Artifacts and noise removal for electroencephalogram (EEG): A literature review

Electroencephalogram (EEG) is a signal collected from the human brain to study and analyze the brain activities. However, raw EEG may be contaminated with unwanted components such as noises and artifacts caused by power source, environment, eye blinks, heart rate and muscle movements, which are unavoidable. These unwanted components will effect the analysis of EEG and provide inaccurate information. Therefore, researchers have proposed all kind of approaches to eliminate unwanted noises and artifacts from EEG. In this paper, a literature review is carried out to study the works that have been done for noise and artifact removal from year 2010 up to the present. It is found that conventional approaches include ICA, wavelet based analysis, statistical analysis and others. However, the existing ways of artifacts removal cannot eliminate certain noise and will cause information lost by directly discard the contaminated components. From the study, it is shown that combination of conventional with other methods is popularly used, as it is able to improve the removal of artifacts. The current trend of artifacts removal makes use of machine learning to provide an automated solution with higher efficiency.

[1]  Tom Eichele,et al.  Semi-automatic identification of independent components representing EEG artifact , 2009, Clinical Neurophysiology.

[2]  F. La Foresta,et al.  Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA , 2012, IEEE Sensors Journal.

[3]  Saleha Saleha Khatun Khatun,et al.  Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data , 2016, IEEE Journal of Translational Engineering in Health and Medicine.

[4]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[5]  Damien Coyle,et al.  A hybrid ICA-wavelet transform for automated artefact removal in EEG-based emotion recognition , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[6]  Alexandre Gramfort,et al.  Autoreject: Automated artifact rejection for MEG and EEG data , 2016, NeuroImage.

[7]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[8]  Tarmo Lipping,et al.  Prediction of outcome in traumatic brain injury patients using long-term qEEG features , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[9]  Ashkan Ashrafi,et al.  Application of multivariate empirical mode decomposition and canonical correlation analysis for EEG motion artifact removal , 2016, 2016 Conference on Advances in Signal Processing (CASP).

[10]  Kiret Dhindsa,et al.  Filter-Bank Artifact Rejection: High performance real-time single-channel artifact detection for EEG , 2017, Biomed. Signal Process. Control..

[11]  Manish N. Tibdewal,et al.  Power line and ocular artifact denoising from EEG using notch filter and wavelet transform , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[12]  Seyyed Mohammad Reza Hashemi,et al.  Classification of EEG-based emotion for BCI applications , 2017, 2017 Artificial Intelligence and Robotics (IRANOPEN).

[13]  Toshihisa Tanaka,et al.  EEG energy analysis based on MEMD with ICA pre-processing , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[14]  Dattaprasad A. Torse,et al.  Design of adaptive EEG preprocessing algorithm for neurofeedback system , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).

[15]  A. Schlögl,et al.  Artifact Processing in Computerized Analysis of Sleep EEG – A Review , 1999, Neuropsychobiology.

[16]  Maide Bucolo,et al.  Automatic preprocessing of EEG signals in long time scale , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[17]  Siti Anom Ahmad,et al.  Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks , 2017, Sensors.

[18]  Yu Liu,et al.  Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics , 2017, Comput. Biol. Medicine.

[19]  Masahiro Iwahashi,et al.  Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA , 2018, IEEE Journal of Biomedical and Health Informatics.

[20]  M. Congedo,et al.  The Riemannian Potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry , 2013 .

[21]  R. B. Reilly,et al.  FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection , 2010, Journal of Neuroscience Methods.

[22]  R. Ward,et al.  EMG and EOG artifacts in brain computer interface systems: A survey , 2007, Clinical Neurophysiology.

[23]  Debatri Chatterjee,et al.  Inactive-state recognition from EEG signals and its application in cognitive load computation , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[24]  M. Tangermann,et al.  Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals , 2011, Behavioral and Brain Functions.

[25]  Amitash Ojha,et al.  Difference in brain activation patterns of individuals with high and low intelligence in linguistic and visuo-spatial tasks: An EEG study , 2017 .

[26]  Caroline Palmer,et al.  Synchronizing MIDI and wireless EEG measurements during natural piano performance , 2017, Brain Research.

[27]  Antoine Souloumiac,et al.  Jacobi Angles for Simultaneous Diagonalization , 1996, SIAM J. Matrix Anal. Appl..

[28]  Paolo Maria Rossini,et al.  Searching for signs of aging and dementia in EEG through network analysis , 2017, Behavioural Brain Research.

[29]  Bin Hu,et al.  A method of removing Ocular Artifacts from EEG using Discrete Wavelet Transform and Kalman Filtering , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[30]  Kenneth A. Loparo,et al.  Automated Removal of EKG Artifact From EEG Data Using Independent Component Analysis and Continuous Wavelet Transformation , 2014, IEEE Transactions on Biomedical Engineering.

[31]  Bashir I. Morshed,et al.  Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA , 2015, IEEE Journal of Biomedical and Health Informatics.

[32]  J. McBride,et al.  Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy , 2011, Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine.

[33]  P. Israsena,et al.  Automatic removal of EEG artifacts using ICA and Lifting Wavelet Transform , 2013, 2013 International Computer Science and Engineering Conference (ICSEC).

[34]  Matthew R. Myers,et al.  Real-Time Detection and Monitoring of Acute Brain Injury Utilizing Evoked Electroencephalographic Potentials , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[35]  M. Congedo,et al.  Low-resolution electromagnetic tomography neurofeedback , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[36]  Chamandeep Kaur,et al.  EEG artifact suppression based on SOBI based ICA using wavelet thresholding , 2015, 2015 2nd International Conference on Recent Advances in Engineering & Computational Sciences (RAECS).

[37]  Debi Prosad Dogra,et al.  Prediction of advertisement preference by fusing EEG response and sentiment analysis , 2017, Neural Networks.

[38]  Lianyang Li,et al.  Brain activation profiles in mTBI: Evidence from combined resting-state EEG and MEG activity , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[39]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..