Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods

Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications

Effective EEG Artifact Removal from EEG Signal

An EEG (electroencephalography) provides insight into the status of the brain’s electrical activity. EEG is based on the electrical activity measured in voltage at various sites in the brain. Generally speaking, these signals are non-stationery and time-varying. Various signal processing techniques can be used to examine these signals. Several statistical approaches to EEG data analysis are discussed in this chapter. In this Chapter, Electroencephalograph Signals and their generation process have been discussed; the EEG signal has been compared with fMRI and PET signals. The classification of the EEG signals on the amplitude, frequency, and shape have been elaborated in wave analysis of EEG, and applications of these components are presented. The artifacts of EEG have been explained in detail

Automated and Reliable Low-Complexity SoC Design Methodology for EEG Artefacts Removal

EEG is a non-invasive tool for neurodevelopmental disorder diagnosis (NDD) and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artefacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Independent Component Analysis (ICA) and wavelet-based algorithms require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the NDD and Brain Computer Interface (BCI). Therefore, it would be ideal if these artefacts can be removed real time and on hardware platform in an automated fashion and denoised EEG can be used for online diagnosis in a pervasive personalised healthcare environment without the need of any reference electrode. In this thesis we propose a reliable, robust and automated methodology to solve the aforementioned problem and its subsequent hardware implementation results are also presented. 100 EEG data from Physionet, Klinik fur Epileptologie, Universitat Bonn, Germany, Caltech EEG databases and 3 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The performance of the proposed methodology is measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and hardware delay 53.58% compared to state-ofthe art approach. We believe the proposed methodology would be useful in next generation of pervasive healthcare for BCI and NDD diagnosis and treatment

ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository

Characterization and filtering of electroencephalogram contaminated by electromyography of facial muscles

