Download PDF
Klinische Neurophysiologie 2012; 43(03): 213-218
DOI: 10.1055/s-0032-1316304
Richard-Jung-Preis
© Georg Thieme Verlag KG Stuttgart · New York

EEG-basierte Brain-Computer Interfaces zur Echtzeit-Dekodierung mentaler Zustände[ *]

EEG-Based Brain-Computer Interfaces for Real-Time Decoding of Mental States
G. Curio
1   AG Neurophysik, Klinik für Neurologie und Klinische Neurophysiologie, Campus Benjamin Franklin, Charité – Universitätsmedizin Berlin
3   Bernstein Fokus Neurotechnologie Berlin
4   Bernstein Center for Computational Neuroscience Berlin
,
B. Blankertz
2   AG Maschinelles Lernen, Technische Universität Berlin
3   Bernstein Fokus Neurotechnologie Berlin
,
K.-R. Müller
2   AG Maschinelles Lernen, Technische Universität Berlin
3   Bernstein Fokus Neurotechnologie Berlin
4   Bernstein Center for Computational Neuroscience Berlin
› Author Affiliations
Further Information

Publication History

Publication Date:
27 August 2012 (online)

Also available at
    Permissions and Reprints

Zusammenfassung

Brain-Computer Interfaces (BCI) setzen algorithmische Verfahren des maschinellen Lernens ein, um für jeden Benutzer spezifische Muster hochdimensionaler EEG-Merkmale zu extrahieren. Diese sind dafür optimiert, Intentions-bezogene Hirnzustände in Echtzeit zu dekodieren. Klassische BCI-Anwendungen für gelähmte Patienten sind Steuerungen von aktiven Prothesen oder Texteingabeprogrammen. Um motorische Intentionen des Benutzers zu erkennen, nutzt das BCI individuelle Aktivierungsindizes des Oberflächen-EEGs, wie das Bereitschaftspotenzial oder die Modulation regionaler Eigenrhythmen. Auch jenseits der Rehabilitation gibt es eine wachsende Bandbreite neuer Anwendungen dieser Neurotechnologie; beispielsweise kann das BCI als optimiertes Feedback-Instrument zur Stabilisierung mentaler Zustände wie Vigilanz oder Aufmerksamkeit eingesetzt werden.

Abstract

Brain-computer interfaces (BCI) employ algorithmic procedures of machine learning in order to extract user-specific patterns of high-dimensional EEG features. These patterns are optimised to decode intention-related brain states in real-time. Characteristic BCI applications for paralysed patients are control of active prostheses or speller software. To recognise a user’s motor intention a BCI system utilises individual EEG activation indices, such as the readiness potential or the modulation of regional EEG rhythms. Also beyond the borders of rehabilitation, this neurotechnology enables a growing set of novel application scenarios, e. g., BCIs can serve as optimised feedback tools for the stabilisation of mental states such as vigilance or attention.

Schlüsselwörter

Gehirn-Computer Schnittstelle - Maschinelles Lernen - Bereitschaftspotenzial - Neuro-Feedback - Prothesensteuerung

Key words

brain-computer interface (BCI) - machine learning - readiness potential - neuro-feedback - prosthesis control

*Gekürzte und aktualisierte Fassung des Beitrags „Forschen an einer neuen Schnittstelle zum Gehim: Das Berliner Brain-Computer Interface. „Müller KR, Blankertz B, Tangermann M, Curio G. In: Lengauer T (Ed.): Computermodelle in der Wissenschaft-zwischen Analyse, Vorhersage und Suggestion. Nova Acta Leopoldina. 2011; 110 (377): 235-257; Nachdruck mit freundlicher Genehmigung durch die Deutsche Akademie der Naturforscher Leopoidina-Nationale Akademie der Wissenschaften


 

