Electrooculogram Artifacts Research Articles

EOG Artifacts Suppression From single channel EEG Signals by VME-GMETV model

Nowadays the usage of portable electroencephalography (EEG) recorded systems are raised for recording of neuro activities of the brain, which are useful in clinical diagnosis such as brain–computer interface (BCI) applications. The eye movements or blinks are unavoidable activities, which reflects the electrooculogram (EOG) artifacts in the recorded EEG signals, which leads deceptive interpretation of the underlying brain state. In this paper a novel framework is proposed as Variational Mode Extraction (VME) method is integrated with Generalized Moreau Envelope Total Variation (GMETV) technique to filter the EOG artifacts. Initially, the contaminated EEG is applied to the VME method which extracts the EOG artifact segment. Then, this EOG artifact segment is given to the GMETV technique and it separates the wanted component of the EEG residue from the EOG artifact segment. The EEG residue is added back to the non EOG artifact segment signal which gives the final denoised EEG. Matlab simulations are done on both simulated and real time EEG databases to evolute the proposed VME-GMETV technique performance in terms of Relative Root Mean Square Error (RRMSE), Correlation Coefficient (CC) and Mean Absolute Error (MAE). From the experimental results, the proposed technique is outperformed the existing methods by averaged low RRMSE (0.1557), MAE (5.0714e−04) and averaged high CC (0.9695).

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.

MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals.

Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG's usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models' performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics.

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.

Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.

SNOAR: a new regression approach for the removal of ocular artifact from multi-channel electroencephalogram signals.

Electroencephalogram (EEG) signals are often corrupted by undesirable sources like electrooculogram (EOG) artifacts, which have a substantial impact on the performance of EEG-based systems. This study proposes a new singular spectrum analysis (SSA)-non-negative matrix factorization (NMF)-based ocular artifact removal (SNOAR) method to suppress ocular artifacts from multi-channel EEG signals. First, SSA was used to estimate EOG artifacts using a small subset of frontal electrodes. Then, NMF was applied to decompose the estimated EOG artifacts into vertical EOG (VEOG) and horizontal EOG (HEOG) signals. Finally, a simple linear regression with estimated VEOG and HEOG signals was used to remove artifacts from multi-channel EEG signals. EEG recordings from two EEG datasets (Klados dataset and KARA ONE) were used to evaluate the efficiency of the proposed method. From the simulation results, it was observed that the proposed method achieved betters results in terms of low root-mean-square error (RMSE), low delta band energy ratio, and less power spectral density (PSD) difference between the original clean EEG signal and its filtered version of contaminated EEG signal compared to selected EOG artifact removal methods (independent component analysis (ICA), wavelet-enhanced ICA (wICA), improved wICA, and multivariate empirical mode decomposition (MEMD)).

Single-channel EEG signal extraction based on DWT, CEEMDAN, and ICA method.

In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact removal requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems.

Removing EOG Artifacts from the EEG signal of Methamphetamine Addicts.

EEG can be used to characterize the electrical activity of the cerebral cortex, but it is also susceptible to interference. Compared with the other artifacts, Electrooculogram (EOG) artifacts have a greater impact on EEG processing and are more difficult to remove. Here, we mainly compared the regression and ICA algorithms both based on the EOG channels for the effect of removing EOG artifacts in the Stroop experiment of methamphetamine addicts. From the perspective of time domain and power spectral density, the ICA algorithm based on the EOG channels is more effective. However, the regression algorithm based on the EOG channels is less complex, more time-saving, and more suitable for real-time tasks.Clinical Relevance- For clinical purposes, this research has a certain reference value for selecting appropriate methods of removing EOG artifacts when processing the EEG of methamphetamine addicts.

