TY - GEN
T1 - Training with a Neurofeedback System for the Control of a Drone Using Electroencephalographic Signals
AU - Gonzalez-Munoz, Leasly Atzuri
AU - Orozco-Magdaleno, Luis
AU - Ortiz-Yescas, Vania Alice
AU - Octavio Ramirez-Morales, Adrian
AU - Tovar-Corona, Blanca
AU - Zacatelco-Barrios, Luis Brayan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the present work, a portable electroencephalography system was developed in order to monitor the attention coefficient during a neurofeedback training using a drone as a visual stimulus. A four channels EEG signal acquisition system was developed using the 10-20 international standard, positioned at FP1, FP2, F3 and F4. The Power Spectral Density was calculated using the Modified Covariance method, and the attention coefficient was calculated using the ratio of the area under the curve between the high Beta and Theta bands. With the help of the Neurofeedback system and the drone, a series of eight tests of ten minutes each was carried out with a group of eleven volunteers. Only one session per day was carried out. In each test, the attention coefficient was calculated while the subject watched the drone performing one of the 16 determined trajectories. If the attention coefficient exceeded a threshold, the drone could continue moving, otherwise the drone remained static. As a result of using this neurofeedback system, it was found an increase in sustained attention time from 7.7 to 9.4 s, as well as a reduction in inattention time from 6.0 to 4.8 s.
AB - In the present work, a portable electroencephalography system was developed in order to monitor the attention coefficient during a neurofeedback training using a drone as a visual stimulus. A four channels EEG signal acquisition system was developed using the 10-20 international standard, positioned at FP1, FP2, F3 and F4. The Power Spectral Density was calculated using the Modified Covariance method, and the attention coefficient was calculated using the ratio of the area under the curve between the high Beta and Theta bands. With the help of the Neurofeedback system and the drone, a series of eight tests of ten minutes each was carried out with a group of eleven volunteers. Only one session per day was carried out. In each test, the attention coefficient was calculated while the subject watched the drone performing one of the 16 determined trajectories. If the attention coefficient exceeded a threshold, the drone could continue moving, otherwise the drone remained static. As a result of using this neurofeedback system, it was found an increase in sustained attention time from 7.7 to 9.4 s, as well as a reduction in inattention time from 6.0 to 4.8 s.
KW - Attention Coefficient
KW - Brainwaves
KW - EEG
KW - Modified Covariance
KW - Neurofeedback
KW - Spectral Power Density
UR - http://www.scopus.com/inward/record.url?scp=85146236020&partnerID=8YFLogxK
U2 - 10.1109/CCE56709.2022.9975977
DO - 10.1109/CCE56709.2022.9975977
M3 - Contribución a la conferencia
AN - SCOPUS:85146236020
T3 - CCE 2022 - 2022 19th International Conference on Electrical Engineering, Computing Science and Automatic Control
BT - CCE 2022 - 2022 19th International Conference on Electrical Engineering, Computing Science and Automatic Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2022
Y2 - 9 November 2022 through 11 November 2022
ER -