J, Schreuder M, Blankertz B et al. Two-dimensional auditory p300 speller with predictive text system. Conf Proc IEEE Eng Med Biol Soc 2010; 2010: 4185-4188
• 18 Middendorf M, McMillan G, Calhoun G et al. Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans Rehabil Eng 2000; 8: 211-214
• 19 Birbaumer N, Elbert T, Canavan AG et al. Slow potentials of the cerebral cortex and behavior. Physiol Rev 1990; 70: 1-41
• 20 Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proceedings of the IEEE 2001; 89: 1123-1134
• 21 Pfurtscheller G, Brunner C, Schlögl A et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 2006; 31: 153-159
• 22 Crone NE, Miglioretti DL, Gordon B et al. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. Brain 1998; 121 (Pt 12) 2301-2315
• 23 Taylor DM, Tillery SIH, Schwartz AB. Direct cortical control of 3D neuroprosthetic devices. Science 2002; 296: 1829-1832
• 24 Carmena JM, Lebedev MA, Henriquez CS et al. Stable Ensemble Performance with Single-Neuron Variability during Reaching Movements in Primates. The Journal of Neuroscience 2005; 25: 10712-10716
• 25 Grozea C, Voinescu CD, Fazli S. Bristle-sensors – ow-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications. J Neural Eng 2011; 8: 025008
• 26 Quandt F, Reichert C, Hinrichs H et al. Single trial discrimination of individual finger movements on one hand: A combined MEG and EEG study. NeuroImage 2012; 59: 3316-3324
• 27 Ball T, Demandt E, Mutschler I et al. Movement related activity in the high gamma range of the human EEG. Neuroimage 2008; 41: 302-310
• 28 Fries P. Neuronal Gamma-Band Synchronization as a Fundamental Process in Cortical Computation. Annual Review of Neuroscience 2009; 32: 209-224
• 29 Kubánek J, Miller KJ, Ojemann JG et al. Decoding flexion of individual fingers using electrocorticographic signals in humans. J Neural Eng 2009; 6: 066001
• 30 Mellinger J, Schalk G, Braun C et al. An MEG-based brain-computer interface (BCI). Neuroimage 2007; 36: 581-593
• 31 Cox RW, Jesmanowicz A, Hyde JS. Real-time functional magnetic resonance imaging. Magn Reson Med 1995; 33: 230-236
• 32 Hollmann M, Rieger JW, Baecke S et al. Predicting Decisions in Human Social Interactions Using Real-Time fMRI and Pattern Classification. PLoS ONE 2011; 6: e25304
• 33 Weiskopf N, Veit R, Erb M et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 2003; 19: 577-586
• 34 Weiskopf N, Sitaram R, Josephs O et al. Real-time functional magnetic resonance imaging: methods and applications. Magn Reson Imaging 2007; 25: 989-1003
• 35 Yoo S-S, Fairneny T, Chen N-K et al. Brain-computer interface using fMRI: spatial navigation by thoughts. Neuroreport 2004; 15: 1591-1595
• 36 Jöbsis FF. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 1977; 198: 1264-1267
• 37 Obrig H, Villringer A. Near-infrared spectroscopy in functional activation studies. Can NIRS demonstrate cortical activation? Adv Exp Med Biol 1997; 413: 113-127
• 38 Coyle S, Ward T, Markham C et al. On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces. Physiol Meas 2004; 25: 815-822
• 39 Kanoh S, Murayama Y-M, Miyamoto K-I et al. A NIRS-based brain-computer interface system during motor imagery: system development and online feedback training. Conf Proc IEEE Eng Med Biol Soc 2009; 2009: 594-597
• 40 Nagaoka T, Sakatani K, Awano T et al. Development of a new rehabilitation system based on a brain-computer interface using near-infrared spectroscopy. Adv Exp Med Biol 2010; 662: 497-503
• 41 Dürschmid S, Quandt F, Hinrichs H et al. Human Dorsolateral Prefrontal Cortex Gates Motor Skill. Society for Neuroscience Meeting; Washington, USA: 2011
• 42 Onton J, Makeig S. High-frequency Broadband Modulations of Electroencephalographic Spectra. Front Hum Neurosci 2009; 3: 61
• 43 Wolpaw JR, Loeb GE, Allison BZ et al. BCI Meeting 2005 – workshop on signals and recording methods. IEEE Trans Neural Syst Rehabil Eng 2006; 14: 138-141
• 44 Ganguly K, Dimitrov DF, Wallis JD et al. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat Neurosci 2011; 14: 662-667
• 45 Hochberg LR, Serruya MD, Friehs GM et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006; 442: 164-171