A Novel Method Based on Combination of Independent Component Analysis and Ensemble Empirical Mode Decomposition for Removing Electrooculogram Artifacts From Multichannel Electroencephalogram Signals

Electrooculogram (EOG) is one of common artifacts in recorded electroencephalogram (EEG) signals. Many existing methods including independent component analysis (ICA) and wavelet transform were applied to eliminate EOG artifacts but ignored the possible impact of the nature of EEG signal. Therefore, the removal of EOG artifacts still faces a major challenge in EEG research. In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals. First, the ICA method was used to decompose original EEG signals into multiple independent components (ICs), and the EOG-related ICs were automatically identified through the kurtosis method. Then, by performing the EEMD algorithm on EOG-related ICs, the intrinsic mode functions (IMFs) linked to EOG were discriminated and eliminated. Finally, artifact-free IMFs were projected to obtain the ICs without EOG artifacts, and the clean EEG signals were ultimately reconstructed by the inversion of ICA. Both EOGs correction from simulated EEG signals and real EEG data were studied, which verified that the proposed method could achieve an improved performance in EOG artifacts rejection. By comparing with other existing approaches, the EICA obtained the optimal performance with the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal, which demonstrated that the proposed method could more effectively eliminate blink artifacts from multichannel EEG signals with less error influence. This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis.

An improved MAMA-EMD for the automatic removal of EOG artifacts

The separation of electrooculogram (EOG) and electroencephalogram (EEG) is a potential problem in brain-computer interface (BCI). Especially, it is necessary to accurately remove EOG, as a disturbance, from the measured EEG in brain disease diagnosis, EEG-based rehabilitation systems, etc. Due to the interaction between the eye and periocular musculature, a multipoint spike is often produced in EEG for each ocular activity. Masking-aided minimum arclength empirical mode decomposition (MAMA-EMD) was developed to robustly decompose time series with impulse-like noise. However, the decomposition performance of MAMA-EMD was limited in the case of one impulse with multiple contiguous spike points. In this paper, MAMA-EMD was improved (called IMAMA-EMD) by supplementing the minimum arclength criterion, and it was combined with kernel independent component analysis (KICA), yielding an automatic EOG artifact removal method, denoted as KIIMME. The multi-channel contaminated EEG signals were separated into several independent components (ICs) by KICA. Then, IMAMA-EMD was applied to the EOG-related ICs decomposition to generate a set of inherent mode functions (IMFs), the low frequency ones, which have higher correlation with EOG components, were removed, and the others were employed to construct ‘clean’ EEG. The proposed KIIMME was evaluated and compared with other methods on semisimulated and real EEG data. Experimental results demonstrated that IMAMA-EMD effectively eliminated the influence of multipoint spike on sifting process, and KIIMME improved the removal accuracy of EOG artifacts from EEG while retaining more useful neural data. This improvement is of great significance to research on brain science as well as BCI.

Research on automatic removal of ocular artifacts from single channel electroencephalogram signals based on wavelet transform and ensemble empirical mode decomposition

The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the 'clean' EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.

Removal of EOG artifacts and separation of different cerebral activity components from single channel EEG—An efficient approach combining SSA–ICA with wavelet thresholding for BCI applications

The electroencephalogram (EEG) signals are usually interfered by many sources of noise like electrooculogram (EOG), which degrades the signals of interest. It causes the poor performance of the Brain–Computer Interface (BCI) systems. In this work, the problem of removal of EOG artifacts and separation of different cerebral activities found in a single-channel contaminated EEG is addressed. For this purpose, a novel model, based on the combined use of Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) with a Stationary Wavelet Transform (SWT) is presented. ICA technique is a highly efficient method, which deals with the multichannel EEG signals. But it is difficult to apply the ICA on single channel EEG. Hence, using SSA, the single channel contaminated EEG signals are converted into multivariate information. Then, the multivariate information is fed to ICA, which separates the source signals as different independent components (ICs). Despite the fact that the ICA method performs excellent source separation, still, some required EEG signal content is present in the IC representing itself as an artifact, and thus dropping it would cause loss of EEG signal content. To avoid this problem, SWT is applied on the artifact IC, which performs the thresholding, to separate the actual artifact and preserve the EEG signal content. Matlab simulations have been done on both synthetically generated and real-life EEG signals and the proposed model is compared with the existing works. It is demonstrated that the proposed model has the best artifact separation performance than all the existing techniques, which is shown in terms of the metrics, RRMSE (Relative Root Mean Square Error) and MAE (Mean Absolute Error).

