scholarly journals Motion artifact cancellation from a single channel SCG using adaptive forgetting factor recursive least square filter

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Shuai Yu ◽  
Qinglin Song ◽  
Sheng Liu
Keyword(s):  
Single Channel ◽  
Motion Artifact ◽  
Least Square ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Adaptive Forgetting Factor ◽  
Adaptive Forgetting

scholarly journals Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1733
Author(s):  
Hao Wang ◽  
Yanping Zheng ◽  
Yang Yu
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Unscented Kalman Filter ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Forgetting Factor ◽  
Soc Estimation ◽  
Online Identification ◽  
Adaptive Forgetting Factor ◽  
Adaptive Forgetting

In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

scholarly journals DASH Adaptation Algorithm Based on Adaptive Forgetting Factor Estimation

2018 ◽  
Vol 20 (5) ◽  
pp. 1224-1232 ◽  
Author(s):  
Miguel Aguayo ◽  
Luis Bellido ◽  
Carlos M. Lentisco ◽  
Encarna Pastor
Keyword(s):  
Forgetting Factor ◽  
Adaptation Algorithm ◽  
Adaptive Forgetting Factor ◽  
Adaptive Forgetting ◽  
Factor Estimation

scholarly journals Single channel approach for filtering EEG signals strongly contaminated with facial EMG

2020 ◽  
Author(s):  
Gustavo Moreira da Silva ◽  
Carlos Magno Medeiros Queiroz ◽  
Steffen Walter ◽  
Luciano Brink Peres ◽  
Luiza Maire David Luiz ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Single Channel ◽  
Least Square ◽  
Recursive Least Square ◽  
Eeg Signals ◽  
Denoising Method ◽  
Brain Rhythms ◽  
Facial Emg ◽  
Emg Signal ◽  
Mode Decomposition

Abstract Background Eliminating facial electromyography (EMG) from the electroencephalogram (EEG) is essential for the accuracy of applications such as brain computer interfaces (BCIs) and quantification of brain functionality. Although it is possible to find several studies that address EEG filtering, there is lack of researches that improve the filtering of EEG strongly corrupted by EMG signals with single-channel approaches, which are necessary in situations in which the number of available channels is reduced for the application of filtering methods based on multichannel techniques. In this context, this research proposes an EEG denoising method for filtering EMG from the masseter and frontal. This method, so-called EMDRLS, combines the use of Empirical Mode Decomposition (EMD) and a Recursive Least Square (RLS) filter to attenuate facial EMG noise from EEG. The results were compared with those obtained from Wavelet, EMD, Wiener and Wavelet-RLS (WRLS) filters. Besides the visual inspection of the resultant waveform of filtered signals, the following objective metrics were employed to contrast the performance of the filtering methods: (i) the signal-noise ratio (SNR) of the contaminated signal, (ii) the root mean square error (RMSE) between the power spectrum of artifact free and filtered EEG epochs, (iii) the spectral preservation of brain rhythms (i.e., delta, theta, alpha, beta, and gamma rhythms) of filtered signals.Results The EMDRLS method yielded filtered EEG signals with SNR varying from 0 to 10 dB for EEG signals with SNR below -10dB. The Spearman’s correlation coefficient estimated between the SNR of filtered and corrupted signals was below 0.04, suggesting, in the evaluated conditions, the independence of the EMDRLS filtering performance to the SNR of noisy signals. The technique also improved the RMSE between the power spectrum of artifact free and filtered EEG epochs by a factor of 27 (from 5.429 to 0.197) in the most corrupted EEG channels with the masseter muscle contraction. The Kruskal-Wallis test and the Tukey-Kramer post-hoc test (p < 0.05) confirmed the preservation of all brain rhythms given by EEG signals filtered with the EMDRLS method.Conclusions The results showed that the single-channel EMDRLS method can be applied to highly contaminated EEG signals by facial EMG signal with performance superior to that of the compared methods. The method can be applied for the offline filtering of EEG signals contaminated by facial EMG.

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