TY - GEN
T1 - A Convolutional Neural Network Approach for a P300-based Brain-Computer Interface for Disabled and Healthy Subjects
AU - Flores, Christian
AU - Flores, Victor
AU - Achanccaray, David
AU - Andreu-Perez, Javier
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/3/25
Y1 - 2019/3/25
N2 - In this manuscript, we analyze different topologies of Convolutional Neural Networks (CNN) for classifying the P300 wave from an EEG signal. Also, we propose a selection criteria in order to improve the classification accuracy. In this study, the brain signals of healthy and disabled subjects were analyzed and four architectures were tested with different numbers of filters with the same dimensions. The results of the current work indicate that the best bitrate in disabled and healthy subjects was 14.14 and 25.44 bits per minute, respectively. Using target by block evaluation, the classification accuracy of 100% was obtained in healthy and disabled subjects. This approach is compared to various machine learning algorithms so that our results outperformed others works.
AB - In this manuscript, we analyze different topologies of Convolutional Neural Networks (CNN) for classifying the P300 wave from an EEG signal. Also, we propose a selection criteria in order to improve the classification accuracy. In this study, the brain signals of healthy and disabled subjects were analyzed and four architectures were tested with different numbers of filters with the same dimensions. The results of the current work indicate that the best bitrate in disabled and healthy subjects was 14.14 and 25.44 bits per minute, respectively. Using target by block evaluation, the classification accuracy of 100% was obtained in healthy and disabled subjects. This approach is compared to various machine learning algorithms so that our results outperformed others works.
UR - https://www.scopus.com/pages/publications/85064380819
U2 - 10.1109/CEEC.2018.8674229
DO - 10.1109/CEEC.2018.8674229
M3 - Conference contribution
AN - SCOPUS:85064380819
T3 - 2018 10th Computer Science and Electronic Engineering Conference, CEEC 2018 - Proceedings
SP - 192
EP - 197
BT - 2018 10th Computer Science and Electronic Engineering Conference, CEEC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th Computer Science and Electronic Engineering Conference, CEEC 2018
Y2 - 19 September 2018 through 21 September 2018
ER -