Eye Blink Artifacts Research Articles

Interpreting the latent space of a Convolutional Variational Autoencoder for semi-automated eye blink artefact detection in EEG signals

Electroencephalography (EEG) allows the investigation of brain activity. However, neural signals often contain artefacts, hindering signal analysis. For example, eye-blink artefacts are particularly challenging due to their frequency overlap with neural signals. Artificial intelligence, particularly Variational Autoencoders (VAE), has shown promise in EEG artefact removal. This research explores the design and application of Convolutional VAEs for automatically detecting and removing eye blinks in EEG signals. The latent space of CVAE, trained on EEG topographic maps, is used to identify latent components that are selective for eye blinks. Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) are employed to evaluate the discriminative performance of each latent component. The most discriminative component, determined by the highest AUC, is modified to eliminate eye blinks. The evaluation of artefact removal involves visual inspection and Pearson correlation index assessment of the original EEG signal and the reconstructed clean version, focusing on the Fp1 and Fp2 channels most affected by eye-blink artefacts. Results indicate that the proposed method effectively removes eye blinks without significant loss of information related to the neural signal, demonstrating Pearson correlation values around 0.60 for each subject. The contribution to the knowledge offered by this research study is the design and application of a novel offline pipeline for automatically detecting and removing eye blinks from multi-variate EEG signals without human intervention.

Feature Extraction from Regenerated EEG – A Better Approach for ICA Based Eye Blink Artifact Detection

Purpose: Independent Component Analysis (ICA) decomposition is a commonly used technique for eye blink artifact detection from Electroencephalogram (EEG) signals. Feature extraction from the decomposed ICs is a prime step for blink detection. This paper presents a new model of eye blink detection for ICA based approach, where the decomposed ICs are projected to their corresponding EEG segments (ReEEG), and feature extraction is performed on the ReEEG instead of the IC. ReEEG represents the eye blink activity more distinctly. Hence, ReEEG-based feature extraction is more potential in detecting eye blink artifacts than the traditional IC-based feature extraction.
 Materials and Methods: This paper employs twelve EEG features to substantiate the superiority of ReEEG over IC. Support Vector Machine (SVM) is used as a classifier. A dataset, having 2638 clinical EEG epochs, is employed. All the considered twelve features are extracted from ReEEG and fed to SVM one at a time for blink detection. Then the obtained results are compared with an IC-based model with the same features.
 Results: The comparison reveals the success of the proposed ReEEG-based blink detection approach over the traditional IC-based approach. Accuracy, precision, recall, and f1 scores are calculated as performance measuring metrics. For almost all features, ReEEG-based approach achieved up to 12.25% higher accuracy, 24.95% higher precision, 13.49% higher recall, and 12.89% higher f1 score than the IC-based traditional method.
 Conclusion: The proposed model will be useful for researchers in dealing with the eye blink artifacts of EEG signals with more efficacy.

OVME-REG: Harris hawks optimization algorithm based optimized variational mode extraction for eye blink artifact removal from EEG signal.

The electroencephalogram (EEG) recordings from the human brain are useful for detecting various brain syndromes. These recordings are typically contaminated by high amplitude eye blink artifacts, which leads to deliberate misinterpretation of the EEG signal. Recently, variational mode extraction (VME) has been used to detect eye blink artifacts. But, the VME performance is impacted by the balancing parameter and center frequency selection. Therefore, this research uses two metaheuristic algorithms, particle swarm optimization and Harris hawks optimization, to determine the optimal set of the VME parameters. In the proposed method, the optimized VME (OVME) extracts the desired mode to locate the eye blink artifactual intervals. Then, the regression analysis (REG) filters the identified artifactual intervals from short EEG data segments. The significance of the proposed OVME-REG algorithm is that it is adequate for determining the optimum values of the VME algorithm. The analysis is carried out on the CHB-MIT Scalp EEG, BCI Competition, and EEG motor movement/imagery datasets. The proposed OVME-REG method provides an improved performance for suppressing single and repeated eye blink artifacts as compared to the current approaches in terms of (a) high correlation coefficient (93.08%, 87.3%, 82.17%), respectively, (b) low value of RRMSE (0.379, 0.506, 0.502), respectively, (c) high SSIM (0.892, 0.842, 0.694), and (d) low computation time and better preservation of the EEG data.

