Varone, G.; Boulila, W.; Lo Giudice, M.; Benjdira, B.; Mammone, N.; Ieracitano, C.; Dashtipour, K.; Neri, S.; Gasparini, S.; Morabito, F.C.;
et al. A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls. Sensors 2022, 22, 129.
https://doi.org/10.3390/s22010129
AMA Style
Varone G, Boulila W, Lo Giudice M, Benjdira B, Mammone N, Ieracitano C, Dashtipour K, Neri S, Gasparini S, Morabito FC,
et al. A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls. Sensors. 2022; 22(1):129.
https://doi.org/10.3390/s22010129
Chicago/Turabian Style
Varone, Giuseppe, Wadii Boulila, Michele Lo Giudice, Bilel Benjdira, Nadia Mammone, Cosimo Ieracitano, Kia Dashtipour, Sabrina Neri, Sara Gasparini, Francesco Carlo Morabito,
and et al. 2022. "A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls" Sensors 22, no. 1: 129.
https://doi.org/10.3390/s22010129
APA Style
Varone, G., Boulila, W., Lo Giudice, M., Benjdira, B., Mammone, N., Ieracitano, C., Dashtipour, K., Neri, S., Gasparini, S., Morabito, F. C., Hussain, A., & Aguglia, U.
(2022). A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls. Sensors, 22(1), 129.
https://doi.org/10.3390/s22010129
