Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Theoretical Derivation of the Kernel Extreme Energy Ratio Method for EEG Feature Extraction
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
A Theory of Kernel Extreme Energy Difference for Feature Extraction of EEG Signals
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Importance weighted extreme energy ratio for EEG classification
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Spatial filter selection with LASSO for EEG classification
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
A subject transfer framework for EEG classification
Neurocomputing
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Energy is an important feature for electroencephalogram (EEG) signal classification in brain computer interfaces (BCIs). It is not only physiologically rational but also empirically effective. This paper proposes extreme energy ratio (EER), a discriminative objective function to guide the process of spatially filtering EEG signals. The energy of the filtered EEG signals has the optimal discriminative capability under the EER criterion, and hence EER can as well be regarded as a feature extractor for distilling energy. The paper derives the solutions which optimize the EER criterion, shows the theoretical equivalence of EER to the existing method of common spatial patterns (CSP), and gives the computational savings EER makes in comparison with CSP. Two paradigms extending EER from binary classification to multi-class classification are also provided.