5 papers
Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series
Pedram Rajaei, Maryam Ostadsharif Memar, Navid Ziaei +2
The manifold hypothesis suggests that high-dimensional neural time series lie on a low-dimensional manifold shaped by simpler underlying dynamics. To uncover this structure, latent…
A Combined Channel Approach for Decoding Intracranial EEG Signals: Enhancing Accuracy through Spatial Information Integration
Maryam Ostadsharif Memar, Navid Ziaei, Behzad Nazari
Intracranial EEG (iEEG) recording, characterized by high spatial and temporal resolution and superior signal-to-noise ratio (SNR), enables the development of precise brain-computer…
RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier
Maryam Ostadsharif Memar, Navid Ziaei, Behzad Nazari +1
Intracranial electroencephalography (iEEG) is increasingly used for clinical and brain-computer interface applications due to its high spatial and temporal resolution. However, int…
Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data
Navid Ziaei, Joshua J. Stim, Melanie D. Goodman-Keiser +4
Non-parametric models, such as Gaussian Processes (GP), show promising results in the analysis of complex data. Their applications in neuroscience data have recently gained tractio…
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data
Navid Ziaei, Behzad Nazari, Uri T. Eden +2
Extracting meaningful information from high-dimensional data poses a formidable modeling challenge, particularly when the data is obscured by noise or represented through different…