A New Method for Automatic Electrooculogram and Eye Blink Artifacts Correction of EEG Signals using CCA and NAPCT

Sheoran, Poonam; Saini, J.S.

doi:10.1016/j.procs.2020.03.386

Cited by 16 publications

(9 citation statements)

References 18 publications

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“…Therefore, removal of ocular artifacts is a key stepbefore further analysis of EEG data. In recent years, researchers have proposed various methods for removal of ocular artifacts [39][40][41][42]. Although these methods work well to remove ocular artifacts they still have some disadvantages.…”

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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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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“…These artifacts are easy to use for testing a developed EEG device [18]. Sheoran [19] detailed how to detect it and how to deal with this artifact. In our experiments, the blinking and chewing artifact was strongly distinguished against the background of the EEG signal (Figure 3).…”

Section: Artefactsmentioning

confidence: 99%

Low-Cost Shield ardEEG to Measure EEG with Arduino Uno R4 WiFi

Rakhmatulin

2024

Preprint

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The article presents an accessible route into the field of neuroscience through ardEEG device. This device allows convert Arduino UNO R4 WIFI into a brain-computer interface, making it easy to measure EEG, EMG and ECG signals with 8 channels. Over the past decade, the proliferation of artificial intelligence has significantly impacted various industries, including neurobiology. The integration of machine learning techniques has opened avenues for practical applications of EEG signals across technology sectors. This surge in interest has led to the widespread popularity of low-cost brain-computer interface devices capable of recording EEG signals using non-invasive electrodes. ardEEG device demonstrates satisfactory noise levels and accuracy for use in applied tasks. Moreover, it successfully detects alpha brain waves, showcasing its effectiveness in capturing essential neurological signals

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“…In the context of EEG signals contaminated with muscle artifacts, canonical correlation analysis (CCA) is generally more effective than ICA [ 69 ]. Due to the relatively lower autocorrelation of muscular artifacts compared to brain activity, it is feasible to employ canonical correlation analysis (CCA) as a means of distinguishing between muscle activity and brain activity [ 70 ].…”

Section: The Pipeline Of Eeg Signal Analysismentioning

confidence: 99%

“…In [ 69 ], P Sheoran et al. proposed a new algorithm that combined CCA and noise adjusted principal component transform (NAPCT) to eliminate noise in EEG data.…”

Section: The Pipeline Of Eeg Signal Analysismentioning

confidence: 99%

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

Chaddad

Kateb

et al. 2023

Sensors

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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.

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A New Method for Automatic Electrooculogram and Eye Blink Artifacts Correction of EEG Signals using CCA and NAPCT

Cited by 16 publications

References 18 publications

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

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

Low-Cost Shield ardEEG to Measure EEG with Arduino Uno R4 WiFi

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

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