Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fuzzy systems to process ECG and EEG signals for quantification of the mental workload
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Classifying EEG for brain computer interfaces using Gaussian processes
Pattern Recognition Letters
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Information Sciences: an International Journal
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A driver fatigue recognition model based on information fusion and dynamic Bayesian network
Information Sciences: an International Journal
Two-Layer Hidden Markov Models for Multi-class Motor Imagery Classification
WBD '10 Proceedings of the 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging
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An appropriate preprocessing of EEG signals is crucial to get high classification accuracy for Brain-Computer Interfaces (BCI). The raw EEG data are continuous signals in the time-domain that can be transformed by means of filters. Among them, spatial filters and selecting the most appropriate frequency-bands in the frequency domain are known to improve classification accuracy. However, because of the high variability among users, the filters must be properly adjusted to every user's data before competitive results can be obtained. In this paper we propose to use the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for automatically tuning the filters. Spatial and frequency-selection filters are evolved to minimize both classification error and the number of frequency bands used. This evolutionary approach to filter optimization has been tested on data for different users from the BCI-III competition. The evolved filters provide higher accuracy than approaches used in the competition. Results are also consistent across different runs of CMA-ES.