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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)

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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


 

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