Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs.

ABSTRACT: Brain-Computer Interfaces (BCI) aim to bypass the peripheral nervous system to link the brain to external devices via successful modeling of decoding mechanisms. BCI based on electrocorticogram or ECoG represent a viable compromise between clinical practicality, spatial resolution, and signal quality when it comes to extracellular electrical potentials from local neuronal assemblies. Classic analysis of ECoG traces usually falls under the umbrella of Time-Frequency decompositions with adaptations from Fourier analysis and wavelets as its most prominent variants. However, analyzing such high-dimensional, multivariate time series demands for specialized signal processing and neurophysiological principles. We propose a generative model for single-channel ECoGs that is able to fully characteri

SUBMITTER: Loza CA 

PROVIDER: S-EPMC6882771 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature