Robust removal of ocular artifacts by combining Independent Component Analysis and system identification

A Long Short-Term Memory Network for Sparse Spatiotemporal EEG Source Imaging

EEG inverse problem is underdetermined, which poses a long standing challenge in Neuroimaging. The combination of source-imaging and analysis of cortical directional networks enables us to noninvasively explore the underlying neural processes. However, existing EEG source imaging approaches mainly focus on performing the direct inverse operation for source estimation, which will be inevitably influenced by noise and the strategy used to find the inverse solution. Here, we develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation. In DeepBraiNNet, considering that Recurrent Neural Network (RNN) are usually "deep" in temporal dimension and thus suitable for time sequence modelling, the RNN with Long Short-Term Memory (LSTM) is utilized to approximate the inverse operation for the lead field matrix instead of performing the direct inverse operation, which avoids the possible effect of the direct inverse operation on the underdetermined lead field matrix prone to be influenced by noise. Simulations on various source patterns and noise conditions confirmed that the proposed approach could actually recover the spatiotemporal sources well, outperforming existing state of-the-art methods. DeepBraiNNet also estimated sparse MI related activation patterns when it was applied to a real Motor Imagery dataset, consistent with other findings based on EEG and fMRI. Based on the spatiotemporal sources estimated from DeepBraiNNet, we constructed MI related cortical neural networks, which clearly exhibited strong contralateral network patterns for the two MI tasks. Consequently, DeepBraiNNet may provide an alternative way different from the conventional approaches for spatiotemporal EEG source imaging.

Progress and Challenges in Physiological Artifacts' Detection in Electroencephalographic Readings

BACKGROUND

Electroencephalographic (EEG) recordings are used to trace neural activity within the cortex to study brain functioning over time.

INTRODUCTION

During data acquisition, the unequivocal way to reduce artifact is to avoid artifact stimulating events. Though there are certain artifacts that make this task challenging due to their association with the internal human mechanism, in the human-computer interface, these physiological artifacts are of great assistance and act as a command signal for controlling a device or an application (communication). That is why pre-processing of electroencephalographic readings has been a progressive area of exploration, as none of the published work can be viewed as a benchmark for constructive artifact handling.

METHOD

This review offers a comprehensive insight into state of the art physiological artifact removal techniques listed so far. The study commences from the single-stage traditional techniques to the multistage techniques, examining the pros and cons of each discussed technique. Also, this review paper gives a general idea of various datasets available and briefs the topical trend in EEG signal processing.

RESULT

Comparing the state of the art techniques with hybrid ones on the basis of performance and computational complexity, it has been observed that the single-channel techniques save computational time but lack in effective artifact removal especially physiological artifacts. On the other hand, hybrid techniques merge the essential characteristics resulting in increased performance, but time consumption and complexity remain an issue.

CONCLUSION

Considering the high probability of the presence of multiple artifacts in EEG channels, a trade-off between performance, time and computational complexity is the only key for effective processing of artifacts in the time ahead. This paper is anticipated to facilitate upcoming researchers in enriching the contemporary artifact handling techniques to mitigate the expert's burden.

Probability mapping based artifact detection and removal from single-channel EEG signals for brain-computer interface applications

BACKGROUND

Different types of artifacts in the electroencephalogram (EEG) signals can considerably reduce the performance of the later-stage EEG analysis algorithms for making decisions, such as those for brain-computer interfacing (BCI) classification. In this paper, we address the problem of artifact detection and removal from single-channel EEG signals.

NEW METHOD

We propose a novel approach that maps the probability of an EEG epoch to be artifactual based on four different statistical measures: entropy (a measure of uncertainty), kurtosis (a measure of peakedness), skewness (a measure of asymmetry), and periodic waveform index (a measure of periodicity). Then, a stationary wavelet transform based artifact removal is proposed that employs a particular probability threshold provided by the user.

RESULTS

We have executed our experiments with both synthetic and real EEG data. It is observed that the proposed method exhibits a superior performance for suppressing the artifact contaminated from EEG with minimum distortion. Moreover, evaluation of the algorithm using EEG dataset for BCI experiments reveals that artifact removal can considerably improve the BCI output in both event-related potential and motor-imagery based BCI applications.

COMPARISON WITH EXISTING METHODS

The proposed algorithm has been applied to both real and synthesized data testing and compared with other state-of-the-art automated artifact removal methods. Its superior performance is verified in terms of various performance metrics including computational complexity for justifying its use in BCI-like real-time applications.

CONCLUSION

Our work is expected to be useful for future research EEG signal processing and eventually to develop more accurate real-time EEG-based BCI applications.

Robust Autoregression with Exogenous Input Model for System Identification and Predicting

Autoregression with exogenous input (ARX) is a widely used model to estimate the dynamic relationships between neurophysiological signals and other physiological parameters. Nevertheless, biological signals, such as electroencephalogram (EEG), arterial blood pressure (ABP), and intracranial pressure (ICP), are inevitably contaminated by unexpected artifacts, which may distort the parameter estimation due to the use of the L2 norm structure. In this paper, we defined the ARX in the Lp (p ≤ 1) norm space with the aim of resisting outlier influence and designed a feasible iteration procedure to estimate model parameters. A quantitative evaluation with various outlier conditions demonstrated that the proposed method could estimate ARX parameters more robustly than conventional methods. Testing with the resting-state EEG with ocular artifacts demonstrated that the proposed method could predict missing data with less influence from the artifacts. In addition, the results on ICP and ABP data further verified its efficiency for model fitting and system identification. The proposed Lp-ARX may help capture system parameters reliably with various input and output signals that are contaminated with artifacts.