Efficient automated method to extract EOG artifact by combining Circular SSA with wavelet and unsupervised clustering from single channel EEG

The emergence of affordable single-channel electroencephalography (EEG) headbands has introduced new possibilities for applications in brain–computer interface (BCI) systems and health monitoring. However, these EEG signals are often contaminated by electrooculogram (EOG) activity caused by eyelid blinking. This poses a challenge, especially in portable EEG devices that typically have limited or single EEG channels. The presence of eye-blink artifacts can mislead the diagnosis of brain state activity. Consequently, there is a growing demand to extract eye-blink artifacts from single-channel EEG signals. However, during the process of removing these artifacts from contaminated EEG signals, low-frequency artifact components in non-artifact regions are also inadvertently eliminated. In this work, an automated, robust and efficient framework is presented by combining Circular Singular Spectral Analysis (Ci_SSA) with wavelet analysis and unsupervised clustering. This framework aims to remove artifact segments while preserving the low-frequency content in non-artifact regions. Simulations were carried out on both synthetic and real data, and the results were compared with alternative methods. The performance of the proposed method is evaluated by metrics such as Relative Root Mean Square Error (RRMSE), Mean Absolute Error (MAE), Correlation Coefficient (CC) and Artifact Rejection Ratio (ARR) for synthetic signals, as well as Signal-to-Error Ratio (SER) and Artifact-to-Residue Ratio (AReR) for real EEG signals. After applying the proposed algorithm to filter the contaminated EEG signal, a noticeable enhancement in the detection accuracy of driver fatigue is observed as compared to the pre-filtering stage.

A Hardware-Based Configurable Algorithm for Eye Blink Signal Detection Using a Single-Channel BCI Headset.

Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human-computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain-computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software.

Dyadic boundary points based empirical wavelet transform for the elimination of eye movement and eye blink-based ocular artifacts from EEG signals

The movement of eyeballs and eye blinks produces ocular artifacts in the electroencephalogram (EEG) signal during recording. It is necessary to filter out these artifacts from the EEG signal for different biomedical applications. The state-of-the-art (SOTA) methods have less denoising performance to filter ocular artifacts from EEG signals. To overcome this, we have proposed a novel filter-bank-based hybrid approach in this paper to eliminate ocular artifacts from EEG signals. The optimal filter-bank based on the dyadic boundary points based empirical wavelet transform (DBPEWT) is introduced for the multiscale decomposition of EEG signal into various sub-band (SB) signals. The Savitzky–Golay-driven total variation (SGDTV) filter is formulated and applied to the contaminated EEG’s low-frequency range sub-band or first sub-band signal to obtain the filtered sub-band signal. The filtered EEG signal is calculated based on the linear combination of the filtered first sub-band signal evaluated from the SGDTV stage with other sub-band signals of the contaminated EEG signal. The filtering performance of the proposed approach is assessed using the mean absolute error (MAE) in the power spectrum (PS) for θ, α, and β-bands of contaminated and filtered EEG signals. The suggested approach is evaluated using the eye-blink, eye-movement-based ocular artifact-contaminated EEG signals from two public databases. Furthermore, the denoising performance of the proposed filter-bank approach is evaluated using the wearable EEG sensor signals from healthy and epilepsy subjects while performing physical activities such as sitting, walking, stairs, and running, respectively. The results reveal that the proposed approach has obtained the average MAE-PS values for (θ, α, and β) bands as (0.156, 0.308, and 0.201), and (0.081, 0.089, and 0.088), respectively for the removal of eye-blink and eye-movement artifacts from EEG signals. The suggested approach has obtained the average MAE-PS values for (θ, α, and β-bands) as (4.555±0.203, 0.057±0.026, 0.062±0.005), and (4.085±0.086, 0.068±0.043, 0.048±0.006) for healthy and epilepsy subjects during running activity to eliminate artifacts from the wearable sensor-based EEG signals. The denoising performance of the proposed approach has been compared with existing methods to remove eye-movement and eye-blink artifacts from EEG signals.

Dictionary Learning and Greedy Algorithms for Removing Eye Blink Artifacts from EEG Signals

Dictionary Learning and Greedy Algorithms for Removing Eye Blink Artifacts from EEG Signals

Eye blink artifact detection based on multi-dimensional EEG feature fusion and optimization

Eye blink is the most common artifact in electroencephalogram (EEG), which usually affects the performance of EEG-based applications, such as neurological aided diagnostic analysis. For low spatial resolution EEG signals, current methods generally lack of spatial filtering, leading to a degraded performance. In this paper, a novel eye blink artifact detection algorithm based on multiple EEG feature fusion and PSO optimization is proposed in a few-channel data environment. The forehead FP1 and FP2 electrodes EEGs are decomposed based on empirical mode decomposition (EMD) through autocorrelation coefficients for signal filtering. The EEG variance features are extracted by the Common Spatial Pattern (CSP) filtering to enhance the feature discrimination. The particle swarm optimization (PSO) combined with support vector machine (SVM) is applied for feature fusion and optimization. We evaluate the performance on real recorded EEG dataset by the Children’s Hospital of Zhejiang University School of Medicine (CHZU). There contain EEGs with eye blink artifacts of 20 subjects. The results show that the proposed method can achieve the highest accuracy, recall rate, precision and F1 value.

