Academic literature on the topic 'Noise Electroencephalography'
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Journal articles on the topic "Noise Electroencephalography"
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Huynh, Tuan Van, and Vu Quang Huynh. "Study on method of filtering noises from electroencephalography signals and its application for identification of several electroencephalography signals." Science and Technology Development Journal - Natural Sciences 1, T4 (December 31, 2017): 95–104. http://dx.doi.org/10.32508/stdjns.v1it4.497.
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Electroencephalographic (EEG) signals have usually been affected by different types of noise as 50 Hz noise, mechanical noise caused by body movements, heart disturbance, eye noise... In this paper, methods such as: independent component analysis (independent component analysis-ICA), discrete wavelet transform and design of digital filters, were used to filter the noises, to classify the basic components for EEG signals. Then the mean of energy value was calculated to identify the status of the EEG signals such as blink, thoughts, emotion, smoking and blood pressure. The results of calculations and simulations of signals EEG could demonstrate the efficiency of the method.
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McMillan, Rebecca, Anna Forsyth, Doug Campbell, Gemma Malpas, Elizabeth Maxwell, Juergen Dukart, Joerg F. Hipp, and Suresh Muthukumaraswamy. "Temporal dynamics of the pharmacological MRI response to subanaesthetic ketamine in healthy volunteers: A simultaneous EEG/fMRI study." Journal of Psychopharmacology 33, no. 2 (January 21, 2019): 219–29. http://dx.doi.org/10.1177/0269881118822263.
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Background: Pharmacological magnetic resonance imaging has been used to investigate the neural effects of subanaesthetic ketamine in healthy volunteers. However, the effect of ketamine has been modelled with a single time course and without consideration of physiological noise. Aims: This study aimed to investigate ketamine-induced alterations in resting neural activity using conventional pharmacological magnetic resonance imaging analysis techniques with physiological noise correction, and a novel analysis utilising simultaneously recorded electroencephalography data. Methods: Simultaneous electroencephalography/functional magnetic resonance imaging and physiological data were collected from 30 healthy male participants before and during a subanaesthetic intravenous ketamine infusion. Results: Consistent with previous literature, we show widespread cortical blood-oxygen-level dependent signal increases and decreased blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex following ketamine. However, the latter effect was attenuated by the inclusion of motion regressors and physiological correction in the model. In a novel analysis, we modelled the pharmacological magnetic resonance imaging response with the power time series of seven electroencephalography frequency bands. This showed evidence for distinct temporal time courses of neural responses to ketamine. No electroencephalography power time series correlated with decreased blood-oxygen-level dependent signal in the subgenual anterior cingulate cortex. Conclusions: We suggest the decrease in blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex typically seen in the literature is the result of physiological noise, in particular cardiac pulsatility. Furthermore, modelling the pharmacological magnetic resonance imaging response with a single temporal model does not completely capture the full spectrum of neuronal dynamics. The use of electroencephalography regressors to model the response can increase confidence that the pharmacological magnetic resonance imaging is directly related to underlying neural activity.
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Bruhn, Jörgen, Thomas W. Bouillon, Andreas Hoeft, and Steven L. Shafer. "Artifact Robustness, Inter- and Intraindividual Baseline Stability, and Rational EEG Parameter Selection." Anesthesiology 96, no. 1 (January 1, 2002): 54–59. http://dx.doi.org/10.1097/00000542-200201000-00015.
