Investigating the temporal dynamics of electroencephalogram (EEG) microstates using recurrent neural networks.

ABSTRACT: Electroencephalogram (EEG) microstates that represent quasi-stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non-Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long-short-term-memory (LSTM) units to capture the complex temporal dependencies. Using an LSTM-based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time s

SUBMITTER: Sikka A 

PROVIDER: S-EPMC7267981 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature