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C. Guger, A. Schlögl, C. Neuper, D. Walterspacher, Thomas Strein, G. Pfurtscheller (2001)
Rapid prototyping of an EEG-based brain-computer interface (BCI)IEEE Transactions on Neural Systems and Rehabilitation Engineering, 9
G. Krausz, R. Scherer, G. Korisek, G. Pfurtscheller (2003)
Critical Decision-Speed and Information Transfer in the “Graz Brain–Computer Interface”Applied Psychophysiology and Biofeedback, 28
C. Vidaurre, A. Schlögl, R. Cabeza, Reinhold Scherer, G. Pfurtscheller (2006)
A fully on-line adaptive BCIIEEE Transactions on Biomedical Engineering, 53
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How many people are able to operate an EEG-based brain-computer interface (BCI)?IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11
G. Pfurtscheller, C. Neuper, G. Muller, B. Obermaier, G. Krausz, A. Schlogl, Reinhold Scherer, B. Graimann, C. Keinrath, D. Skliris, M. Wortz, G. Supp, C. Schrank (2003)
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Tommi Nykopp, J. Heikkonen, M. Sams (2004)
Sequential Classification of Finger Movements from MEG Recordings
We present the result of on-line feedback Brain Computer Interface experiments using adaptive and non-adaptive feature extraction methods with an on-line adaptive classifier based on Quadratic Discriminant Analysis. Experiments were performed with 12 naïve subjects, feedback was provided from the first moment and no training sessions were needed. Experiments run in three different days with each subject. Six of them received feedback with Adaptive Autoregressive parameters and the rest with logarithmic Band Power estimates. The study was done using single trial analysis of each of the sessions and the value of the Error Rate and the Mutual Information of the classification were used to discuss the results. Finally, it was shown that even subjects starting with a low performance were able to control the system in a few hours: and contrary to previous results no differences between AAR and BP estimates were found. Wir zeigen Ergebnisse eines Brain-Computer Interfaces mit online-feedback wobei adaptive und nicht-adaptive Merkmalsextraktionsmethoden mit einem adaptiven, auf quadratischer Diskriminanzanalyse-basierender, Klassifikator verwendet wurden. Dazu wurden Experiment mit 12 untrainierten Versuchspersonen an jeweils 3 verschiedenen Tagen durchgeführt, wobei Feedback bereits mit Beginn der ersten Messung, d. h. ohne die Notwendigkeit einer zusätzlichen „Trainingssitzung” gegeben werden konnte. Jeweils sechs Personen erhielten Feedback basierend auf Adaptive Autoregressiven (AAR) Parametern und auf logarithmierten Bandleistungswerten (BP). In der Studie wurde eine „single-Trial” Analyse für jede Sitzung durchgeführt, wobei die Fehlerrate und die „Mutual Information” als Evaluierungsmerkmale verwendet wurden. Es konnte gezeigt werden, das selbst Personen mit anfänglich schlechter Performanz nach einigen Stunden in der Lage waren das System zu kontrollieren. Im Gegensatz zu früheren Ergebnissen konnten keine Performanz-Unterschiede zwischen AAR und BP Schätzwerten gefunden warden.
Biomedizinische Technik / Biomedical Engineering – de Gruyter
Published: Nov 1, 2005
Keywords: Adaptive Classification; BCI; AAR; BP; QDA
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