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

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

Design of a Simulator for Neonatal Multichannel EEG: Application to Time-Frequency Approaches for Automatic Artifact Removal and Seizure Detection

The electroencephalogram (EEG) is used to noninvasively monitor brain activities; it is the most utilized tool to detect abnormalities such as seizures. In recent studies, detection of neonatal EEG seizures has been automated to assist neurophysiologists in diagnosing EEG as manual detection is time consuming and subjective; however it still lacks the necessary robustness that is required for clinical implementation. Moreover, as EEG is intended to record the cerebral activities, extra-cerebral activities external to the brain are also recorded; these are called “artifacts” and can seriously degrade the accuracy of seizure detection. Seizures are one of the most common neurologic problems managed by hospitals occurring in 0.1%-0.5% livebirths. Neonates with seizures are at higher risk for mortality and are reported to be 55-70 times more likely to have severe cerebral-palsy. Therefore, early and accurate detection of neonatal seizures is important to prevent long-term neurological damage. Several attempts in modelling the neonatal EEG and artifacts have been done, but most did not consider the multichannel case. Furthermore, these models were used to test artifact or seizure detection separately, but not together. This study aims to design synthetic models that generate clean or corrupted multichannel EEG to test the accuracy of available artifact and seizure detection algorithms in a controlled environment. In this thesis, synthetic neonatal EEG model is constructed by using; single-channel EEG simulators, head model, 21-electrodes, and propagation equations, to produce clean multichannel EEG. Furthermore, neonatal EEG artifact model is designed using synthetic signals to corrupt EEG waveforms. After that, an automated EEG artifact detection and removal system is designed in both time and time-frequency domains. Artifact detection is optimised and removal performance is evaluated. Finally, an automated seizure detection technique is developed, utilising fused and extended multichannel features along a cross-validated SVM classifier. Results show that the synthetic EEG model mimics real neonatal EEG with 0.62 average correlation, and corrupted-EEG can degrade seizure detection average accuracy from 100% to 70.9%. They also show that using artifact detection and removal enhances the average accuracy to 89.6%, and utilising the extended features enhances it to 97.4% and strengthened its robustness.