Removal of EOG artifacts from single channel EEG – An efficient model combining overlap segmented ASSA and ANC

Electroencephalogram (EEG) signals are mostly interfered by electrooculogram (EOG) artifacts. These artifacts degrade the performance of portable or wearable EEG recording systems. In this work, overlap segmented adaptive singular spectrum analysis (Ov-ASSA) combined with adaptive noise canceler (ANC) technique is presented for removal of EOG artifacts. Depending on the amplitude of the EEG signal, the first one or two reconstructed components of Ov-SSA technique are adaptively grouped and considered as a reference EOG signal for ANC in a single channel EEG recording system. In order to demonstrate the performance of the proposed technique, Matlab simulations are done on both synthetic and real EEG data. The synthetic EEG data is derived using the Markov Process Amplitude (MPA) EEG model. The performance metrics namely, RRMSE (relative root mean square error) and MAE (Mean Absolute Error) of proposed Ov-ASSA-ANC model outperformed the existing techniques. In addition to the removal of EOG artifacts, the proposed Ov-ASSA-ANC technique was also applied for seizure detection and an average accuracy of 98.05% was achieved.

Automated artifact rejection algorithms harm P3 Speller brain-computer interface performance

ABSTRACTBrain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, noninvasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance reduction are proposed.

Automated elimination of EOG artifacts in sleep EEG using regression method

Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%– 1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.

Remove Diverse Artifacts Simultaneously From a Single-Channel EEG Based on SSA and ICA: A Semi-Simulated Study

Electroencephalogram (EEG) signals are often contaminated with diverse artifacts, such as electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. These artifacts make subsequent EEG analysis inaccurate and prevent practical usage. Recently, the use of wearable EEG devices in ambulatory systems has been developed. For practical reasons, these systems usually contain a single EEG channel. Several studies have proposed to combine single-channel decomposition methods with blind source separation (BSS) methods to denoise the single-channel EEG. However, the existing methods have their own limitations since most of them only focus on removing one single kind of artifacts. Unfortunately, the EEG is prone to be contaminated by various kinds of artifacts simultaneously. Yet to our knowledge, there are no existing methods to remove diverse artifacts simultaneously from the single-channel EEG. To address this issue, we propose an effective method to remove diverse artifacts simultaneously for the single-channel EEG case. This method is a combination of singular spectrum analysis (SSA) and second-order blind identification (SOBI) method. We conduct a semi-simulated study to investigate all possible cases of the single-channel EEG been contaminated by EMG, EOG, and ECG artifacts. The results show that the proposed method can successfully remove diverse artifacts from the single-channel EEG. It is a promising tool for biomedical signal processing applications.

Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition

Electrooculogram (EOG) is one of the major artifacts in the design of electroencephalogram (EEG)-based brain computer interfaces (BCIs). That removing EOG artifacts automatically while retaining more neural data will benefit for further feature extraction and classification. In order to remove EOG artifacts automatically as well as reserve more useful information from raw EEG, this paper proposes a novel blind source separation method called CCA-EEMD (canonical correlation analysis, ensemble empirical mode decomposition). Technically, the major steps of CCA-EEMD are as follows: Firstly, the multiple-channel original EEG signals are separated into several uncorrelated components using CCA. Then, the EOG component can be identified automatically by its kurtosis value. Next, the identified EOG component is decomposed into several intrinsic mode functions (IMFs) by EEMD. The IMFs uncorrelated to the EOG component are recognized and retained, and a new component will be constructed by the retained IMFs. Finally, the clean EEG signals are reconstructed. Keep in mind that the novelty of this paper is that the identified EOG component is not removed directly but used to extract neural EEG data, which would keep more effective information. Our tests with the data of seven subjects demonstrate that the proposed method has distinct advantages over other two commonly used methods in terms of average root mean square error [37.71 ± 0.14 (CCA-EEMD), 44.72 ± 0.13 (CCA), 49.59 ± 0.16 (ICA)], signal-to-noise ratio [3.59 ± 0.24 (CCA-EEMD), −6.53 ± 0.18(CCA), −8.43 ± 0.26 (ICA)], and classification accuracy [0.88 ± 0.002 (CCA-EEMD), 0.79 ± 0.001 (CCA), 0.73 ± 0.002 (ICA)]. The proposed method can not only remove EOG artifacts automatically but also keep the integrity of EEG data to the maximum extent.

Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms.

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.

Removal of EOG artifacts from EEG using a cascade of sparse autoencoder and recursive least squares adaptive filter

Electrooculogram (EOG) artifacts are the most important form of interferences in electroencephalogram (EEG) based brain computer interfaces (BCIs). In traditional methods for EOG artifacts removal, either an additional EOG recording in real time or multi-channel (more than three channels) EEG recording is required. To address these limitations of existing methods, a method using a cascade of sparse autoencoder (SAE) and recursive least squares (RLS) adaptive filter is proposed to remove the EOG artifacts from EEG. The proposed approach consists of offline stage and online stage. The high-order statistical moments information in the EOG artifacts can be learned automatically by using only EOG signals during offline stage and so an SAE model is obtained. In the online stage, the learned SAE model is firstly used to identify and extract preliminary EOG artifacts from a given raw EEG signal. Then an RLS adaptive filter uses the identified EOG artifacts as reference signal to remove interference without parallel EOG recordings. Compared with the exiting methods, the proposed method has the following advantages: (i) nonuse of an additional EOG recording in removal process, (ii) few number of EEG channels being used in removal process, and (iii) time-saving. The performance of the proposed method is evaluated by EEG classification accuracy and time consumption. Compared with traditional methods, the proposed method is proven to be more effective and faster. Moreover, experiment results also show good generalization ability in cross-subject testing scenarios.

Removal of EOG Artifacts from Single Channel EEG Signals using Combined Singular Spectrum Analysis and Adaptive Noise Canceler

The electroencephalogram (EEG) signals represent the electrical activity of the brain. In applications, such as brain–computer interface (BCI), features of the EEG signals are used to control the devices. However, while recording, EEG signals often contaminated by electrooculogram (EOG) artifacts; such artifacts degrade the performance of the BCI. In this paper, we proposed a new technique using singular spectrum analysis (SSA) and adaptive noise canceler (ANC) to remove the EOG artifact from the contaminated EEG signal. In this technique, first, we proposed a novel grouping technique for SSA to construct the reference signal (EOG) for ANC. Later, using the extracted reference signal, the adaptive filter was employed to remove EOG artifact from the contaminated EEG signal. To quantify the performance of the proposed technique, we carried out simulations on synthetic and real-life EEG signals. In terms of relative root mean square error and mean absolute error, the proposed SSA-ANC method outperforms the existing techniques.

Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising

The physiological artifacts such as electromyogram (EMG) and electrooculogram (EOG) remain a major problem in electroencephalogram (EEG) research. A number of techniques are currently in use to remove these artifacts with the hope that the process does not unduly degrade the quality of the obscured EEG. In this paper, a new method has been proposed by combining two techniques: a canonical correlation analysis (CCA) followed by a stationary wavelet transform (SWT) to remove EMG artifacts and a second-order blind identification (SOBI) technique followed by SWT to remove EOG artifacts. The simulation results show that these combinations are more effective than either using the individual techniques alone or using other combinations of techniques. The quality of the artifact removal is evaluated by calculating correlations between processed and unprocessed data, and the practicability of the technique is judged by comparing execution times of the algorithms.