@inproceedings{97dff11b02504557bec8d473df7abfa7,
title = "On the use of the Emotiv EPOC neuroheadset as a low cost alternative for EEG signal acquisition",
abstract = "This paper describes the use of the Emotiv EPOC as a relatively low cost method for acquiring raw EEG signals, and proposes an automatic eyewink interpretation system based on raw EEG signal analysis for human-machine interface as a possible aid for people with disabilities. To demonstrate the feasibility of using the EEG raw signals acquired by using the Emotiv system, an eyewinks' classification algorithm based on Artificial Neural Networks (ANN) was implemented as an example. The proposed algorithm has been found effective in detecting and classifying eyewinks that can then be translated to valid commands for human-machine interface. The performance of the proposed approach was studied using two types of ANN topologies. The results obtained indicated a high rate of classification accuracy, therefore, suggesting that the Emotiv EPOC can be used as a valid tool for research.",
keywords = "Brain Computer Interface, EEG, Eyewink Detection, Neural Network Classification",
author = "Benitez, {Diego S.} and Sebastian Toscano and Adrian Silva",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Colombian Conference on Communications and Computing, COLCOM 2016 ; Conference date: 27-04-2016 Through 29-04-2016",
year = "2016",
month = jul,
day = "18",
doi = "10.1109/ColComCon.2016.7516380",
language = "Ingl{\'e}s",
series = "2016 IEEE Colombian Conference on Communications and Computing, COLCOM 2016 - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Lorena Garcia",
booktitle = "2016 IEEE Colombian Conference on Communications and Computing, COLCOM 2016 - Conference Proceedings",
}