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Background Artifact robustness (i.e., size of deviation of an electroencephalographic parameter value from baseline caused by artifacts) and baseline stability (i.e., consistency of median baseline values) of electroencephalographic parameters profoundly influence electroencephalography-based pharmacodynamic parameter estimation and the usefulness of the processed electroencephalogram as measure of the arousal state of the central nervous system (depth of anesthesia). In this study, the authors compared the artifact robustness and the interindividual and intraindividual baseline stability of several univariate descriptors of the electroencephalogram (Shannon entropy, approximate entropy, spectral edge frequency 95, delta ratio, and canonical univariate parameter). Methods Electroencephalographic data of 16 healthy volunteers before and after administration of an intravenous bolus of propofol (2 mg/kg body weight) were analyzed. Each volunteer was studied twice. The baseline electroencephalogram was recorded for a median of 18 min before drug administration. For each electroencephalographic descriptor, the authors calculated the following: (1) baseline variability (= (median baseline - median effect) [i.e., signal]/SD baseline [i.e., noise]) without artifact rejection; (2) baseline variability with artifact rejection; and (3) baseline stability within and between individuals (= (median baseline - median effect) averaged over all volunteers/SD of all median baselines). Results Without artifact rejection, Shannon entropy and canonical univariate parameter displayed the highest signal-to-noise ratio. After artifact rejection, approximate entropy, Shannon entropy, and the canonical univariate parameter displayed the highest signal-to-noise ratio. Baseline stability within and between individuals was highest for approximate entropy. Conclusions With regard to robustness against artifacts, the electroencephalographic entropy parameters and the canonical univariate parameter were superior to spectral edge frequency 95 and delta ratio. Electroencephalographic approximate entropy displayed the best interindividual and intraindividual baseline stability.
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Jamison, Caroline, Steve J. Aiken, Michael Kiefte, Aaron J. Newman, Manohar Bance, and Lauren Sculthorpe-Petley. "Preliminary Investigation of the Passively Evoked N400 as a Tool for Estimating Speech-in-Noise Thresholds." American Journal of Audiology 25, no. 4 (December 2016): 344–58. http://dx.doi.org/10.1044/2016_aja-15-0080.
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PurposeSpeech-in-noise testing relies on a number of factors beyond the auditory system, such as cognitive function, compliance, and motor function. It may be possible to avoid these limitations by using electroencephalography. The present study explored this possibility using the N400.MethodEleven adults with typical hearing heard high-constraint sentences with congruent and incongruent terminal words in the presence of speech-shaped noise. Participants ignored all auditory stimulation and watched a video. The signal-to-noise ratio (SNR) was varied around each participant's behavioral threshold during electroencephalography recording. Speech was also heard in quiet.ResultsThe amplitude of the N400 effect exhibited a nonlinear relationship with SNR. In the presence of background noise, amplitude decreased from high (+4 dB) to low (+1 dB) SNR but increased dramatically at threshold before decreasing again at subthreshold SNR (−2 dB).ConclusionsThe SNR of speech in noise modulates the amplitude of the N400 effect to semantic anomalies in a nonlinear fashion. These results are the first to demonstrate modulation of the passively evoked N400 by SNR in speech-shaped noise and represent a first step toward the end goal of developing an N400-based physiological metric for speech-in-noise testing.
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Alyan, Emad, Naufal M. Saad, Nidal Kamel, Mohd Zuki Yusoff, Mohd Azman Zakariya, Mohammad Abdul Rahman, Christophe Guillet, and Frederic Merienne. "Frontal Electroencephalogram Alpha Asymmetry during Mental Stress Related to Workplace Noise." Sensors 21, no. 6 (March 11, 2021): 1968. http://dx.doi.org/10.3390/s21061968.
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This study aims to investigate the effects of workplace noise on neural activity and alpha asymmetries of the prefrontal cortex (PFC) during mental stress conditions. Workplace noise exposure is a pervasive environmental pollutant and is negatively linked to cognitive effects and selective attention. Generally, the stress theory is assumed to underlie the impact of noise on health. Evidence for the impacts of workplace noise on mental stress is lacking. Fifteen healthy volunteer subjects performed the Montreal imaging stress task in quiet and noisy workplaces while their brain activity was recorded using electroencephalography. The salivary alpha-amylase (sAA) was measured before and immediately after each tested workplace to evaluate the stress level. The results showed a decrease in alpha rhythms, or an increase in cortical activity, of the PFC for all participants at the noisy workplace. Further analysis of alpha asymmetry revealed a greater significant relative right frontal activation of the noisy workplace group at electrode pairs F4-F3 but not F8-F7. Furthermore, a significant increase in sAA activity was observed in all participants at the noisy workplace, demonstrating the presence of stress. The findings provide critical information on the effects of workplace noise-related stress that might be neglected during mental stress evaluations.
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Alkhorshid, Daniel Rostami, Seyyedeh Fatemeh Molaeezadeh, and Mikaeil Rostami Alkhorshid. "Analysis: Electroencephalography Acquisition System: Analog Design." Biomedical Instrumentation & Technology 54, no. 5 (September 1, 2020): 346–51. http://dx.doi.org/10.2345/0899-8205-54.5.346.
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Abstract Electroencephalography (EEG) is a sensitive and weak biosignal that varies from person to person. It is easily affected by noise and artifacts. Hence, maintaining the signal integrity to design an EEG acquisition system is crucial. This article proposes an analog design for acquiring EEG signals. The proposed design consists of eight blocks: (1) a radio-frequency interference filter and electro-static discharge protection, (2) a preamplifier and second-order high-pass filter with feedback topology and an unblocking mechanism, (3) a driven right leg circuit, (4) two-stage main and variable amplifiers, (5) an eight-order anti-aliasing filter, (6) a six-order 50-Hz notch filter (optional), (7) an opto-isolator circuit, and (8) an isolated power supply. The maximum gain of the design is approximately 94 dB, and its bandwidth ranges from approximately 0.18 to 120 Hz. The depth of the 50-Hz notch filter is −35 dB. Using this filter is optional because it causes EEG integrity problems in frequencies ranging from 40 to 60 Hz.
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Frescura, Alessia, Pyoung Jik Lee, Jeong-Ho Jeong, and Yoshiharu Soeta. "Electroencephalogram (EEG) responses to indoor sound sources in wooden residential buildings." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 4 (August 1, 2021): 1989–98. http://dx.doi.org/10.3397/in-2021-2021.
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The present study aimed to explore relationships between physiological and subjective responses to indoor sounds. Specifically, The electroencephalograms (EEG) responses to neighbour sounds in wooden dwellings were investigated. Listening tests were performed to collect EEG data in distinct acoustics scenarios. Experimental work was carried out in a laboratory with a low background noise level. A series of impact and airborne sounds were presented through loudspeakers and subwoofer, while participants sat comfortably in the simulated living room wearing the EEG headset (B-alert X24 system). The impact sound sources were an adult walking and a child running recorded in a laboratory equipped with different floor configurations. Two airborne sounds (a live conversation and a piece of classical piano music) were digitally filtered to resemble good and poor sound insulation performances of vertical partitions. The experiment consisted of two sessions, namely, the evaluation of individual sounds and the evaluation of the combined noise sources. In the second session, pairs of an impact and an airborne sound were presented. During the listening test, electroencephalography alpha reactivity (α-EEG) and electroencephalography beta reactivity (β-EEG) were monitored. In addition, participants were asked to rate noise annoyance using an 11-point scale.
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Shim, Allison I., Bruce G. Berg, and Ramesh Srinivasan. "Auditory detection of amplitude modulation in psychophysical notched noise task and electroencephalography." Journal of the Acoustical Society of America 122, no. 5 (2007): 3064. http://dx.doi.org/10.1121/1.2942935.
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Choi, Jee Hyun, Klaus Peter Koch, Wigand Poppendieck, Mina Lee, and Hee-Sup Shin. "High Resolution Electroencephalography in Freely Moving Mice." Journal of Neurophysiology 104, no. 3 (September 2010): 1825–34. http://dx.doi.org/10.1152/jn.00188.2010.
