Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Modeling MPEG Coded Video Traffic by Markov-Modulated Self-Similar Processes
Journal of VLSI Signal Processing Systems
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Mutual information approach to blind separation of stationary sources
IEEE Transactions on Information Theory
Self-adaptive blind source separation based on activation functions adaptation
IEEE Transactions on Neural Networks
The Potential of the BCI for Accessible and Smart e-Learning
UAHCI '09 Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments
Discovering patterns in traffic sensor data
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming
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Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.