Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
Advanced CORBA programming with C++
Advanced CORBA programming with C++
Extended ICA removes artifacts from electroencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A flexible brain-computer interface
A flexible brain-computer interface
A classification method based on generalized eigenvalue problems
Optimization Methods & Software - Systems Analysis, Optimization and Data Mining in Biomedicine
Nonparallel plane proximal classifier
Signal Processing
Classifiers fusion for EEG signals processing in human-computer interface systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
Least squares twin support vector hypersphere (LS-TSVH) for pattern recognition
Expert Systems with Applications: An International Journal
Generalized eigenvalue proximal support vector regressor
Expert Systems with Applications: An International Journal
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Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG) signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.