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Electroencephalography (EEG) is a standard tool for monitoring brain states in humans. Understanding the molecular and cellular mechanisms underlying diverse EEG rhythms can be facilitated by using mouse models under molecular, pharmacological, or electrophysiological manipulations. The small size of the mouse brain, however, poses a severe limitation in the spatial information of EEG. To overcome this limitation, we devised a polyimide based microelectrode array (PBM array) with nanofabrication technologies. The microelectrode contains 32 electrodes, weighs 150 mg, and yields noise-insensitive signals when applied on the mouse skull. The high-density microelectrode allowed both global and focused mapping of high resolution EEG (HR-EEG) in the mouse brain. Mapping and dynamical analysis tools also have been developed to visualize the dynamical changes of spatially resolved mouse EEG. We demonstrated the validity and utility of mouse EEG in localization of the seizure onset in absence seizure model and phase dynamics of abnormal theta rhythm in transgenic mice. Dynamic tracking of the EEG map in genetically modified mice under freely moving conditions should allow study of the molecular and cellular mechanisms underlying the generation and dynamics of diverse EEG rhythms.
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Goldenholz, Daniel M., Seppo P. Ahlfors, Matti S. Hämäläinen, Dahlia Sharon, Mamiko Ishitobi, Lucia M. Vaina, and Steven M. Stufflebeam. "Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography." Human Brain Mapping 30, no. 4 (April 2009): 1077–86. http://dx.doi.org/10.1002/hbm.20571.
Full textDissertations / Theses on the topic "Noise Electroencephalography"
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Foster, Paul Stephen. "Graded Cerebral Activation to Noise: Behavioral and Cardiovascular Effects." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11146.
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Research has indicated that the frontal and temporal lobes are involved in the mediation of heart rate and blood pressure. However, whereas these regions of the brain have been identified in the mediation of heart rate and blood pressure, the specific cerebral processes involved in determining the direction and magnitude of change in heart rate and blood pressure has not been adequately addressed. The present paper proposes that changes in the magnitude of cerebral activation between the left and right frontal and temporal lobes is partly that which determines the direction and magnitude of changes in heart rate and blood pressure. The present investigation sought to test part of this proposition, namely, that increasing magnitude of cerebral activity within the right anterior temporal region generates increasing levels of sympathetic control of heart rate and blood pressure and that the right lateral frontal region acts to inhibit sympathetic activity. A total of 45 right handed men, with no history of significant head injury, were exposed to 55 dB, 75 dB, and 90 dB white noise presentations. Right frontal lobe functioning was assessed by performance on the Ruff Figural Fluency Test (RFFT), with the participants scoring in the lower one-third classified as Low Fluency. Those scoring in the upper one-third were classified as High Fluency. Quantitative electroencephalography, measured at 19 electrodes sites arranged according to the International 10/20 System, as well as heart rate and blood pressure responses to white noise presentation were measured. Although the results failed to support any of the hypotheses concerning the effects of varying intensity of white noise on cardiovascular activity, partial support was found for the hypotheses that varying intensity of white noise would generate differential changes in high beta magnitude between the Low and High Fluency groups. The results are discussed in terms of support for the model being tested. Alternative explanations of the findings are also provided that demonstrate correspondence between the QEEG and cardiovascular data. Finally, limitations of the model and the methods of the present investigation are discussed and suggestions for improvement are provided.Ph. D.
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Massias, Mathurin. "Sparse high dimensional regression in the presence of colored heteroscedastic noise : application to M/EEG source imaging." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT053.
