Stochastic Generative Model and Signal Analysis of EEG

Electroencephalography (EEG) is the measurement of electrical activity generated by the brain from the scalp. It is known that characteristic waveform patterns appear in EEG depending on the brain state. For example, during sleep, characteristic waves such as spindles and K-complexes appear in EEG according to sleep depth. Additionally, when people with epilepsy have seizures, sharp waves such as spikes and sharp waves are observed. In this research, we are developing an approach based on stochastic generative models to accurately discover and analyze such characteristic waves in EEG. Based on the modeling approach for EMG signals, and by considering the multidimensionality and time-series characteristics of EEG, we aim to capture the characteristic changes in EEG that change moment by moment according to brain states.