Publications by authors named "Nichita Bozga"
Article Synopsis
- The study explores how visual stimuli trigger visual evoked potentials in EEG signals, highlighting challenges in interpreting these signals due to mixed power variations and phase-locking mechanisms.
- The researchers propose that EEG data contains identifiable information about visual features, and they utilize advanced classification algorithms based on Riemannian geometry to analyze single-trial EEG data.
- Results reveal high classification accuracy for distinguishing between different visual images using surface EEG (84% inter-subject and 93% intra-subject), though classification based on sLORETA estimates struggles to generalize across subjects, indicating potential limitations in the method.
