TY - GEN
T1 - EEG Signals processing two state discrimination using self-organizing maps
AU - Diaz-Sotelo, Wilber J.
AU - Roman-Gonzalez, Avid
AU - Vargas-Cuentas, Natalia I.
AU - Meneses-Claudio, Brian
AU - Zimic, Mirko
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - At present, there are many reasons why persons are affected in their ability to communicate with the society, so it is necessary to find an alternative communication channel for these people. The primary objective of this work is to process electroencephalographic (EEG) signals related to two specific mental task; which are also used to give Yes/No type short answers using signals produced by the brain. These signals come from two electrodes placed directly over the scalp. Obtained signals are related to specific commands or motion intention which can be used to generate an interaction channel for people-who have lost their standard capabilities of communication-with the society. Different processing methods for EEG signals were implemented, analyzed and classified in state of the art. In this paper, a Kohonen self-organizing map is proposed as the classifier. The obtained results give errors of 6% to 7%. The data used in this work was taken from the database of Universidad Peruana Caytano Heredia.
AB - At present, there are many reasons why persons are affected in their ability to communicate with the society, so it is necessary to find an alternative communication channel for these people. The primary objective of this work is to process electroencephalographic (EEG) signals related to two specific mental task; which are also used to give Yes/No type short answers using signals produced by the brain. These signals come from two electrodes placed directly over the scalp. Obtained signals are related to specific commands or motion intention which can be used to generate an interaction channel for people-who have lost their standard capabilities of communication-with the society. Different processing methods for EEG signals were implemented, analyzed and classified in state of the art. In this paper, a Kohonen self-organizing map is proposed as the classifier. The obtained results give errors of 6% to 7%. The data used in this work was taken from the database of Universidad Peruana Caytano Heredia.
KW - BCI
KW - Brain-computer interface
KW - SOM
KW - electroencephalogram
KW - yes/no question
UR - http://www.scopus.com/inward/record.url?scp=85062178666&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062178666&partnerID=8YFLogxK
U2 - 10.1109/ICA-ACCA.2018.8609745
DO - 10.1109/ICA-ACCA.2018.8609745
M3 - Conference contribution
AN - SCOPUS:85062178666
T3 - IEEE ICA-ACCA 2018 - IEEE International Conference on Automation/23rd Congress of the Chilean Association of Automatic Control: Towards an Industry 4.0 - Proceedings
BT - IEEE ICA-ACCA 2018 - IEEE International Conference on Automation/23rd Congress of the Chilean Association of Automatic Control
A2 - Duran-Faundez, Cristian
A2 - Lefranc, Gaston
A2 - Fernandez-Fernandez, Mario
A2 - Munoz, Carlos
A2 - Rubio, Ernesto
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Automation/23rd Congress of the Chilean Association of Automatic Control: Towards an Industry 4.0, ICA-ACCA 2018
Y2 - 17 October 2018 through 19 October 2018
ER -