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

• Main Authors: Karen Alicia Aguilar-Cruz, Jose De Jesus Medel-Juarez, Maria Teresa Zagaceta-Alvarez, Rosaura Palma-Orozco, Romeo Urbieta-Parrazales
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Adaptive estimation; electroencephalogram; parameter estimation; signal processing algorithm; system identification
• Online Access: https://ieeexplore.ieee.org/document/9108253/

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).