Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks

Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation X C o r r and peak signal to noise ratio ( P S N R ) (ANOVA, p ˂ 0.05). The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing

脳波信号解析に注目したノイズ除去、特徴抽出、実験観測応用を最適化する数理基盤に関する研究

Electroencephalography (EEG) data inevitably contains a large amount of noise particularly from ocular potentials in tasks with eye-movements and eye-blink, known as electrooculography (EOG) artifact, which has been a crucial issue in the braincomputer- interface (BCI) study. The eye-movements and eye-blinks have different time-frequency properties mixing together in EEGs of interest. This time-frequency characteristic has been substantially dealt with past proposed denoising algorithms relying on the consistent assumption based on the single noise component model. However, the traditional model is not simply applicable for biomedical signals consist of multiple signal components, such as weak EEG signals easily recognized as a noise because of the signal amplitude with respect to the EOG signal. In consideration of the realistic signal contamination, we newly designed the EEG-EOG signal contamination model for quantitative validations of the artifact removal from EEGs, and then proposed the two-stage wavelet shrinkage method with the undecimated wavelet decomposition (UDWT), which is suitable for the signal structure. The features of EEG-EOG signal has been extracted with existing decomposition methods known as Principal Component Analysis (PCA), Independent Component Analysis (ICA) based on a consistent assumption of the orthogonality of signal vectors or statistical independence of signal components. In the viewpoint of the signal morphology such as spiking, waves and signal pattern transitions, A systematic decomposition method is proposed to identify the type of signal components or morphology on the basis of sparsity in time-frequency domain. Morphological Component Analysis (MCA) is extended the traditional concept of signal decomposition including Fourier and wavelet transforms and provided a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases being independent of each other and uniqueness representation, called the concept of “dictionary”. MCA is applied to decompose the real EEG signal and clarified the best combination of dictionaries for the purpose. In this proposed semi-realistic biological signal analysis, target EEG data was prepared as mixture signals of artificial eye movements and blinks and iEEG recorded from electrodes embedded into the brain intracranially and then those signals were successfully decomposed into original types by a linear expansion of waveforms such as redundant transforms: UDWT, DCT,LDCT, DST and DIRAC. The result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST and DIRAC to represent the baseline envelop, multi frequency wave forms and spiking activities individually as representative types of EEG morphologies. MCA proposed method is used in negative-going Bereitschaftspotential (BP). It is associated with the preparation and execution of voluntary movement. Thus far, the BP for simple movements involving either the upper or lower body segment has been studied. However, the BP has not yet been recorded during sit-to-stand movements, which use the upper and lower body segments. Electroencephalograms were recorded during movement. To detect the movement of the upper body segment, a gyro sensor was placed on the back, and to detect the movement of the lower body segment, an electromyogram (EMG) electrode was placed on the surface of the hamstrings and quadriceps. Our study revealed that a negative-going BP was evoked around -3 to -2 seconds before the onset of the upper body movement in the sit-to-stand movement in response to the start cue. The BP had a negative peak before the onset of the movement. The potential was followed by premotor positivity, a motor-related potential, and a reafferent potential. The BP for the sit-to-stand movement had a steeper negative slope (-0.8 to -0.001 seconds) just before the onset of the upper body movement. The slope correlated with the gyro peak and the max amplitude of hamstrings EMG. A BP negative peak value was correlated with the max amplitude of the hamstring EMG. These results suggested that the observed BP is involved in the preparation/execution for a sit-to-stand movement using the upper and lower body. In summary, this thesis is help to pave the practical approach of real time analysis of desired EEG signal of interest toward the implementation of rehabilitation device which may be used for motor disabled people. We also pointed out the EEG-EOG contamination model that helps in removal of the artifacts and explicit dictionaries are representing the EEG morphologies.九州工業大学博士学位論文 学位記番号:生工博甲第290号 学位授与年月日:平成29年3月24日1 Introduction|2 Research Background and Preliminaries|3 Introduction of Morphological Component Analysis|4 Two-Stage Undecimated Wavelet Shrinkage Method|5 Morphologically Decomposition of EEG Signals|6 Bereitschaftspotential for Rise to Stand-Up Behavior九州工業大学平成28年

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

Selection of Mother Wavelet Function for Multi-Channel EEG Signals Analysis during a Working Memory Task

We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions

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

AUTOMATED ARTIFACT REMOVAL AND DETECTION OF MILD COGNITIVE IMPAIRMENT FROM SINGLE CHANNEL ELECTROENCEPHALOGRAPHY SIGNALS FOR REAL-TIME IMPLEMENTATIONS ON WEARABLES

Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has three major aspects: 1) cleaning the EEG signal, 2) detecting MCI, and 3) predicting the severity of the MCI using the data obtained from a single-channel EEG electrode. Artifacts such as eye blink activities can corrupt the EEG signals. We investigate unsupervised and effective removal of ocular artifact (OA) from single-channel streaming raw EEG data. Wavelet transform (WT) decomposition technique was systematically evaluated for effectiveness of OA removal for a single-channel EEG system. Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), is studied with four WT basis functions: haar, coif3, sym3, and bior4.4. The performance of the artifact removal algorithm was evaluated by the correlation coefficients (CC), mutual information (MI), signal to artifact ratio (SAR), normalized mean square error (NMSE), and time-frequency analysis. It is demonstrated that WT can be an effective tool for unsupervised OA removal from single channel EEG data for real-time applications.For the MCI detection from the clean EEG data, we collected the scalp EEG data, while the subjects were stimulated with five auditory speech signals. We extracted 590 features from the Event-Related Potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with Radial Basis Kernel (RBF) (sigma = 10, cost = 102). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI. We also developed a single channel Electro-encephalography (EEG) based MCI severity monitoring algorithm by generating the Montreal Cognitive Assessment (MoCA) scores from the features extracted from EEG. We performed multi-trial and single-trail analysis for the algorithm development of the MCI severity monitoring. We studied Multivariate Regression (MR), Ensemble Regression (ER), Support Vector Regression (SVR), and Ridge Regression (RR) for multi-trial and deep neural regression for the single-trial analysis. In the case of multi-trial, the best result was obtained from the ER. In our single-trial analysis, we constructed the time-frequency image from each trial and feed it to the convolutional deep neural network (CNN). Performance of the regression models was evaluated by the RMSE and the residual analysis. We obtained the best accuracy with the deep neural regression method