Directed EEG neural network analysis by LAPPS (p≤1) Penalized sparse Granger approach

The conventional multivariate Granger Analysis (GA) of directed interactions has been widely applied in brain network construction based on EEG recordings as well as fMRI. Nevertheless, EEG is usually inevitably contaminated by strong noise, which may cause network distortion due to the L2-norm used in GAs for directed network recovery. The Lp (p ≤1) norm has been shown to be more robust to outliers as compared to LASSO and L2-GAs. Motivated to construct the sparse brain networks under strong noise condition, we hereby introduce a new approach for GA analysis, termed LAPPS (Least Absolute LP (0<p<1) Penalized Solution). LAPPS utilizes the L1-loss function for the residual error to alleviate the effect of outliers, and another Lp-penalty term (p=0.5) to obtain the sparse connections while suppressing the spurious linkages in the networks. The simulation results reveal that LAPPS obtained the best performance under various noise conditions. In a real EEG data test when subjects performed the left and right hand Motor Imagery (MI) for brain network estimation, LAPPS also obtained a sparse network pattern with the hub at the contralateral brain primary motor areas consistent with the physiological basis of MI.

Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis

Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods.

EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations

OBJECTIVE

Spectral power analysis plays a predominant role in electroencephalogram-based emotional recognition. It can reflect activity differences among multiple brain regions. In addition to activation difference, different emotions also involve different large-scale network during related information processing. In this paper, both information propagation patterns and activation difference in the brain were fused to improve the performance of emotional recognition.

METHODS

We constructed emotion-related brain networks with phase locking value and adopted a multiple feature fusion approach to combine the compensative activation and connection information for emotion recognition.

RESULTS

Recognition results on three public emotional databases demonstrated that the combined features are superior to either single feature based on power distribution or network character. Furthermore, the conducted feature fusion analysis revealed the common characters between activation and connection patterns involved in the positive, neutral, and negative emotions for information processing.

SIGNIFICANCE

The proposed feasible combination of both information propagation patterns and activation difference in the brain is meaningful for developing the effective human-computer interaction systems by adapting to human emotions in the real world applications.

Sparse EEG Source Localization Using LAPPS: Least Absolute l-P (0 < p < 1) Penalized Solution

OBJECTIVE

The electroencephalographic (EEG) inverse problem is ill-posed owing to the electromagnetism Helmholtz theorem and since there are fewer observations than the unknown variables. Apart from the strong background activities (ongoing EEG), evoked EEG is also inevitably contaminated by strong outliers caused by head movements or ocular movements during recordings.

METHODS

Considering the sparse activations during high cognitive processing, we propose a novel robust EEG source imaging algorithm, LAPPS (Least Absolute -P (0 < p < 1) Penalized Solution), which employs the -loss for the residual error to alleviate the effect of outliers and another -penalty norm (p=0.5) to obtain sparse sources while suppressing Gaussian noise in EEG recordings. The resulting optimization problem is solved using a modified ADMM algorithm.

RESULTS

Simulation study was performed to recover sparse signals of randomly selected sources using LAPPS and various methods commonly used for EEG source imaging including WMNE, -norm, sLORETA and FOCUSS solution. The simulation comparison quantitatively demonstrates that LAPPS obtained the best performances in all the conducted simulations for various dipoles configurations under various SNRs on a realistic head model. Moreover, in the localization of brain neural generators in a real visual oddball experiment, LAPPS obtained sparse activations consistent with previous findings revealed by EEG and fMRI.

CONCLUSION

This study demonstrates a potentially useful sparse method for EEG source imaging, creating a platform for investigating the brain neural generators.

SIGNIFICANCE

This method alleviates the effect of noise and recovers sparse sources while maintaining a low computational complexity due to the cheap matrix-vector multiplication.

Robust Granger Analysis in Lp Norm Space for Directed EEG Network Analysis

Granger analysis (GA) is widely used to construct directed brain networks based on various physiological recordings, such as functional magnetic resonance imaging, and electroencephalogram (EEG). However, in real applications, EEGs are inevitably contaminated by unexpected artifacts that may distort the networks because of the L2 norm structure utilized in GAs when estimating directed links. Compared with the L2 norm, the Lp ( ) norm can compress outlier effects. In this paper, an extended GA is constructed by applying the Lp ( ) norm strategy to estimate robust causalities under outlier conditions, and a feasible iteration procedure is utilized to solve the new GA model. A quantitative evaluation using a predefined simulation network demonstrates smaller bias errors and higher linkage consistence for the Lp ( , 0.8, 0.6, 0.4, 0.2) -GAs compared with both the Lasso- and L2-GAs under various simulated outlier conditions. Applications in resting-state EEGs that contain ocular artifacts also show that the proposed GA can effectively compress the ocular outlier influence and recover the reliable networks. The proposed Lp-GA may be helpful in capturing the reliable network structure when EEGs are contaminated with artifacts in related studies.

Methods for artifact detection and removal from scalp EEG: A review

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.

Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.

Autoregressive model in the Lp norm space for EEG analysis

The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses such as waveform fitting, spectrum estimation, and system identification. In real applications, EEGs are inevitably contaminated with unexpected outlier artifacts, and this must be overcome. However, most of the current AR models are based on the L2 norm structure, which exaggerates the outlier effect due to the square property of the L2 norm. In this paper, a novel AR object function is constructed in the Lp (p≤1) norm space with the aim to compress the outlier effects on EEG analysis, and a fast iteration procedure is developed to solve this new AR model. The quantitative evaluation using simulated EEGs with outliers proves that the proposed Lp (p≤1) AR can estimate the AR parameters more robustly than the Yule-Walker, Burg and LS methods, under various simulated outlier conditions. The actual application to the resting EEG recording with ocular artifacts also demonstrates that Lp (p≤1) AR can effectively address the outliers and recover a resting EEG power spectrum that is more consistent with its physiological basis.