Journal ArticleDOI
Classification of EEG mental patterns by using two scalp electrodes and Mahalanobis distance-based classifiers.
Febo Cincotti,Donatella Mattia,Claudio Babiloni,Filippo Carducci,Luigi Bianchi,J. del R. Millan,J. Mourino,Serenella Salinari,M.G. Marciani,Fabio Babiloni +9 more
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TLDR
The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy by using only C3 and C4 electrodes, interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.Abstract:
Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.read more
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Journal ArticleDOI
On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications
Christos A. Frantzidis,Charalampos Bratsas,Manousos A. Klados,Evdokimos I. Konstantinidis,Chrysa Lithari,Ana B. Vivas,Christos Papadelis,Eleni Kaldoudi,Costas Pappas,Panagiotis D. Bamidis +9 more
TL;DR: It is envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions, and it is hereby discussed how future developments may be steered to serve for affective healthcare applications, such as the monitoring of the elderly or chronically ill people.
Book ChapterDOI
Brain–Computer Interfaces
TL;DR: Brain-computer interfaces (BCIs) are systems that give their users communication and control capabilities that do not depend on muscles.
PatentDOI
Brain-computer interface
Helge Bjarup Dissing Sørensen,Sadasivan Puthusserypady,Adnan Vilic,Troels Wessenberg Kjær,Carsten E. Thomsen +4 more
TL;DR: In this article, a computer-implemented method of providing an interface between a user and a processing unit is proposed, which consists of presenting one or more stimuli to a user, each stimulus varying at a respective stimulation frequency, each stimulation frequency being associated with a respective user-selectable input; receiving at least one signal indicative of brain activity of the user; and determining, from the received signal, which of the stimuli the user attends to and selecting the user selectable input associated with the stimulation frequency of the determined stimuli as being a userselected input.
Journal ArticleDOI
Random matrix analysis of human EEG data.
Petr Šeba,Petr Šeba +1 more
TL;DR: The spectral density as well as the level spacings was analyzed and shown to be generic and subject independent and the number variance distributions were investigated to show deviations from the random matrix prediction.
Journal ArticleDOI
High-resolution EEG techniques for brain-computer interface applications.
Febo Cincotti,Donatella Mattia,Fabio Aloise,S. Bufalari,Laura Astolfi,Fabrizio De Vico Fallani,A. Tocci,Luigi Bianchi,Maria Grazia Marciani,Shangkai Gao,José del R. Millán,Fabio Babiloni +11 more
TL;DR: This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.