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

A new eliminating EOG artifacts technique using combined decomposition methods with CCA and H.P.F. techniques

Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal

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

Removal of movement-induced EEG artifacts: current state of the art and guidelines

Objective: Electroencephalography (EEG) is a non-invasive technique used to record cortical neurons’ electrical activity using electrodes placed on the scalp. It has become a promising avenue for research beyond state-of-the-art EEG research that is conducted under static conditions. EEG signals are always contaminated by artifacts and other physiological signals. Artifact contamination increases with the intensity of movement. Approach: In the last decade (since 2010), researchers have started to implement EEG measurements in dynamic setups to increase the overall ecological validity of the studies. Many different methods are used to remove non-brain activity from the EEG signal, and there are no clear guidelines on which method should be used in dynamic setups and for specific movement intensities. Main results: Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis. However, the choice of method for artifact removal depends on the type and intensity of movement, which affects the characteristics of the artifacts and the EEG parameters of interest. When dealing with EEG under non-static conditions, special care must be taken already in the designing period of an experiment. Software and hardware solutions must be combined to achieve sufficient removal of unwanted signals from EEG measurements. Significance: We have provided recommendations for the use of each method depending on the intensity of the movement and highlighted the advantages and disadvantages of the methods. However, due to the current gap in the literature, further development and evaluation of methods for artifact removal in EEG data during locomotion is needed.EC/H2020/952401/EU/TWINning the BRAIN with machine learning for neuro-muscular efficiency/TwinBrai

A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain-Computer Interface Task Onset

Electromyography artefacts are a well-known problem in Electroencephalography studies (BCIs, brain mapping, and clinical areas). Blind source separation (BSS) techniques are commonly used to handle artefacts. However, these may remove not only EMG artefacts but also some useful EEG sources. To reduce this useful information loss, we propose a new technique for statistically selecting EEG channels that are contaminated with class-dependent EMG (henceforth called EMG-CCh). Methods: The EMG-CCh are selected based on the correlation between EEG and facial EMG channels. They were compared (using a Wilcoxon test) to determine whether the artefacts played a significant role in class separation. To ensure that promising results are not due to weak EMG removal, reliability tests were done. Results: In our data set, the comparison results between BSS artefact removal applied in two ways, to all channels and only to EMG-CCh, showed that ICA, PCA and BSS-CCA can yield significantly better (p<0.05) class separation with the proposed method (79% of the cases for ICA, 53% for PCA and 11% for BSS-CCA). With BCI competition data, we saw improvement in 60% of the cases for ICA and BSS-CCA. Conclusion: The simple method proposed in this paper showed improvement in class separation with both our data and the BCI competition data. Significance: There are no existing methods for removing EMG artefacts based on the correlation between EEG and EMG channels. Also, the EMG-CCh selection can be used on its own or it can be combined with pre-existing artefact handling methods. For these reasons, we believe this method can be useful for other EEG studies

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography

Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study

Detecção e remoção de artefatos em biosinais

O monitoramento de biosinais é usado em hospitais, ambulatórios e dispositivos vestíveis, para diversos fins. Um dos problemas recorrentes em todas eles são os artefatos de movimentos. Os artefatos de movimentos estão entre as principais causas de alarmes falsos nos hospitais e ambulatórios. Neste trabalho são explorados dois aspectos do uso de biosinais, a detecção de sinais contaminados com artefato, e a remoção de artefatos dos biosinais. O desafio na detecção de artefatos é utilizar apenas medidas estatísticas, para caracterizar o artefato, para que o sistema possa futuramente ser generalizado par outros biosinais. Assim foi proposta uma Máquina de Vetores de Suporte para classificar os sinais em "com artefato" ou "sem artefato", este classificador atingiu acurácia de 97%. Na remoção de artefatos os desafios são maiores, para tanto foram propostas duas solu- ções a primeira baseada em decomposição Ensemble Empirical Mode Decompositions - (EEMD), que melhorou a qualidade do sinal principalmente nos sinais mais degradados, e a segunda solução baseada em separação cega de fontes que melhorou a qualidade do sinal em todos os casos.The monitoring of biosigns is used in hospitals, ambulatory and wearable equipment. A recurring problem in all of them is movement artifacts. In hospitals and clinics these are the main causes of false alarms. In this work, we explore two faces of the use of biosignals, detection of signals contaminated with artifact, and a removal of artifacts from biosignals. The challenge in detecting artifacts is to use only statistical measures to characterize the artifact so that the system can be generalized to other bi0signals in the future. Thus, a Support Vector Machine was proposed to classify the signals in with artifact or without artifact, this classifier reached an accuracy of 97%. The challenge of artifact removal is greater than detection, so two solutions were proposed, the first one based on Ensemble Empirical Mode Decompositions - EEMD, which improved the quality of the signal mainly in the most degraded signals and the second solution based on blind source separation has improved signal quality in all cases