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

Real-Time, Hardware Efficient Ocular Artifact Removal From Single Channel EEG data Using a Hybrid Algebraic and Wavelet Algorithm

Electroencephalography (EEG) is a promising technique to record brain activities in natural settings. EEG signal usually gets contaminated by Ocular Artifacts (OA), removal of which is critical for the feature extraction and classification. With the increasing interest in wearable technologies, single channel EEG systems are becoming more prevalent that often require real-time signal processing for immediate feedback. In this context, a new hybrid algorithm to detect OA and subsequently remove OA from single channel streaming EEG data is proposed here. The algorithm first detects the OA zones using Algebraic approach, and then removes artifact from the detected OA zones using Discrete Wavelet Transform (DWT) decomposition method. De-noising technique is applied only to the OA zone that minimizes interference to neural information outside of OA zone. The microcontroller hardware implemented hybrid OA removal algorithm demonstrated real-time execution with sufficient accuracy in both OA detection and removal. The performance evaluation was carried out qualitatively and quantitatively for 0.5 sec epoch in overlapping manner using time-frequency analysis, mean square coherence, Correlation Coefficient (CC) and Mutual Information statistics. Matlab implementation resulted in average CC of 0.3242 and average MI of 1.0042, while microcontroller implementation resulted in average CC of 0.4033 for all blinks. Successful implementation of OA removal from single channel real-time EEG data using the proposed algorithm shows promise for real-time feedabck applications of wearable EEG devices

Review of Artifact Rejection Methods for Electroencephalographic Systems

Technologies using electroencephalographic (EEG) signals have been penetrated into public by the development of EEG systems. During EEG system operation, recordings ought to be obtained under no restriction of movement for routine use in the real world. However, the lack of consideration of situational behavior constraints will cause technical/biological artifacts that often mixed with EEG signals and make the signal processing difficult in all respects by ingeniously disguising themselves as EEG components. EEG systems integrating gold standard or specialized device in their processing strategies would appear as daily tools in the future if they are unperturbed to such obstructions. In this chapter, we describe algorithms for artifact rejection in multi-/single-channel. In particular, some existing single-channel artifact rejection methods that will exhibit beneficial information to improve their performance in online EEG systems were summarized by focusing on the advantages and disadvantages of algorithms

ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS

Ph.DDOCTOR OF PHILOSOPH

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

Noise Reduction of EEG Signals Using Autoencoders Built Upon GRU based RNN Layers

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an efficient noise reduction technique to get more accurate recordings. Numerous traditional techniques such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), wavelet transformations and machine learning techniques were proposed for reducing the noise in EEG signals. The aim of this paper is to investigate the effectiveness of stacked autoencoders built upon Gated Recurrent Unit (GRU) based Recurrent Neural Network (RNN) layers (GRU-AE) against PCA. To achieve this, Harrell-Davis decile values for the reconstructed signals’ signal-to- noise ratio distributions were compared and it was found that the GRU-AE outperformed PCA for noise reduction of EEG signals

Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network

High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application