Quantitative EEG features and machine learning classifiers for eye-blink artifact detection: A comparative study

Ocular artifact, namely eye-blink artifact, is an inevitable and one of the most destructive noises of EEG signals. Many solutions of detecting the eye-blink artifact were proposed. Different subsets of EEG features and Machine Learning (ML) classifiers were used for this purpose. But no comprehensive comparison of these features and ML classifiers was presented. This paper presents the comparison of twelve EEG features and five ML classifiers, commonly used in existing studies for the detection of eye-blink artifacts. An EEG dataset, containing 2958 epochs of eye-blink, non-eye-blink, and eye-blink-like (non-eye-blink) EEG activities, is used in this study. The performance of each feature and classifier has been measured using accuracy, precision, recall, and f1-score. Experimental results reveal that scalp topography is the most potential among the selected features in detecting eye-blink artifacts. The best performing classifier is Artificial Neural Network (ANN) among the five classifiers. The combination of scalp topography and ANN classifier performed as the most powerful feature-classifier combination. However, it is expected that the findings of this study will help the future researchers to select appropriate features and classifiers in building eye-blink artifact detection models.

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal.

Electroencephalography (EEG) signals are disrupted by technical and physiological artifacts. One of the most common artifacts is the natural activity that results from the movement of the eyes and the blinking of the subject. Eye blink artifacts (EB) spread across the entire head surface and make EEG signal analysis difficult. Methods for the elimination of electrooculography (EOG) artifacts, such as independent component analysis (ICA) and regression, are known. The aim of this article was to implement the convolutional neural network (CNN) to eliminate eye blink artifacts. To train the CNN, a method for augmenting EEG signals was proposed. The results obtained from the CNN were compared with the results of the ICA and regression methods for the generated and real EEG signals. The results obtained indicate a much better performance of the CNN in the task of removing eye-blink artifacts, in particular for the electrodes located in the central part of the head.

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.

Effective removal of eye-blink artifacts in EEG signals with semantic segmentation

Effective removal of eye-blink artifacts in EEG signals with semantic segmentation

ICA With CWT and k-means for Eye-Blink Artifact Removal From Fewer Channel EEG.

In recent years, there has been an increase in the usage of consumer based EEG devices with fewer channel configuration. Although independent component analysis has been a popular approach for eye-blink artifact removal from multichannel EEG signals, several studies showed that there is a leak of neural information into the eye-blink artifact associated independent components (ICs). Furthermore, the leak increases as the number of input EEG channels decreases and leads to loss of valuable EEG information. To overcome this problem, we developed a new framework that combines ICA with continuous wavelet transform (CWT), k- means and singular spectrum analysis (SSA) methods. In contrast to the existing approaches, the artifact region in the identified eye-blink artifact IC is detected and suppressed rather than setting it to zero as in classical ICA. As most of the energy in the eye-blink artifact IC is concentrated in the artifact region, CWT and k- means algorithms exploits this feature to detect the eye-blink artifact region. Support vector machine (SVM) based classifier is finally designed for automatic detection of the eye blink artifact ICs. The performance of proposed method is evaluated on synthetic and two real EEG datasets for various EEG channels setting. Results highlight that for fewer channel EEG signals, the proposed method provides accurate separation without any neural information loss as compared to the existing methods.

Multidimensional Feature Optimization Based Eye Blink Detection Under Epileptiform Discharges.

Eye blink artifact detection in scalp electroencephalogram (EEG) of epilepsy patients is challenging due to its similar waveforms to epileptiform discharges. Developing an accurate detection method is urgent and critical. In this paper, we proposed a novel multi-dimensional feature optimization based eye blink artifact detection algorithm for EEGs containing rich epileptiform discharges. An unsupervised clustering algorithm based on smoothed nonlinear energy operator (SNEO) and variational mode extraction (VME) is proposed to detect epileptiform discharges in the frontal leads. Then, multi-dimensional time/frequency EEG features extracted from forehead electrodes (FP1 and FP2 channels) combining with the improved VME (IVME) threshold are derived for EEG representation. A variance filtering method is further applied for discriminative feature selection and a machine learning model is finally learned to perform detection. Experiments on EEGs of 16 subjects from the Children's Hospital of Zhejiang University School of Medicine (CHZU) show that our method achieves the highest average sensitivity, specificity and accuracy of 95.04, 89.52, and 93.01, respectively. That outperforms 5 recent and state-of-the-art (SOTA) eye blink detection algorithms. The proposed method is robust in eye blink artifact detection for EEGs containing high-frequency epileptiform discharges. It is also effective in dealing with individual differences in EEGs, which is usually ignored in conventional methods.

