ArticleScientific reports2021
Eye-blink artifact removal from single channel EEG with k-means and SSA.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 68 citations in OpenAlex.
- Oracle Upper Bounds on Clean-EEG Recoverability from Single-Channel Decompositions Under EOG/EMG Contamination.Sensors (Basel, Switzerland) · 2026Article
- AQMFB-DWT: A Preprocessing Technique for Removing Blink Artifacts Before Extracting Pain-evoked Potential EEG.Neuroscience bulletin · 2025Article
- Eye-blink artifact removal in single-channel electroencephalogram using K-means and Savitzky Golay-singular Spectrum Analysis hybrid technique.Physical and engineering sciences in medicine · 2025Article
- Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG Data.Brain sciences · 2025Article
- EEG based real time classification of consecutive two eye blinks for brain computer interface applications.Scientific reports · 2025Article
- Artifact-reference multivariate backward regression (ARMBR): a novel method for EEG blink artifact removal with minimal data requirements.Journal of neural engineering · 2025Article
- Monitoring Sleep Quality Through Low α-Band Activity in the Prefrontal Cortex Using a Portable Electroencephalogram Device: Longitudinal Study.Journal of medical Internet research · 2025Article
- Dynamical Embedding of Single-Channel Electroencephalogram for Artifact Subspace Reconstruction.Sensors (Basel, Switzerland) · 2024Article
- Evaluating the effectiveness of artifact correction and rejection in event-related potential research.Psychophysiology · 2024Article
- Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification.Sensors (Basel, Switzerland) · 2024Article
- OVME-REG: Harris hawks optimization algorithm based optimized variational mode extraction for eye blink artifact removal from EEG signal.Medical & biological engineering & computing · 2024Article
- Evaluating the effectiveness of artifact correction and rejection in event-related potential research.bioRxiv : the preprint server for biology · 2023Article
- Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.Sensors (Basel, Switzerland) · 2023Article
- EEG Emotion Recognition Applied to the Effect Analysis of Music on Emotion Changes in Psychological Healthcare.International journal of environmental research and public health · 2022Article
- SSA with CWT andSensors (Basel, Switzerland) · 2022Article
- EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development.Computational intelligence and neuroscience · 2022Article
- Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ([Formula: see text]) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.