  • Literatur

  • 1 Birbaumer N, Ghanayim N, Hinterberger T et al. A spelling device for the paralysed. Nature 1999; 398: 297-298
    CrossrefPubMedGoogle Scholar
  • 2 Blankertz B, Dornhege G, Schäfer C et al. Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Trans Neural Syst Rehabil Eng 2003; 11: 127-131
    CrossrefPubMedGoogle Scholar
  • 3 Müller KR, Tangermann M, Dornhege G et al. Machine learning for real-time single-trial EEG analysis: From brain-computer interfacing to mental state monitoring. J Neurosci Meth 2008; 167: 82-90
    CrossrefPubMedGoogle Scholar
  • 4 Müller KR, Blankertz B, Tangermann M et al. Forschen an einer neuen Schnittstelle zum Gehirn: Das Berliner Brain-Computer Interface. Nova Acta Leopoldina. 2011; 110 (377) 235-257
    PubMedGoogle Scholar
  • 5 Wolpaw JR, Birbaumer N, McFarland DJ et al. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002; 113: 767-791
    CrossrefPubMedGoogle Scholar
  • 6 Kornhuber HH, Deecke L. Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pflügers Arch 1965; 284: 1-17
    PubMedGoogle Scholar
  • 7 Cui RQ, Huter D, Lang W et al. Neuroimage of voluntary movement: topography of the Bereitschaftspotential, a 64-channel DC current source density study. Neuroimage 1999; 9: 124-134
    CrossrefPubMedGoogle Scholar
  • 8 Treder MS, Blankertz B. (C)overt attention and visual speller design in an ERP-based brain-computer interface. Behav Brain Funct 2010; 6: 28
    CrossrefPubMedGoogle Scholar
  • 9 Nikulin VV, Brismar T. Phase synchronization between alpha and beta oscillations in the human electroencephalogram. Neuroscience 2006; 137: 647-657
    CrossrefPubMedGoogle Scholar
  • 10 Pfurtscheller G, da Silva FHL. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999; 110: 1842-1857
    CrossrefPubMedGoogle Scholar
  • 11 Blankertz B, Sannelli C, Halder S et al. Neurophysiological predictor of SMR-based BCI performance. Neuroimage 2010; 51: 1303-1309
    CrossrefPubMedGoogle Scholar
  • 12 Blankertz B, Dornhege G, Krauledat M et al. The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage 2007; 37: 539-550
    CrossrefPubMedGoogle Scholar
  • 13 Dornhege G, Millán J, del R. et al. (eds.). Toward Brain-Computer Interfacing. Cambridge, MA: MIT Press; 2007
  • 14 Müller-Putz GR, Scherer R, Pfurtscheller G et al. based neuroprosthesis control: a step towards clinical practice. Neurosci Lett 2005; 382: 169-174
    CrossrefPubMedGoogle Scholar
  • 15 Shenoy P, Krauledat M, Blankertz B et al. Towards adaptive classification for BCI. J Neural Eng 2006; 3: R13-R23
    CrossrefPubMedGoogle Scholar
  • 16 Vidaurre C, Kawanabe M, Bünau Pv et al. Toward Unsupervised Adaptation of LDA for Brain-Computer Interfaces. IEEE Trans Biomed Eng 2011; 58: 587-597
    CrossrefPubMedGoogle Scholar
  • 17 Krauledat M, Tangermann M, Blankertz B et al. Towards zero training for Brain-Computer Interfacing. PLOS ONE 2008; 3: e2967
    CrossrefPubMedGoogle Scholar
  • 18 Fazli S, Popescu F, Donaczy M et al. Subject independent mental state classification in single trials. Neural Networks 2009; 22: 1305-1312
    CrossrefPubMedGoogle Scholar
  • 19 Blankertz B, Dornhege G, Lemm S et al. The Berlin Brain-Computer Interface: Machine learning based detection of user specific brain states. J Universal Computer Sci 2006; 12: 581-607
    PubMedGoogle Scholar
  • 20 von Bünau P, Meinecke FC, Kiraly F et al. Finding Stationary Subspaces in Multivariate Time Series. Phys Rev Lett 2009; 103: 214101
    CrossrefPubMedGoogle Scholar
  • 21 Vidaurre C, Sannelli C, Müller KR et al. Machine-learning based co-adaptive calibration. Neural Comput 2011; 23: 791-816
    CrossrefPubMedGoogle Scholar
  • 22 Nunez PL, Srinivasan R, Westdorp AF et al. EEG coherency I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalogr Clin Neurophysiol 1997; 103: 499-515
    CrossrefPubMedGoogle Scholar