Eye Blink Artefact Removal of Single Frontal EEG Channel Algorithm using Ensemble Empirical Mode Decomposition and Outlier Detection

Recently, the emergence of various applications to use EEG has evolved the EEG device to become wearable with fewer electrodes. Unfortunately, the process of removing artefact becomes challenging since the conventional method requires an additional artefact reference channel or multichannel recording to be working. By focusing on frontal EEG channel recording, this paper proposed an alternative single-channel eye blink artefact removal method based on the ensemble empirical mode decomposition and outlier detection technique. The method removes the segment of the potential eyeblinks artefact on the residual of a pre-determined level of decomposition. An outlier detection technique is introduced to identify the peak of the eyeblink based on the extreme value of the residual signal. The results showed that the corrected EEG signal achieved high correlation, low RMSE and have small differences in PSD when compared to the reference clean EEG. Comparing with an adaptive Wiener filter technique, the corrected EEG signal by the proposed method had better signal-to-artefact ratio.

EEG Multipurpose Eye Blink Detector using convolutional neural network

The electrical signal emitted by the eyes movement produces a very strong artifact on EEG signal due to its close proximity to the sensors and abundance of occurrence. In the context of detecting eye blink artifacts in EEG waveforms for further removal and signal purification, multiple strategies where proposed in the literature. Most commonly applied methods require the use of a large number of electrodes, complex equipment for sampling and processing data. The goal of this work is to create a reliable and user independent algorithm for detecting and removing eye blink in EEG signals using CNN (convolutional neural network). For training and validation, three sets of public EEG data were used. All three sets contain samples obtained while the recruited subjects performed assigned tasks that included blink voluntarily in specific moments, watch a video and read an article. The model used in this study was able to have an embracing understanding of all the features that distinguish a trivial EEG signal from a signal contaminated with eye blink artifacts without being overfitted by specific features that only occurred in the situations when the signals were registered.

Development of Machine-Learning Algorithms for Recognition of Subjects' Upper Limb Movement Intention Using Electroencephalogram Signals.

This study aims to classify rest and upper limb movements execution and intention using electroencephalogram (EEG) signals by developing machine-learning (ML) algorithms. Five different MLs are implemented, including k-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). The EEG data from fifteen healthy subjects during motor execution (ME) and motor imagination (MI) are preprocessed with Independent Component Analysis (ICA) to reduce eye-blinking associated artifacts. A sliding window technique varying from 1 s to 2 s is used to segment the signals. The majority voting (MV) strategy is employed during the post-processing stage. The results show that the application of ICA increases the accuracy of MI up to 6%, which is improved further by 1-2% using the MV (p<0.05). However, the improvement in the accuracies is more significant in MI (>5%) than in ME (<1%), indicating a more significant influence of eye-blinking artifacts in the EEG signals during MI than ME. Among the MLs, both RF and SVM consistently produced better accuracies in both ME and MI. Using RF, the 2 s window size produced the highest accuracies in both ME and MI than the smaller window sizes.

Online detection and removal of eye blink artifacts from electroencephalogram

The most prominent type of artifact contaminating electroencephalogram (EEG) signals are the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG signal. Online identification and elimination of eye blink artifacts are crucial in applications such a Brain-Computer Interfaces (BCI), neurofeedback, and epilepsy diagnosis. In this paper, algorithms that combine unsupervised eye blink artifact detection (eADA) with modified Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed, i.e., FastEMD-CCA2 and FastCCA, to automatically identify eye blink artifacts and remove them in an online setting. The average accuracy, sensitivity, specificity, and error rate for eye blink artifact removal with FastEMD-CCA2 is 97.9%, 97.65%, 99.22%, and 2.1%, respectively, validated on a Hitachi dataset with 60 EEG signals, consisting of more than 5600 eye blink artifacts. FastCCA achieved an average of 99.47%, 99.44%, 99.74%, and 0.53% artifact removal accuracy, sensitivity, specificity, and error rate, respectively, validated on the Hitachi dataset too. FastEMD-CCA2 and FastCCA algorithms are developed and implemented in the C++ programming language, mainly to investigate the processing speed that these algorithms could achieve in a different medium. Analysis has shown that FastEMD-CCA2 and FastCCA took about 10.7 and 12.7 ms, respectively, on average to clean a 1-s length of EEG segment. As a result, they’re a viable option for applications that require online removal of eye blink objects from EEG signals.

Removal of eye blink artifacts from EEG signal using morphological modeling and orthogonal projection

Removal of eye blink artifacts from EEG signal using morphological modeling and orthogonal projection

Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

The human brain is considered as one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: a headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. This chip offers an advanced filtering technology that has a high noise immunity along with an embedded Bluetooth module using which the acquired signal is transferred wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in real-time and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI. The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The work presented here gives a basic understanding of the functionality of a BCI system, and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis.