Learning of Non-stationarity multivariate functional processes:Application to EEG

Key Points

• The analysis reveals distinct patterns in EEG signals influenced by non-stationary variables, enhancing understanding of brain dynamics.
• Key findings indicate that non-stationary conditions significantly alter EEG signal structures, emphasizing the importance of adaptive methods.
• Assessment of multivariate functional processes utilizes advanced signal analysis techniques, addressing complex temporal dynamics in EEG.
• The findings may enable improved interpretations of EEG data, with implications for neurological disorder assessments and treatment planning.