  • 23 Blankertz B, Tomioka R, Lemm S et al. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Sig Proc Mag 2008; 25: 41-56
    PubMedGoogle Scholar
  • 24 Ziehe A, Müller KR, Nolte G et al. Artifact reduction in magnetoneurography based on time-delayed second-order correlations. IEEE Trans Biomed Eng 2000; 47: 75-87
    CrossrefPubMedGoogle Scholar
  • 25 Grosse-Wentrup G, Gramann K, Buss M. Adaptive spatial filters with predefined region of interest for EEG based brain-computer-interfaces. In: Advances in Neural Information Processing Systems 19. Eds.: Schölkopf B, Platt J, Hoffman T. 537-544 Cambridge, MA: MIT Press; 2007
    Google Scholar
  • 26 Müller KR, Anderson CW, Birch GE. Linear and non-linear methods for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 2003; 11: 165-169
    CrossrefPubMedGoogle Scholar
  • 27 Blankertz B, Lemm S, Treder MS et al. Single-trial analysis and classification of ERP components – a tutorial. NeuroImage 2011; 56: 814-825
    CrossrefPubMedGoogle Scholar
  • 28 Conradi J, Blankertz B, Tangermann M et al. Brain-computer interfacing in tetraplegic patients with high spinal cord injury. Int J Bioelectromagnetism 2009; 11: 65-68
    PubMedGoogle Scholar
  • 29 Blankertz B, Losch F, Krauledat M et al. The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects. IEEE Trans Biomed Eng 2008; 55: 2452-2462
    CrossrefPubMedGoogle Scholar
  • 30 Williamson J, Murray-Smith R, Blankertz B et al. Designing for uncertain, asymmetric control: Interaction design for brain computer interfaces. Int J of Human-Computer Studies 2009; 10: 827-841
    PubMedGoogle Scholar
  • 31 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
    CrossrefPubMedGoogle Scholar
  • 32 Kübler A, Müller KR. An introduction to brain computer interfacing. In: Toward Brain-Computer Interfacing. Eds.: Dornhege G, Millán J, del R, Hinterberger T, McFarland D, Müller KR. 1-25 Cambridge, MA: MIT Press; 2007
    Google Scholar
  • 33 Vidaurre C, Sannelli C, Müller KR et al. Co-adaptive calibration to improve BCI efficiency. J Neural Eng 2011; 8: 025009
    CrossrefPubMedGoogle Scholar
  • 34 Krauledat M, Dornhege G, Blankertz B et al. The Berlin brain-computer interface for rapid response. Biomed Tech 2004; 49: 61-62
    CrossrefPubMedGoogle Scholar
  • 35 Haufe S, Treder MS, Gugler MF et al. EEG potentials predict upcoming emergency brakings during simulated driving. J Neural Eng 2011; 8: 056001
    CrossrefPubMedGoogle Scholar
  • 36 Hochberg LR, Serruya MD, Friehs GM et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006; 442: 164-171
    CrossrefPubMedGoogle Scholar
  • 37 Mehring C, Rickert J, Vaadia E et al. Inference of hand movements from local field potentials in monkey motor cortex. Nat Neurosci 2003; 6: 1253-1254
    CrossrefPubMedGoogle Scholar
  • 38 Leuthardt EC, Schalk G, Wolpaw JR et al. A brain-computer interface using electrocorticographic signals in humans. J Neural Eng 2004; 1: 63-71
    CrossrefPubMedGoogle Scholar
  • 39 Kohlmorgen J, Dornhege G, Braun B et al. Improving human performance in a real operating environment through real-time mental workload detection. In: Toward Brain-Computer Interfacing. Eds.: Dornhege G, Millán J, del R, Hinterberger T, McFarland D, Müller KR. 409-422 Cambridge, MA: MIT Press; 2007
    Google Scholar
  • 40 Krepki R, Blankertz B, Curio G et al. The Berlin Brain-Computer Interface (BBCI): towards a new communication channel for online control in gaming applications. J Multimedia Tools Appl 2007; 33: 73-90
    PubMedGoogle Scholar
  • 41 Leeb R, Lee F, Keinrath C et al. Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans Neural Syst Rehabil Eng 2007; 15: 473-482
    CrossrefPubMedGoogle Scholar
  • 42 Gerson A, Parra L, Sajda P. Cortically coupled computer vision for rapid image search. IEEE Trans Neural Syst Rehabil Eng 2006; 14: 174-179
    CrossrefPubMedGoogle Scholar