A primer on coupled state-switching models for multiple interacting time series
State-switching models such as hidden Markov models or Markov-switching regression
models are routinely applied to analyse sequences of observations that are driven by …
models are routinely applied to analyse sequences of observations that are driven by …
Neural networks to recognize patterns in topographic images of cortical electrical activity of patients with neurological diseases
FGA de Meneses, AS Teles, M Nunes… - Brain Topography, 2022 - Springer
Software such as EEGLab has enabled the treatment and visualization of the tracing and
cortical topography of the electroencephalography (EEG) signals. In particular, the …
cortical topography of the electroencephalography (EEG) signals. In particular, the …
Combining EEG microstates with fMRI structural features for modeling brain activity
K Michalopoulos, N Bourbakis - International journal of neural …, 2015 - World Scientific
Combining information from Electroencephalography (EEG) and Functional Magnetic
Resonance Imaging (fMRI) has been a topic of increased interest recently. The main …
Resonance Imaging (fMRI) has been a topic of increased interest recently. The main …
Tractable maximum likelihood estimation for latent structure influence models with applications to eeg & ecog processing
S Karimi, MB Shamsollahi - IEEE Transactions on Pattern …, 2023 - ieeexplore.ieee.org
Brain signals are nonlinear and nonstationary time series, which provide information about
spatiotemporal patterns of electrical activity in the brain. CHMMs are suitable tools for …
spatiotemporal patterns of electrical activity in the brain. CHMMs are suitable tools for …