Adaptive Filtering Approach with Forgetting Factor for Stochastic Signals Applied to EEG

Karen Alicia Aguilar-Cruz, Jose De Jesus Medel-Juarez, Maria Teresa Zagaceta-Alvarez, Rosaura Palma-Orozco, Romeo Urbieta-Parrazales

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

© 2013 IEEE. This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages-estimation, adaptive estimation, and adaptive estimation-identification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).
Idioma originalInglés estadounidense
Páginas (desde-hasta)101274-101283
Número de páginas10
PublicaciónIEEE Access
DOI
EstadoPublicada - 1 ene 2020

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