A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals
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
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 StyleGóngora, 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