The Electroencephalogram (EEG) has been the most preferred way of recording brain activity due to its noninvasiveness and affordability benefits. Information estimated from EEG has been employed broadly, e.g., for diagnosis or as an input signal to Brain-Computer Interfaces (BCI). Nevertheless, the EEG is prone to artifacts including non-brain physiological activities, such as eye blinking and the contraction of the muscles of the scalp. Some applications such as BCI systems may occasionally be associated with frequent contractions of muscles of the head corrupting the EEG-based control signal. This requires the application of several filtering techniques. However, the gold standard techniques for signal filtering still contain limitations, such as the incapacity of eliminating noise in all EEG channels. For this reason, besides studying and applying filtering techniques, it is necessary to understand the contamination from electromyogram (EMG) along the scalp. Several studies concluded that EMG artifact contaminates the EEG at frequencies beginning at 15 Hz on the topographic distribution of the energy that encompasses practically the entire scalp. Thus, the present work aims to quantitatively estimate EMG noise in 16 bipolar channels of EEG distributed along the scalp according to the 10-20 system. This estimation was based on an experimental protocol considering the simultaneous acquisition of EEG and EMG of five facial muscles sampled at 5 kHz. The protocol consisted of activating facial muscles while listening to 15 beep sounds. The evaluated muscles were frontal, masseter, zygomatic, orbicularis oculi, and orbicularis oris. The mean power of the EEG contaminated by EMG of facial muscle contractions was compared between the periods of muscle contraction and non-contraction. The results show that EMG contamination from frontal and masseter muscles are present over the scalp with an increase from 63.5 μV2 to 816 μV2 and from 118.3 μV2 to 5,617.9 μV2, respectively. Also, this work proposes a technique for EMG artifact removal that is less sensitive to low SNR as the current gold standard techniques. The proposed method, so-called EMDRLS, employs Empirical Mode Decomposition (EMD) to generate an EMG noise reference to an adaptive Recursive Least Squares (RLS) filter. To test the EMDRLS method, EEG signals were collected from 10 healthy subjects during the controlled execution of successive facial muscular contractions. The experimental protocol considered the isolated activation of the masseter and frontal muscles. EEG corrupted signals were filtered by the EMDRLS method considering distinct SNRs. The results were compared to traditional approaches: Wiener, Wavelet, EMD, and a hybrid wavelet-RLS filtering method. The following performance metrics were considered in the comparative evaluation: (i) SNR of the contaminated signal; (ii) the root mean square error (RMSE) between the power spectrum of artifact-free and filtered EEG epochs; (iii) the spectral preservation of brain rhythms (i.e., delta, theta, alpha, beta, and gamma) of filtered signals. For EEG signals with SNR below -10dB, the EMDRLS method yielded filtered EEG signals with SNR varying from 0 to 10 dB. The technique reduced the RMSE of frontal channels from 1.202 to 0.043, which are the source of the most corrupted EEG signals. The Kruskal-Wallis test and the Tukey-Kramer post-hoc test (p < 0.05) confirmed the preservation of all brain rhythms given by EEG signals filtered with the EMDRLS method. The results have shown that the single-channel EMDRLS method can be applied to highly contaminated EEG signals by facial EMG signal with performance superior to that of established methods.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisTese (Doutorado)O Eletroencefalograma (EEG), é uma medida da atividade cerebral que ostenta as vantagens de portabilidade, baixo custo, alta resolução temporal e não invasivo. Os desafios desse exame são os artefatos de diferentes fontes que tornam a análise de dados do EEG mais difícil, e que potencialmente resulta em erros de interpretação. Portanto, é essencial para muitas aplicações médicas e práticas remover esses artefatos no pré-processamento antes de analisar os dados do EEG. Nos últimos trinta anos, vários métodos foram desenvolvidos para remover diferentes tipos de artefatos de dados de EEG contaminados; ainda assim, não há nenhum método padrão que pode ser usado de forma otimizada e, portanto, a pesquisa permanece atraente e desafiadora. Algumas aplicações, como as Interfaces Homem Computador (HCI), podem ocasionalmente estar associadas a frequentes contrações dos músculos da cabeça, corrompendo o sinal de controle baseado no EEG, requerendo a aplicação de alguma técnica de filtragem. No entanto, as técnicas padrão de ouro para filtragem de sinal ainda contêm limitações, como a incapacidade de eliminar o ruído em todos os canais EEG com relações sinal-ruído (SNR) muito baixas e quando a faixa espectral do ruído sobrepõe a do EEG, que caracteriza diversas contaminações no EEG, mas principalmente a contaminação oriunda do sinal eletromiográfico. Por esta razão, além de estudar e aplicar técnicas de filtragem, é necessário entender a contaminação do eletromiograma (EMG) ao longo do couro cabeludo. Alguns estudos concluíram que o artefato EMG contamina o EEG em frequências a partir de 15 Hz em uma distribuição topográfica que engloba praticamente todo o couro cabeludo. Assim, o presente trabalho tem como objetivo estimar quantitativamente o ruído EMG em 16 canais bipolares de EEG distribuídos ao longo do couro cabeludo de acordo com o sistema 10-20. Essa estimativa foi baseada em um protocolo experimental considerando a aquisição simultânea de EEG e EMG de cinco músculos faciais amostrados a 5 kHz. O protocolo consistiu em ativar os músculos faciais enquanto o voluntário ouvisse 15 sons de bip. Os músculos avaliados foram o frontal, masseter, temporal, zigomático, orbicular do olho e orbicular da boca. A potência média do EEG contaminado pela EMG das contrações da musculatura facial foi comparado entre os períodos de contração muscular e não contração. Os resultados mostram que a contaminação muscular do frontal e do masseter provoca um aumento de energia sobre o couro cabeludo de 63,5 μV2 para 816 μV2 e de 118,3 μV2 para 5,617,9 μV2, respectivamente. Além disso, este trabalho propõe uma técnica de remoção do artefato de EMG menos sensível a baixas SNRs que as atuais técnicas padrão ouro. O método proposto, chamado EMDRLS, emprega Decomposição do Modo Empírico (EMD) para gerar uma referência de ruído EMG a um filtro RLS (Recursive Least Squares) adaptativo. Para testar o EMDRLS, foram coletados sinais de EEG de 10 indivíduos saudáveis durante a execução controlada de sucessivas contrações musculares faciais. O protocolo experimental considerou a ativação isolada dos músculos masseter e frontal. Os sinais corrompidos por EEG foram filtrados por EMDRLS considerando SNRs distintos. Os resultados foram comparados às abordagens tradicionais: Wiener, Wavelet, EMD e um método de filtragem híbrido wavelet-RLS. As seguintes métricas de desempenho foram consideradas na avaliação comparativa: (i) SNR do sinal contaminado; (ii) o erro quadrático médio da raiz (RMSE) entre o espectro de potência das épocas de EEG filtradas e sem artefatos; (iii) a preservação espectral de ritmos cerebrais (isto é, delta, teta, alfa, beta e gama) dos sinais filtrados. Para sinais EEG com SNR abaixo de -10dB, o método EMDRLS produziu sinais EEG filtrados com SNR variando de 0 a 10 dB. A técnica reduziu o RMSE dos canais frontais de 1,202 para 0,043, que são a fonte dos sinais de EEG mais corrompidos. O teste de Kruskal-Wallis e o teste post-hoc de Tukey-Kramer (p <0,05) confirmaram a preservação de todos os ritmos cerebrais dados pelos sinais de EEG filtrados pelo método EMDRLS. Os resultados mostraram que o método EMDRLS pode ser aplicado a sinais EEG altamente contaminados por sinal facial EMG com desempenho superior ao dos métodos estabelecidos