لمراقبة ورصد أنشطة واشارات المخ، دون الحاجة لأي عملیات (EEG) یستخدم الرسم أو التخطیط الكھربائي للدماغ للدماغجراحیة، وھي تعد الأداة الأكثر استخداما في الكشف عن أي شذوذأو نوبات غیر طبیعیة مثل نوبات الصرع. وقد أظھرت دراسات حدیثة، أن الكشف الآلي لنوبات حدیثي الولادة، ساعد علماء الفسیولوجیا العصبیة في تشخیص الاشارات الدماغیة بشكل أكبر من الكشف الیدوي، حیث أن الكشف الیدوي یحتاج إلى وقت وجھد أكبر وھوذو فعالیة أقل بكثیر، إلا أنھ لا یزال یفتقر إلى المتانة الضروریة والمطلوبة للتطبیق السریري.علاوة على ذلك؛ فكما یقوم الرسم الكھربائي بتسجیل الأنشطة والإشارات الدماغیة الداخلیة، فھو یسجل أیضا أي نشاط أو اشارات خارجیة، مما یؤدي إلى -(artifacts) :حدوث خلل في مدى دقة وفعالیة الكشف عن النوبات الدماغیة الداخلیة، ویطلق على تلك الاشارات مسمى (نتاج صنعي) . 0.5٪ولادة حدیثة في -٪تعد نوبات الصرع من أكثر المشكلات العصبیة انتشارا،ً وھي تصیب ما یقارب 0.1المستشفیات. حیث أن حدیثي الولادة المصابین بنوبات الصرع ھم أكثر عرضة للوفاة، وكما تشیر التقاریر الى أنھم 70مرة أكثر. لذا یعد الكشف المبكر والدقیق للنوبات الدماغیة -معرضین للإصابة بالشلل الدماغي الشدید بما یقارب 55لحدیثي الولادة مھم جدا لمنع الضرر العصبي على المدى الطویل. لقد تم القیام بالعدید من المحاولات التي كانتتھدف الى تصمیم نموذج التخطیط الكھربائي والنتاج الصنعي لدماغ حدیثي الولادة, إلا أن معظمھا لم یعر أي اھتمام الى قضیة تعدد القنوات. إضافة الى ذلك, استخدمت ھذه النماذج , كل على حدة, أو نوبات الصرع. تھدف ھذه الدراسة الى تصمیم نماذج مصطنعة من شأنھا (artifact) لإختبار كاشفات النتاج الصنعيأن تولد اشارات دماغیة متعددة القنوات سلیمة أو معطلة وذلك لفحص مدى دقة فعالیة خوارزمیات الكشف عن نوبات ضمن بیئة یمكن السیطرة علیھا. (artifact) الصرع و النتاج الصنعي في ھذه الأطروحة, یتكون نموذج الرسم الكھربائي المصطنع لحدیثي الولادة من : قناة محاكاة واحده للرسم الكھربائي, نموذج رأس, 21قطب كھربائي و معادلات إنتشار. حیث تھدف جمیعھا لإنتاج إشاراة سلیمة متعدده القنوات للتخطیط عن طریق استخدام اشارات مصطنعة (artifact) الكھربائي للدماغ.علاوة على ذلك, لقد تم تصمیم نموذجالنتاج الصنعيفي نطاقالوقت و (artifact) لإتلاف الرسم الكھربائي للدماغ. بعد ذلك تم انشاء برنامج لكشف و إزالةالنتاج الصناعينطاقالوقت و التردد المشترك. تم تحسین برنامج الكشف النتاج الصناعيالى ابعد ما یمكن بینما تمت عملیة تقییم أداء الإزالة. وفي الختام تم التمكن من تطویر تقنیة الكشف الآلي عن نوبات الصرع, وذلك بتوظیف صفات مدمجة و صفات الذي تم التأكد من صحتھ. (SVM) جدیدة للقنوات المتعددة لإستخدامھا للمصنفلقد أظھرت النتائج أن نموذج الرسم الكھربائي المصطنع لحدیثي الولادة یحاكي الرسمالكھربائي الحقیقي لحدیثي الولادة بمتوسط ترابط 0.62, و أنالرسم الكھربائي المتضرر للدماغ قد یؤدي الى حدوث ھبوطفي مدى دقة متوسط الكشف عن نوبات الصرع من 100%الى 70.9%. وقد أشارت أیضا الى أن استخدام الكشف والإزالة عن النتاج الصنعي (artifact) یؤدي الى تحسن مستوى الدقة الى نسبة 89.6 %, وأن توظیف الصفات الجدیدة للقنوات المتعددة یزید من تحسنھا لتصل الى نسبة 94.4 % مما یعمل على دعم متانتھا

