@inproceedings{73fef2228d484d16a6d1aea09b389e42,
title = "Feature extraction for BCIs based on electromagnetic source localization and Common Spatial Patterns",
abstract = "Brain-Computer Interfaces (BCIs) provide a way to communicate without movement and can offer significant clinical benefits. Electrical brain activity recorded using electroencephalography (EEG) can be automatically interpreted by supervised learning classifiers according to the descriptive features of the signal. This paper investigates the performance of novel feature extraction based on a signal source localization and Common Spatial Patterns (CSP) methods. The proposed approach was evaluated by the reference EEG dataset yielding an average classification accuracy of 74.6 \% for a chosen group of subjects. It is shown that CSP feature extraction performs significantly better when applied to source components compared to features from the original sensor domain.",
keywords = "Accuracy, Brain modeling, Electrodes, Electroencephalography, Feature extraction, Head",
author = "Aleksandr Zaitcev and Greg Cook and Wei Liu and Martyn Paley and Elizabeth Milne",
note = "Publisher Copyright: {\textcopyright} 2015 EurAAP.; 9th European Conference on Antennas and Propagation, EuCAP 2015 ; Conference date: 13-05-2015 Through 17-05-2015",
year = "2015",
month = may,
language = "English",
series = "2015 9th European Conference on Antennas and Propagation, EuCAP 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 9th European Conference on Antennas and Propagation, EuCAP 2015",
}