Motion Artifact Processing Techniques for Physiological Signals

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signicantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identication and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly eective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identication of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signicantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth

Study of non-invasive cognitive tasks and feature extraction techniques for brain-computer interface (BCI) applications

A brain-computer interface (BCI) provides an important alternative for disabled people that enables the non-muscular communication pathway among individual thoughts and different assistive appliances. A BCI technology essentially consists of data acquisition, pre-processing, feature extraction, classification and device command. Indeed, despite the valuable and promising achievements already obtained in every component of BCI, the BCI field is still a relatively young research field and there is still much to do in order to make BCI become a mature technology. To mitigate the impediments concerning BCI, the study of cognitive task together with the EEG feature and classification framework have been investigated. There are four distinct experiments have been conducted to determine the optimum solution to those specific issues. In the first experiment, three cognitive tasks namely quick math solving, relaxed and playing games have been investigated. The features have been extracted using power spectral density (PSD), logenergy entropy, and spectral centroid and the extracted feature has been classified through the support vector machine (SVM), K-nearest neighbor (K-NN), and linear discriminant analysis (LDA). In this experiment, the best classification accuracy for single channel and five channel datasets were 86% and 91.66% respectively that have been obtained by the PSD-SVM approach. The wink based facial expressions namely left wink, right wink and no wink have been studied through fast Fourier transform (FFT) and sample range feature and then the extracted features have been classified using SVM, K-NN, and LDA. The best accuracy (98.6%) has been achieved by the sample range-SVM based approach. The eye blinking based facial expression has been investigated following the same methodology as the study of wink based facial expression. Moreover, the peak detection approach has also been employed to compute the number of blinks. The optimum accuracy of 99% has been achieved using the peak detection approach. Additionally, twoclass motor imagery hand movement has been classified using SVM, K-NN, and LDA where the feature has been extracted through PSD, spectral centroid and continuous wavelet transform (CWT). The optimum 74.7% accuracy has been achieved by the PSDSVM approach. Finally, two device command prototypes have been designed to translate the classifier output. One prototype can translate four types of cognitive tasks in terms of 5 watts four different colored bulbs, whereas, another prototype may able to control DC motor utilizing cognitive tasks. This study has delineated the implementation of every BCI component to facilitate the application of brainwave assisted assistive appliances. Finally, this thesis comes to the end by drawing the future direction regarding the current issues of BCI technology and these directions may significantly enhance usability for the implementation of commercial applications not only for the disabled but also for a significant number of healthy users

Artifact Analysis and Removal of Electroencephalographic (EEG) Recordings

Electroencephalography (EEG) technique has been widely used in continuous monitoring the brain activities in academic research and clinical applications. In cognitive neuroscience research, the electrical brain signals can be used to measure mental effort of subjects. However, the presence of artifacts is a constant problem when recording brain activities, which will obscure the underlying neural dynamics and therefore make it difficult to interpret EEG signals accurately. These unwanted signals or artifacts have different effects depending on their sources of generation. Among them, the motion of the subject is one of the major contributors to physiological artifacts that causes most of the contaminations to the underlying brain activities. It is quite challenging to correct the myogenic activity from EEG background potentials due to its wide spectral distribution overlapped with typical bands of brain waves related to cognitive activities, and the spatial distribution over the entire scalp of human. As such, this thesis focuses on the analysis and removal of motion artifacts from EEG signals. The preliminary investigations include the movement-triggered artifact identification and the analysis of the characteristics of the motion artifact. According to the recorded video, the contaminated epochs are extracted from the original EEG signals. A set of features of the movement-triggered artifacts are proposed based on power spectral density and wavelet transform. Statistical analysis is performed to distinguish the segments that contain motions. Two typical methods of artifact removal are then studied, and the efficiency to correct this type of artifact is validated by comparing the extracted features of non-movement segments and the contaminated segments. The result shows that the tested artifact removal methods cannot completely remove movement artifacts, which also infers the potential relation between motion and mental activities