Applications of Adaptive Filtering

Avalos, Juan-Gerardo; Sánchez, Juan C.; Velázquez, J. González

doi:10.5772/16873

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…The adaptive RLS filtering [35,52,53] is depicted in Figure 5. The input signal s[n] is composed of two components, i.e., EEG and EMG artifact.…”

Section: The Emdrls Filtering Methodsmentioning

confidence: 99%

“…The most prevalent family of algorithms for removing EEG artifacts is based on the method of least squares [19,34]. Adaptive filters vary in time because their parameters are continuously changing to meet a performance requirement [35].…”

Section: Introductionmentioning

confidence: 99%

“…In order to contribute to the research of facial EMG removal from EEG, this study proposes an EEG filtering method for facial EMG removal that is independent of an external reference noise and is suitable for low SNR (e.g., less than -10 dB) signals. Thus, the hypothesis to be tested is that a single-channel EMG reference signal, estimated from EMD, in combination with a Recursive Least Squares (RLS) filter [35,52,53] improves the filtering of EEG strongly contaminated with facial EMG. The results of the proposed method, so-called EMDRLS, were compared with those obtained from traditional methods such as the wavelet transform, Wiener filtering, EMD and hybrid filtering.…”

Section: Introductionmentioning

confidence: 99%

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

Silva

Queiroz

Walter

et al. 2020

Preprint

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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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“…The adaptive RLS filtering [35,52,53] is depicted in Figure 5. The input signal s[n] is composed of two components, i.e., EEG and EMG artifact.…”

Section: The Emdrls Filtering Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Silva

Queiroz

Walter

et al. 2020

Preprint

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“…The literature describes that the most prevalent family of algorithms for removing EEG artifacts is based on the method of least squares (Correa et al, 2007 ; Kim and Kim, 2018 ). Adaptive filters vary in time because their parameters are continuously changing to meet a performance requirement (Gerardo et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography

Queiroz

Silva

Walter

et al. 2022

Front. Comput. Neurosci.

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Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.

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Aumento en la efectividad de la identificación de cimas y pies en el pulso fotopletismográfico al reconstruirlo mediante filtrado adaptativo

Becerra-Luna

Martínez-Memije

Cartas-Rosado

et al. 2017

Archivos de Cardiología de México

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Applications of Adaptive Filtering

Cited by 8 publications

References 5 publications

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

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

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography

Aumento en la efectividad de la identificación de cimas y pies en el pulso fotopletismográfico al reconstruirlo mediante filtrado adaptativo

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