Effects of EEG-neurofeedback training on brain functional connectivity

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasO neurofeedback (NF) consiste em medir a atividade cerebral, usando técnicas como a eletroencefalografia (EEG) ou a imagem por ressonância magnética funcional (fMRI), e apresentar ao participante, em tempo real, uma representação de um padrão de atividade de interesse, enquanto lhe é pedido para manipular essa mesma representação através da autorregulação da atividade cerebral (Sitaram et al., 2017). As bases neurofisiológicas desta técnica ainda não são conhecidas na sua totalidade, apesar de vários estudos terem demonstrado que o treino através de NF tende a reorganizar as redes cerebrais. Posto isto, existem poucos estudos que tentam comparar a influência da utilização de diferentes modalidades sensoriais de apresentação do “feedback” nos resultados do treino por NF em EEG, e os poucos estudos existentes não investigam possíveis efeitos nas métricas de conectividade funcional do cérebro. Neste projeto, pretendemos avaliar o efeito da utilização de diferentes modalidades de “feedback” no treino de NF através EEG (EEG-NF) para o incremento da amplitude relativa da banda alfa superior no canal Cz, e investigar se existe um efeito significativo nos padrões de conectividade funcional do cérebro. Para esse fim, será efetuada a análise de dados previamente recolhidos em 20 participantes saudáveis que realizaram quatro sessões de treino por EEG-NF, que visava incrementar a densidade espectral na banda alfa superior, e que utilizaram diferentes modalidades de feedback (visual, realidade virtual (VR), e auditiva). Os dados de EEG foram pré-processados, com remoção de artefactos através de análise de componentes independentes. Adicionalmente, duas técnicas de re-referenciação do sinal EEG foram utilizadas para comparação posterior, sendo estas a re-referenciação para a média de todos os canais EEG, e a re-referenciação através da aplicação de um Laplaciano de Superfície com parâmetro de rigidez de valores 4 e 3, respetivamente. A avaliação dos resultados foi efetuada a diversos níveis, com a análise: i) das variações intra-sessão da amplitude relativa da banda alfa superior no canal Cz, ii) da distribuição topológica da banda alfa superior no decorrer do treino, iii) das variações intrasessão dos padrões de conectividade funcional da banda alfa superior, utilizando a parte imaginária da coerência como métrica de conectividade, e iv) por fim, em termos de uma análise de redes, que visava avaliar a importância de nodos de rede, verificada através das métricas como betweeness centrality e força, da atividade segregada, verificada através da métrica de transitividade, e da atividade integrada, verificada através de métricas como caminho característico e eficiência global da rede cerebral. Relativamente aos resultados para a análise espectral e topológica, encontram-se correlações estatisticamente significativas entre o valor da amplitude relativa da banda alfa superior e o número de set, em todos os grupos, principalmente nas duas primeiras sessões, sendo cada set composto por 6 trials com duração de 30 segundos Posto isto, não são registadas diferenças estatisticamente significativas intra-sessão, isto é, do set 1 para o set 5 de cada sessão. Para a análise topológica, não se realizaram testes de significância, mas é possível visualizar uma acentuação da amplitude relativa da banda alfa superior em zonas parietais/occipitais, e é também possível verificar que o treino realizado, não afetou somente a banda de interesse mas também a banda theta, cuja atividade não focal diminui, a banda alfa inferior, cuja amplitude relativa parece incrementar. Relativamente aos resultados da análise de conetividade, os mesmos sugerem que o treino de EEG-NF para o incremento da banda alfa superior resulta num incremento mais pronunciado nas fases iniciais do treino, isto é, nas duas primeiras sessões de treino. Este incremento é representado pelo do número de canais que apresentam conectividade funcional com a zona parietal central, com canais como o Pz, e com a zona parietal direita, CP6, P4, entre outros, independentemente da modalidade de feedback, ou seja, para a generalidade dos "Learners”. De facto, os próprios canais parietais direitos, P4, P8, CP6, TP10 aumentam de forma estatisticamente significativa a conectividade entre eles. Isto parece indiciar a criação de um complexo focado na zona parietal direita. Em todas as modalidades, à exceção da VR, verifica-se ainda um aumento significativo intra-sessão da transitividade e eficiência global enquanto uma diminuição estatisticamente significativa intra-sessão é observada para a métrica caminho característico. Posto isto, a metodologia de neurofeedback no contexto experimental que foi implementado, parece promover a atividade cerebral segregada, isto é, a atividade que resulta de uma atividade cerebral mais localizada, e também integrada, isto é, que resulta da integração da atividade de áreas cerebrais dispersas. A não existência de variações significativas na modalidade VR não parece estar relacionada com a modalidade em si, mas sim devido a uma menor amostra do respetivo grupo. Assim, futuramente será necessário aumentar a amostra, pelo menos para este grupo, por forma a poderem ser extraídos resultados significativos da análise do mesmo. Interessantemente, e independentemente do método de rereferenciação utilizado, enquanto para o grupo do treino NF para a modalidade visual se observa a partir da terceira sessão de treino a estabilização do número de