G
Gabriel Curio
Researcher at Charité
Publications - 244
Citations - 16300
Gabriel Curio is an academic researcher from Charité. The author has contributed to research in topics: Somatosensory evoked potential & Electroencephalography. The author has an hindex of 59, co-authored 241 publications receiving 15041 citations. Previous affiliations of Gabriel Curio include Technical University of Berlin & Helsinki University of Technology.
Papers
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The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.
TL;DR: It is proposed that the key to quick efficiency in the BBCI system is its flexibility due to complex but physiologically meaningful features and its adaptivity which respects the enormous inter-subject variability.
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The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials
Benjamin Blankertz,Klaus-Robert Müller,Gabriel Curio,Theresa M. Vaughan,Gerwin Schalk,Jonathan R. Wolpaw,Alois Schlögl,C. Neuper,Gert Pfurtscheller,Thilo Hinterberger,Michael Schröder,Niels Birbaumer +11 more
TL;DR: The BCI Competition 2003 was organized to evaluate the current state of the art of signal processing and classification methods and the results and function of the most successful algorithms were described.
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Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms
TL;DR: It is shown that a suitably arranged interaction between these concepts can significantly boost BCI performances and derive information-theoretic predictions and demonstrate their relevance in experimental data.
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Neurophysiological Predictor of SMR-based BCI Performance
Benjamin Blankertz,Claudia Sannelli,Sebastian Halder,Eva Maria Hammer,Andrea Kübler,Andrea Kübler,Klaus-Robert Müller,Gabriel Curio,Thorsten Dickhaus +8 more
TL;DR: A neurophysiological predictor of BCI performance is proposed which can be determined from a two minute recording of a 'relax with eyes open' condition using two Laplacian EEG channels.
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Spatio-spectral filters for improving the classification of single trial EEG
TL;DR: This paper suggests an extension of CSP to the state space, which utilizes the method of time delay embedding, which allows for individually tuned frequency filters at each electrode position and yields an improved and more robust machine learning procedure.