Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature selection and blind source separation in an EEG-based brain-computer interface
EURASIP Journal on Applied Signal Processing
International Journal of Computer Vision
OP-ELM: optimally pruned extreme learning machine
IEEE Transactions on Neural Networks
Feature selection for brain-computer interfaces
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms
Journal of Medical Systems
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Extracting knowledge from electroencephalographic (EEG) signals has become an increasingly important research area in biomedical engineering. In addition to its clinical diagnostic purposes, in recent years there have been many efforts to develop brain computer interface (BCI) systems, which allow users to control external devices only by using their brain activity. Once the EEG signals have been acquired, it is necessary to use appropriate feature extraction and classification methods adapted to the user in order to improve the performance of the BCI system and, also, to make its design stage easier. This work introduces a novel fast adaptive BCI system for automatic feature extraction and classification of EEG signals. The proposed system efficiently combines several well-known feature extraction procedures and automatically chooses the most useful features for performing the classification task. Three different feature extraction techniques are applied: power spectral density, Hjorth parameters and autoregressive modelling. The most relevant features for linear discrimination are selected using a fast and robust wrapper methodology. The proposed method is evaluated using EEG signals from nine subjects during motor imagery tasks. Obtained experimental results show its advantages over the state-of-the-art methods, especially in terms of classification accuracy and computational cost.