TY - GEN
T1 - Classification of EEG from Black Color Stimuli to Command a Remote-Controlled Car
T2 - 10th Computer Science and Electronic Engineering Conference, CEEC 2018
AU - Guillermo, Gerson
AU - De Lama, Bryan
AU - Flores, Christian
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/3/25
Y1 - 2019/3/25
N2 - In this manuscript, we present a pilot study to use the black color stimuli and resting state to wirelessly control a remote-controlled car. Power Spectral Density (PSD) was calculated on EEG signals to extract features and Multilayer Perceptron (MLP) was proposed to classify the EEG features using a 5-fold cross validation. Our results reported that best score classification was on 100% for Delta band using six electrodes and they allow to control a remote-controlled car. This approach is compared to other BCI paradigm and machine learning algorithms so that our results outperformed others works.
AB - In this manuscript, we present a pilot study to use the black color stimuli and resting state to wirelessly control a remote-controlled car. Power Spectral Density (PSD) was calculated on EEG signals to extract features and Multilayer Perceptron (MLP) was proposed to classify the EEG features using a 5-fold cross validation. Our results reported that best score classification was on 100% for Delta band using six electrodes and they allow to control a remote-controlled car. This approach is compared to other BCI paradigm and machine learning algorithms so that our results outperformed others works.
UR - http://www.scopus.com/inward/record.url?scp=85064381359&partnerID=8YFLogxK
U2 - 10.1109/CEEC.2018.8674232
DO - 10.1109/CEEC.2018.8674232
M3 - Conference contribution
AN - SCOPUS:85064381359
T3 - 2018 10th Computer Science and Electronic Engineering Conference, CEEC 2018 - Proceedings
SP - 198
EP - 201
BT - 2018 10th Computer Science and Electronic Engineering Conference, CEEC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 September 2018 through 21 September 2018
ER -