conexões funcionais entre os diferentes elétrodos, ou seja deixa de haver um crescimento acentuado da transitividade e da eficiência global com diminuição simultânea do caminho característico, para o grupo do treino NF com a modalidade auditiva a generalidade dos incrementos verificados, estão presentes em todas as sessões, incluindo a última sessão. No referente ao estudo sobre o método de re-referenciação dos dados EEG, com interesse específico na utilização de um Laplaciano de superfície comparativamente à simples utilização da média dos sinais EEG, a análise topológica das diferentes bandas cerebrais confirma que a utilização do Laplaciano de superfície contribuiu para aumento da resolução espacial dos dados de EEG, uma vez que atenuou para as diferentes bandas a amplitude relativa da atividade periférica, ou seja não focal, que estará relacionada com frequências espaciais mais baixas. Relativamente à análise da conectividade funcional intra-sessão, verifica-se que a aplicação do Laplaciano se reflete na mudança das configurações de variações de conexões funcionais no cérebro, nomeadamente eliminando determinados aumentos estatisticamente significativos, por exemplo para a sessão 1 dos “Learners”, após a aplicação do Laplaciano de superfície, o incremento da conectividade funcional entre Pz e O2 deixa de ser estatisticamente significativo. Possivelmente, isto poderá estar relacionado com uma eliminação de conexões espúrias. Também na análise de redes, a aplicação do Laplaciano afeta a configuração dos dados e outputs embora não se consiga precisar uma relação causa efeito. Posto isto, a variação da própria configuração do Laplaciano, no que se refere à rigidez do mesmo, de parâmetro m=4 para m=3, não se traduz em resultados tão diferentes, pese embora algumas alterações notadas na análise de redes. De facto, para análise de conectividade funcional, os heatmaps resultantes da aplicação de Laplaciano de superfície com m=4, são exatamente iguais aos heatmaps resultantes da aplicação de Laplaciano de superfície com m=3. Quanto à análise de redes, nomeadamente nas métricas de transitividade, caminho característico e eficiência global, se verificarmos os gráficos e tabelas apresentadas, apesar de serem notados ligeiros desvios quer nas curvas quer em valores de correlação ou variação intra-sessão, o nível de significância é quase sempre atingido, independentemente da rigidez do Laplaciano aplicado, para a mesma sessão. Posto isto, não é possível reportar claramente uma relação causa-efeito vantajosa decorrente da aplicação do Laplaciano de superfície nos dados aqui tratados. De facto, reitera-se que, pela análise topológica se confirma que este possa estar associado a um filtro espacial, mas nas restantes análises não se consegue confirmar se este “melhorou ou não” os nossos dados.Neurofeedback (NF) consists in measuring brain activity and presenting a real-time representation of a brain activity pattern of interest to an individual, while instructing him to manipulate the feedback representation through self-regulation. The neurophysiological basis for NF remains to be fully elucidated, whereas several studies support that NF training tends to reorganize the brain networks. Only a handful of studies compare how different feedback sensory modalities affect the outcomes of EEG-based NF training, and none of them analyzes such effect on the functional connectivity or network metrics. In this project, we evaluate how using different feedback modalities on the EEG-based NFtraining will affect the brain’s functional connectivity, by analyzing previously collected data from a total of 20 healthy subjects, who underwent four sessions of upper-alpha (UA) band EEG-based NF training, with different feedback modalities (visual, auditory, or virtual reality (VR)). The EEG data was preprocessed and re-referenced with three different methods for posterior comparison, the common average reference (avgREF), and spline lines Surface Laplacian with stiffness parameters equals 4 and 3. The data were evaluated in terms of: i) the within-sessions’ variations of the relative amplitude of the UA at the Cz channel, ii) relative band amplitude topological distribution across sets and sessions, iii) the within-sessions’ variations of the UA functional connectivity patterns, computed with the imaginary part of coherency, and iv) an UA band network analysis of the metrics betweenness centrality, strength, transitivity, charpath and global efficiency. Our results suggest that the UA EEG-based NF-training is associated with an early increment of functional connections with channels over parietal areas (e.g. Pz), independently of the feedback sensory modality. All the modalities, except the VR, which had a reduced sample, verify statistically significant intra-session increases in the transitivity and global efficiency, while showing statistically significant intra-session decreases of the charpath, suggesting that this protocol promotes both clustered and integrated brain activity. While for the visual NF-training group the third session seems to be a breakthrough point, where the number of functional connections stabilize, for the auditory NF-training group longer lasting “variations” are reported. Through the topological analysis we confirm that the application of Laplacian leads to higher spatial resolutions on the EEG data. Regarding the connectivity analysis and network analysis, we note that the application of the Surface Laplacian creates different values when compared to the avgREF data, yet no advantageous outcome can be reported

