FastEMD–CCA algorithm for unsupervised and fast removal of eyeblink artifacts from electroencephalogram

Egambaram, Ashvaany; Badruddin, Nasreen; Asirvadam, Vijanth S.; Begum, Tahamina; Fauvet, Éric; Stolz, Christophe

doi:10.1016/j.bspc.2019.101692

Cited by 24 publications

(7 citation statements)

References 24 publications

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“…For removing eyeblink artefacts from simulated 32‐channel EEG data, the proposed system took 14.218 s, whereas the previous method took 37.894 s. Recent traditional methods are also employed to compare with the proposed method. The method based on DWT‐ICA [17], MWF [26], ASR [31], wavelet enhanced ICA [39] and FastEMD‐CCA [40] are compared with the proposed method for eyeblink correction in EEG data. The performance of the proposed method and other recently reported methods is given in Table 5.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed method is evaluated using the following performance metrics to compare with the other recently developed artefact removal methods ( [17,26,31,39,40]). Basically, there are no such tools for measuring the accuracy of denoising eyeblink artefact of EEG signal.…”

Section: Performance Measurementmentioning

confidence: 99%

“…Issa and Juhasz [39] improved the eye blink removal method using wavelet enhanced ICA technique. More recently, Egambaram et al [40] proposed FastEMD-CCA algorithm for unsupervised and fast removal of eyeblink artefacts from the EEG signal. In their method, eyeblink artifact regions were automatically identified and an artefacted template was extracted through EMD then the eyeblink was removed via elimination of CCA components based on cross-correlation with the artefacted template.…”

Section: Introductionmentioning

confidence: 99%

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Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder

Phadikar

Sinha

Ghosh

2020

IET signal process.

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This study proposes a novel combination of independent component analysis (ICA) in conjunction with support vector machine (SVM) and denoising autoencoder (DA), for the first time, for removal of eyeblink artefacts from the corrupted electroencephalography (EEG). At first the eyeblink corrupted EEG signals are decomposed into independent components (ICs) using ICA, the corrupted-ICs are then identified using SVM as a classifier. From the corrupted-ICs, the artefacted segment is identified with a second SVM classifier and corrected by the pre-trained DA. Finally, inverse-ICA operation is applied on the remaining ICs and the corrected ICs to obtain the artefact-free EEG signal. The proposed methodology modifies only the portion corrupted with artefacts, and does not alter the uncorrupted part, thereby preserving the neural information in the original EEG. The proposed methodology was implemented to remove eyeblinks from the EEG data collected from the publicly available EEGLab data set. The results reveal that the proposed methodology is superior to the other recently reported methods in terms of the mutual information and average correlation coefficient. Further, the proposed method is automatic and does not require any intervention of the operator, whereas the other methods require intervention of the user.

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Section: Resultsmentioning

confidence: 99%

Section: Performance Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder

Phadikar

Sinha

Ghosh

2020

IET signal process.

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“…CCA decomposes the source into the greatest degree of autocorrelation and noncorrelation. CCA is the fastest of the common de-noising methods [43]. CCA assumes that the components are uncorrelated.…”

Section: Discussionmentioning

confidence: 99%

AOAR: an automatic ocular artifact removal approach for multi-channel electroencephalogram data based on non-negative matrix factorization and empirical mode decomposition

Chen

et al. 2021

J. Neural Eng.

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Objective. Electroencephalogram (EEG) signals suffer inevitable interference from artifacts during the acquisition process. These artifacts make the analysis and interpretation of EEG data difficult. A major source of artifacts in EEGs is ocular activity. Therefore, it is important to remove ocular artifacts before further processing the EEG data. Approach. In this study, an automatic ocular artifact removal (AOAR) method for EEG signals is proposed based on non-negative matrix factorization (NMF) and empirical mode decomposition (EMD). First, the amplitude of EEG data was normalized in order to ensure its non-negativity. Then, the normalized EEG data were decomposed into a set of components using NMF. The components containing ocular artifacts were extracted automatically through the fractal dimension. Subsequently, the temporal activities of these components were adaptively decomposed into some intrinsic mode functions (IMFs) by EMD. The IMFs corresponding to ocular artifacts were removed. Finally, the de-noised EEG data were reconstructed. Main results. The proposed method was tested against seven other methods. In order to assess the effectiveness and reliability of the AOAR method in processing EEG data, experiments on ocular artifact removal were performed using simulated EEG data. Experimental results indicated that the proposed method was superior to the other methods in terms of root mean square error, signal-to-noise ratio (SNR) and correlation coefficient, especially in cases with a lower SNR. To further evaluate the potential applications of the proposed method in real life, the proposed method and others were applied to preprocess real EEG data recorded from children with and without attention-deficit/hyperactivity disorder (ADHD). After artifact rejection, the event-related potential feature was extracted for classification. The AOAR method was best at distinguishing the children with ADHD from the others. Significance. These results indicate that the proposed AOAR method has excellent prospects for removing ocular artifacts from EEG data.

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“…These artifacts can affect EEG signals and interfere with relevant or dominant potentials in ERPs. Thus, many previous studies have sought to remove artifacts, such as muscular activity ( Chen et al, 2019 ; Zou et al, 2020 ), cardiac activity ( Hamaneh et al, 2014 ; Dai et al, 2019 ), eyeblinks, and ocular movements ( Dimigen, 2020 ; Egambaram et al, 2020 ). The effect of noise on ERP analysis has been minimized thanks to the development of methods to remove noise in EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Anti-Heartbeat-Evoked Potentials Performance in Event-Related Potentials-Based Mental Workload Assessment

Park

Kim

2021

Front. Physiol.

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The aim of this study was to determine the effect of heartbeat-evoked potentials (HEPs) on the performance of an event-related potential (ERP)-based classification of mental workload (MWL). We produced low- and high-MWLs using a mental arithmetic task and measured the ERP response of 14 participants. ERP trials were divided into three conditions based on the effect of HEPs on ERPs: ERPHEP, containing the heartbeat in a period of 280–700ms in ERP epochs after the target; ERPA-HEP, not including the heartbeat within the same period; and ERPT, all trials including ERPA-HEP and ERPHEP. We then compared MWL classification performance using the amplitude and latency of the P600 ERP among the three conditions. The ERPA-HEP condition achieved an accuracy of 100% using a radial basis function-support vector machine (with 10-fold cross-validation), showing an increase of 14.3 and 28.6% in accuracy compared to ERPT (85.7%) and ERPHEP (71.4%), respectively. The results suggest that evoked potentials caused by heartbeat overlapped or interfered with the ERPs and weakened the ERP response to stimuli. This study reveals the effect of the evoked potentials induced by heartbeats on the performance of the MWL classification based on ERPs.

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FastEMD–CCA algorithm for unsupervised and fast removal of eyeblink artifacts from electroencephalogram

Cited by 24 publications

References 24 publications

Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder

Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder

AOAR: an automatic ocular artifact removal approach for multi-channel electroencephalogram data based on non-negative matrix factorization and empirical mode decomposition

Anti-Heartbeat-Evoked Potentials Performance in Event-Related Potentials-Based Mental Workload Assessment

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