Time frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition

ALEGRE-CORTÉS J; SOTO-SANCHEZ C; PIZÁ AG; ALBARRACIN, AL; FARFÁN FD; FELICE CJ; FERNANDEZ E

JOURNAL OF NEUROSCIENCE METHODS

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2016

0165-0270

Background: Linear analysis has classically provided powerful tools for understandingthe behavior of neural populations, but the neuron responses to real-world stimulationare nonlinear under some conditions, and many neuronal components demonstratestrong nonlinear behavior. In spite of this, temporal and frequency dynamics of neuralpopulations to sensory stimulation have been usually analyzed with linear approaches.New method: In this paper, we propose the use of Noise-Assisted MultivariateEmpirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm,plus the Hilbert Transform as a suitable tool for analyzing population oscillatorydynamics in a multi-dimensional space with instantaneous frequency (IF) resolution.Results: The proposed approach was able to extract oscillatory information ofneurophysiological data of deep vibrissal nerve and visual cortex multiunit recordingsthat were not evidenced using linear approaches with fixed bases such as the Fourieranalysis.Comparison with existing methods: Texture discrimination analysis performance wasincreased when Noise-Assisted Multivariate Empirical Mode plus Hilbert Transformwas implemented, compared to linear techniques. Cortical oscillatory populationactivity was analyzed with precise Time-Frequency resolution. Similarly, NA-MEMDprovided increased Time-Frequency resolution of cortical oscillatory populationactivity.Conclusions: Noise-Assisted Multivariate Empirical Mode Decomposition plus HilbertTransform is an improved method to analyze neuronal population oscillatory dynamicsovercoming linear and stationary assumptions of classical methods.