• 46 Simeral JD, Kim S-P, Black MJ et al. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. Journal of Neural Engineering 2011; 8: 025027
• 47 Bishop CM. Pattern Recognition and Machine Learning. 1^st ed. 2006. Corr. 2^nd printing ed. Springer; New York: 2007
• 48 Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. Springer; 2008
• 49 Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999; 110: 1842-1857
• 50 Blankertz B, Tomioka R, Lemm S et al. Optimizing Spatial filters for Robust EEG Single-Trial Analysis. IEEE Signal Processing Magazine 2008; 25: 41-56
• 51 Wolpaw JR, McFarland DJ. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA 2004; 101: 17849-17854
• 52 Lal TN, Schröder M, Hinterberger T et al. Support vector channel selection in BCI. IEEE Trans Biomed Eng 2004; 51: 1003-1010
• 53 Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003; 3: 1157-1182
• 54 Schlögl A, Neuper C, Pfurtscheller G. Estimating the mutual information of an EEG-based Brain-Computer Interface. Biomed Tech (Berl) 2002; 47: 3-8
• 55 Garrett D, Peterson DA, Anderson CW et al. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2003; 11: 141-144
• 56 Wu W, Gao Y, Bienenstock E et al. Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput 2006; 18: 80-118
• 57 Rieger JW, Dürschmid S, Quandt F et al. Motor learning is specifically represented in theta/high gamm cross-frequency coupling. Society for Neuroscience Meeting; Washington, USA: 2011
• 58 Müller-Putz GR, Scherer R, Brauneis C et al. Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J Neural Eng 2005; 2: 123-130
• 59 Mason SG, Birch GE. A brain-controlled switch for asynchronous control applications. IEEE Trans Biomed Eng 2000; 47: 1297-1307
• 60 Del R, Millan JJ, Galan F et al. Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. Conf Proc IEEE Eng Med Biol Soc 2009; 2009: 3361-3364
• 61 Heuschmann P, Busse O, Wagner M et al. Schlaganfallhäufigkeit und Versorgung von Schlaganfallpatienten in Deutschland. Akt Neurol 2010; 37: 333 340
• 62 Birbaumer N, Kübler A, Ghanayim N et al. The thought translation device (TTD) for completely paralyzed patients. IEEE Trans Rehabil Eng 2000; 8: 190-193
• 63 Karim AA, Hinterberger T, Richter J et al. Neural internet: Web surfing with brain potentials for the completely paralyzed. Neurorehabil Neural Repair 2006; 20: 508-515
• 64 Bensch M, Karim AA, Mellinger J et al. Nessi: an EEG-controlled web browser for severely paralyzed patients. Comput Intell Neurosci 2007; 71863
• 65 Blankertz B, Müller K-R, Curio G et al. The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans Biomed Eng 2004; 51: 1044-1051
• 66 Vaughan TM, McFarland DJ, Schalk G et al. The Wadsworth BCI Research and Development Program: at home with BCI. IEEE Trans Neural Syst Rehabil Eng 2006; 14: 229-233
• 67 Nijboer F, Sellers EW, Mellinger J et al. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 2008; 119: 1909-1916
• 68 Kübler A, Furdea A, Halder S et al. A brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients. Ann N Y Acad Sci 2009; 1157: 90-100
• 69 Brunner P, Ritaccio AL, Emrich JF et al. Rapid Communication with a “P300” Matrix Speller Using Electrocorticographic Signals (ECoG). Front Neurosci 2011; 5
• 70 Lai S-M, Studenski S, Duncan PW et al. Persisting consequences of stroke measured by the Stroke Impact Scale. Stroke 2002; 33: 1840-1844
• 71 Taub E, Uswatte G, Pidikiti R. Constraint-Induced Movement Therapy: a new family of techniques with broad application to physical rehabilitation – a clinical review. J Rehabil Res Dev 1999; 36: 237-251
• 72 Sharma N, Pomeroy VM, Baron J-C. Motor Imagery. Stroke 2006; 37: 1941-1952
• 73 Page SJ, Levine P, Leonard A. Mental Practice in Chronic Stroke. Stroke 2007; 38: 1293-1297
• 74 Buch E, Weber C, Cohen LG et al. Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 2008; 39: 910-917
• 75 Broetz D, Braun C, Weber C et al. Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil Neural Repair 2010; 24: 674-679
• 76 Ang KK, Guan C, Chua KSG et al. A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation. Conf Proc IEEE Eng Med Biol Soc 2009; 2009: 5981-5984
