Denoising simulated EEG signals: A comparative study of EMD, wavelet transform and Kalman filter

Salis, Christos; Malissovas, Anastasios E.; Bizopoulos, Paschalis; Tzallas, Alexandros T.; Angelidis, Pantelis; Tsalikakis, Dimitrios G.

doi:10.1109/bibe.2013.6701613

Cited by 19 publications

(13 citation statements)

References 11 publications

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“…The residual ( ) is iterated 7. The filtered sequence ̂2( ) is obtained by summing up the necessary IMFs c) Stationary Wavelet Transform (SWT) based Filtering SWT is a type of wavelet transform used for denoising biomedical signals [12]. The Discrete Wavelet Transform (DWT) does not preserve translation invariance due to subsampling operations in the pyramidal algorithm.…”

Section: B) Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

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Evaluation of denoising techniques for EOG signals based on SNR estimation

Dasgupta

Chakrborty

Chaudhuri

et al. 2016

2016 International Conference on Systems in Medicine and Biology (ICSMB)

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This paper evaluates four algorithms for denoising raw Electrooculography (EOG) data based on the Signal to Noise Ratio (SNR). The SNR is computed using the eigenvalue method. The filtering algorithms are a) Finite Impulse Response (FIR) bandpass filters, b) Stationary Wavelet Transform, c) Empirical Mode Decomposition (EMD) d) FIR Median Hybrid Filters. An EOG dataset has been prepared where the subject is asked to perform letter cancelation test on 20 subjects.

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Section: B) Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

“…IV. SNR ESTIMATION An estimation of SNR, is required to evaluate the performance of the algorithms [12]. First, the noise from each algorithm is obtained as = ( ) −̂( ) (12) The noise covariance of the noise subspace by ( ) = ′ (13) Here is the vector = [ (0) (1) (2) … ( )]′.…”

Section: B) Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

Evaluation of denoising techniques for EOG signals based on SNR estimation

Dasgupta

Chakrborty

Chaudhuri

et al. 2016

2016 International Conference on Systems in Medicine and Biology (ICSMB)

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“…However, the wavelet thresholding approach (incorporated into the W-ICA step in HAPPE 1.0) provides an artifact rejection method that is suitable for low-density layouts, and preliminary work has found that wavelet thresholding outperforms ICA as an artifact removal approach on data with fourteen channels (Bajaj et al, 2020). Additionally, wavelet thresholding has been found to outperform other denoising methods that could apply to low-density data, including Empirical Mode Decomposition and Kalman filtering (Salis et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

HAPPILEE: The Harvard Automated Processing Pipeline In Low Electrode Electroencephalography, a standardized software for low density EEG and ERP data

Lopez

Monachino

Morales

et al. 2021

Preprint

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Low-density Electroencephalography (EEG) recordings (e.g. fewer than 32 electrodes) are widely-used in research and clinical practice and enable scalable brain function measurement across a variety of settings and populations. Though a number of automated pipelines have recently been proposed to standardize and optimize EEG preprocessing for high-density systems with state-of-the-art methods, few solutions have emerged that are compatible with low-density systems. However, low-density data often include long recording times and/or large sample sizes that would benefit from similar standardization and automation with contemporary methods. To address this need, we propose the HAPPE In Low Electrode Electroencephalography (HAPPILEE) pipeline as a standardized, automated pipeline optimized for EEG recordings with low density channel layouts of any size. HAPPILEE processes task-free (e.g. resting-state) and task-related EEG, and event-related potential (ERP) data, from raw files through a series of processing steps including filtering, line noise reduction, bad channel detection, artifact rejection from continuous data, segmentation, and bad segment rejection that have all been optimized for low density data. HAPPILEE also includes post-processing reports of data and pipeline quality metrics to facilitate the evaluation and reporting of data quality and processing-related changes to the data in a standardized manner. We describe multiple approaches with both recorded and simulated EEG data to optimize and validate pipeline performance. The HAPPILEE pipeline is freely available as part of HAPPE 2.0 software under the terms of the GNU General Public License at: https://github.com/PINE-Lab/HAPPE.

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“…WT has also been extensively utilized because this method can remove ocular artifact noise, eye blinking noise and cardiac artifacts [ 29 , 30 , 31 , 32 , 33 ]. Patel et al [ 34 ] conducted a comparative study to remove ocular artifacts by using WT and EMD methods; WT with minimum signal distortion is more efficient than EMD [ 35 ]. Discrete wavelet transform (DWT) has also been considered as a promising technique to represent EEG signal characteristics by extracting features from the sub-band of EEG signals [ 28 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

Al-Qazzaz

Ali

Ahmad

et al. 2015

Sensors

127

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We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.

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Denoising simulated EEG signals: A comparative study of EMD, wavelet transform and Kalman filter

Cited by 19 publications

References 11 publications

Evaluation of denoising techniques for EOG signals based on SNR estimation

Evaluation of denoising techniques for EOG signals based on SNR estimation

HAPPILEE: The Harvard Automated Processing Pipeline In Low Electrode Electroencephalography, a standardized software for low density EEG and ERP data

Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

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