Góngora, L.; Paglialonga, A.; Mastropietro, A.; Rizzo, G.; Barbieri, R.
A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals. Sensors 2022, 22, 4747.
https://doi.org/10.3390/s22134747
AMA Style
Góngora L, Paglialonga A, Mastropietro A, Rizzo G, Barbieri R.
A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals. Sensors. 2022; 22(13):4747.
https://doi.org/10.3390/s22134747
Chicago/Turabian Style
Góngora, Leonardo, Alessia Paglialonga, Alfonso Mastropietro, Giovanna Rizzo, and Riccardo Barbieri.
2022. "A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals" Sensors 22, no. 13: 4747.
https://doi.org/10.3390/s22134747
APA Style
Góngora, L., Paglialonga, A., Mastropietro, A., Rizzo, G., & Barbieri, R.
(2022). A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals. Sensors, 22(13), 4747.
https://doi.org/10.3390/s22134747