• 77 Yao J, Sheaff C, Dewald JPA. Usage of the ACT Robot in a Brain Machine Interface for Hand Opening and Closing in Stroke Survivors. IEEE Int Conf Rehabil Robot 2008; 2007: 938-942
• 78 Do AH, Wang PT, King CE et al. Brain-computer interface controlled functional electrical stimulation system for ankle movement. J Neuroeng Rehabil 2011; 8: 49
• 79 Duncan PW, Goldstein LB, Horner RD et al. Similar motor recovery of upper and lower extremities after stroke. Stroke 1994; 25: 1181-1188
• 80 Ang KK, Guan C, Chua KSG et al. A clinical evaluation on the spatial patterns of non-invasive motor imagery-based brain-computer interface in stroke. Conf Proc IEEE Eng Med Biol Soc 2008; 2008: 4174-4177
• 81 Daly JJ, Cheng R, Rogers J et al. Feasibility of a new application of noninvasive Brain Computer Interface (BCI): a case study of training for recovery of volitional motor control after stroke. J Neurol Phys Ther 2009; 33: 203-211
• 82 Pistohl T, Schulze-Bonhage A, Aertsen A et al. Decoding natural grasp types from human ECoG. Neuroimage 2012; 59: 248-260
• 83 Ganguly K, Secundo L, Ranade G et al. Cortical representation of ipsilateral arm movements in monkey and man. J Neurosci 2009; 29: 12948-12956
• 84 Schalk G, Kubánek J, Miller KJ et al. Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J Neural Eng 2007; 4: 264-275
• 85 Waldert S, Preissl H, Demandt E et al. Hand movement direction decoded from MEG and EEG. J Neurosci 2008; 28: 1000-1008
• 86 Leeb R. Self-paced exploration of the Austrian National Library through thought. International journal of bioelectromagnetism 2007; 9: 237-244
• 87 Leeb R, Sagha H, Chavarriaga R et al. A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities. J Neural Eng 2011; 8: 025011
• 88 Finke A, Lenhardt A, Ritter H. The MindGame: a P300-based brain-computer interface game. Neural Netw 2009; 22: 1329-1333
• 89 Nijholt A, Reuderink B, Oude Bos D. Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games. In: Nijholt A, Reidsma D, Hondorp H. (eds.) Intelligent Technologies for Interactive Entertainment. Berlin, Heidelberg: Springer; Berlin Heidelberg 2009: 153-168
• 90 Lalor EC, Kelly SP, Finucane C et al. Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment. EURASIP J Adv Signal Process 2005; 2005: 3156-3164
• 91 Tangermann M, Krauledat M, Grzeska K et al. Playing pinball with non-invasive BCI. 2009 http://eprints.pascal-network.org/archive/00006476/ (accessed 22 Aug2011)
• 92 Haufe S, Treder MS, Gugler MF et al. EEG potentials predict upcoming emergency brakings during simulated driving. J Neural Eng 2011; 8: 056001
• 93 Chang EF, Rieger JW, Johnson K et al. Categorical speech representation in human superior temporal gyrus. Nat Neurosci 2010; 13: 1428-1432
• 94 Rieger JW, Reichert C, Gegenfurtner KR et al. Predicting the recognition of natural scenes from single trial MEG recordings of brain activity. Neuroimage 2008; 42: 1056-1068
• 95 Miller KJ, Zanos S, Fetz EE et al. Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans. J Neurosci 2009; 29: 3132-3137
• 96 Rieger JW, Secundo L, Chang EF et al. Good vibrations: Single trial high gamma oscillations are better predictors of movement/non-movement discrimination. Society for Neuroscience Meeting; Chicago, USA: 2009
• 97 Quandt F, Rieger JW, Reichert C et al. Comparing the information content of EEG, MEG and ECoG signals in a finger movement task. Society for Neuroscience Meeting; San Diego, USA: 2010
• 98 Brown L, van de Molengraft J, Yazicioglu RF et al. A low-power, wireless, 8-channel EEG monitoring headset. Conf Proc IEEE Eng Med Biol Soc 2010; 2010: 4197-4200
• 99 Polikov VS, Tresco PA, Reichert WM. Response of brain tissue to chronically implanted neural electrodes. J Neurosci Methods 2005; 148: 1-18
• 100 Kim D-H, Viventi J, Amsden JJ et al. Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics. Nat Mater 2010; 9: 511-517
• 101 Brunner C, Allison BZ, Krusienski DJ et al. Improved signal processing approaches in an offline simulation of a hybrid brain-computer interface. J Neurosci Methods 2010; 188: 165-173