Rhythmic activities of the brain: quantifying the high complexity of beta and gamma oscillations during visuomotor tasks

Electroencephalography (EEG) signals depict the electrical activity that take place at the surface of the brain, and provide an important tool for understanding a variety of cognitive processes. The EEG are the product of synchronized activity of the brain and variations in EEG oscillations patterns reflect the underlying changes in neuronal synchrony. Our aim is to characterize the complexity of the EEG rhythmic oscillations bands when the subjects performs a visuomotor or imagined cognitive tasks (imagined movement), providing a causal mapping of the dynamical rhythmic activities of the brain as a measure of attentional investment. We estimate the intrinsic correlational structure of the signals within the causality entropy-complexity plane H x C, where the enhanced complexity in the gamma 1, gamma 2 and beta 1 bands allow us to distinguish motor-visual memory tasks from control conditions. We identify the dynamics of the gamma 1, gamma 2 and beta 1 rhythmic oscillations within the zone of a chaotic dissipative behavior, while in contrast the beta 2 band shows a much higher level of entropy and a significant low level of complexity that corresponds to a non-invertible cubic map. Our findings enhance the importance of the gamma band during attention in perceptual feature binding during the visuomotor/imagery tasks.Instituto de Física de Líquidos y Sistemas Biológico

EEG Cortical Neuroimaging during Human Full-Body Movement.

Studying how the human brain functions during full-body movement can increase our understanding of how to diagnose and treat neurological disorders. High-density electroencephalography (EEG) can record brain activity during body movement due to its portability and excellent time resolution. However, EEG is prone to movement artifact, and traditional EEG methods have poor spatial resolution. Combining EEG with independent component analysis (ICA) and inverse source modeling can improve spatial resolution. In my first study, I used EEG and ICA to investigate the biomechanical and neural interplay of performing a complicated cognitive task at different walking speeds. Young, healthy subjects stepped significantly wider when walking with the cognitive task compared to walking alone, but walking speed did not affect cognitive performance (i.e. reaction time and correct responses). EEG results mirrored cognitive performance, in that there were similar event-related desynchronizations in the somatosensory association cortex around encoding at all speeds. For my second study, I addressed the problem of movement artifact in EEG. I created an interface that blocked true electrocortical signals while recording only movement artifact. I quantified the spectral changes in the movement artifact EEG, tested various methods of removing the artifact, and compared their efficacies. Artifact spectral power varied across individuals, electrode locations, and walking speed. None of the cleaning methods removed all artifact. For my third study, I examined cortical spectral power fluctuations and effective connectivity during active and viewed full-body exercise with different combinations of arm and leg effort. Larger spectral fluctuations occurred in the cortex during rhythmic arm exercise compared to rhythmic leg exercise, which suggests that rhythmic arm movement is more cortically driven. The strength and direction of information flow was very similar between the active and viewed exercise conditions, with the right motor cortex being the hub of information flow. These studies provide insight into how the human brain functions during full-body movement and may have applications for rehabilitation after a brain injury or in brain monitoring for improving cognitive performance.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116622/1/jekline_1.pd

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