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Parmi les techniques d’imagerie cerébrale, la magneto- et l’électro-encéphalographie se distinguent pour leur faible degré d’invasivité et leur excellente résolution temporelle. La reconstruction de l’activité neuronale à partir de l’enregistrement des champs électriques et magnétiques constitue un problème inverse extr êmement mal posé, auquel il est nécessaire d’ajouter des contraintes pour le résoudre. Une approche populaire, empruntée dans ce manuscrit, est de postuler que la solution est parcimonieuse spatialement, ce qui peut s’obtenir par une pénalisation L2/1. Cependant, ce type de régularisation nécessite de résoudre des problèmes d’optimisation non-lisses en grande dimension, avec des méthodes itératives dont la performance se dégrade avec la dimension. De plus, les enregistrements M/EEG sont typiquement corrompus par un fort bruit coloré, allant à l’encontre des hypothèses classiques de résolution des problèmes inverses. Dans cette thèse, nous proposons d’abord une accélération des algorithmes itératifs utilisés pour résoudre le problème bio-magnétique avec régularisation L2/1. Les améliorations classiques (règles de filtrage et ensemble actifs), tirent parti de la parcimonie de la solution: elles ignorent les sources cérébrales inactives, et réduisent ainsi la dimension du problème. Nous introduisons une nouvelle technique d’ensemble actifs, reposant sur les règles de filtrage les plus performantes actuellement. Nous proposons des techniques duales avancées, qui permettent un contrôle plus fin de l’optimalité et améliorent les techniques d’identification de prédicteurs. Notre construction duale extrapole la structure Vectorielle Autoregressive des iterés duaux, régularité que nous relions aux propriétés d’identification de support des algorithmes proximaux. En plus du problème inverse bio-magnétique, l’approche proposée est appliquée à l’ensemble des modèles linéaires g énéralisés r égularisés L1. Deuxièmement, nous introduisons de nouveaux estimateurs concomitants pour la régression multitâche, conçus pour traiter du bruit gaussien correlé. Le probleme d’optimisation sous-jacent est convexe, et présente une structure “lisse + proximable” attrayante ; nous lions la formulation de ce problème au lissage des normes de SchattenUnderstanding the functioning of the brain under normal and pathological conditions is one of the challenges of the 21textsuperscript{st} century.In the last decades, neuroimaging has radically affected clinical and cognitive neurosciences.Amongst neuroimaging techniques, magneto- and electroencephalography (M/EEG) stand out for two reasons: their non-invasiveness, and their excellent time resolution.Reconstructing the neural activity from the recordings of magnetic field and electric potentials is the so-called bio-magnetic inverse problem.Because of the limited number of sensors, this inverse problem is severely ill-posed, and additional constraints must be imposed in order to solve it.A popular approach, considered in this manuscript, is to assume spatial sparsity of the solution: only a few brain regions are involved in a short and specific cognitive task.Solutions exhibiting such a neurophysiologically plausible sparsity pattern can be obtained through L21-penalized regression approaches.However, this regularization requires to solve time-consuming high-dimensional and non-smooth optimization problems, with iterative (block) proximal gradients solvers.% Issues of M/EEG: noise:Additionally, M/EEG recordings are usually corrupted by strong non-white noise, which breaks the classical statistical assumptions of inverse problems. To circumvent this, it is customary to whiten the data as a preprocessing step,and to average multiple repetitions of the same experiment to increase the signal-to-noise ratio.Averaging measurements has the drawback of removing brain responses which are not phase-locked, ie do not happen at a fixed latency after the stimuli presentation onset.%Making it faster.In this work, we first propose speed improvements of iterative solvers used for the L21-regularized bio-magnetic inverse problem.Typical improvements, screening and working sets, exploit the sparsity of the solution: by identifying inactive brain sources, they reduce the dimensionality of the optimization problem.We introduce a new working set policy, derived from the state-of-the-art Gap safe screening rules.In this framework, we also propose duality improvements, yielding a tighter control of optimality and improving feature identification techniques.This dual construction extrapolates on an asymptotic Vector AutoRegressive regularity of the dual iterates, which we connect to manifold identification of proximal algorithms.Beyond the L21-regularized bio-magnetic inverse problem, the proposed methods apply to the whole class of sparse Generalized Linear Models.%Better handling of the noiseSecond, we introduce new concomitant estimators for multitask regression.Along with the neural sources estimation, concomitant estimators jointly estimate the noise covariance matrix.We design them to handle non-white Gaussian noise, and to exploit the multiple repetitions nature of M/EEG experiments.Instead of averaging the observations, our proposed method, CLaR, uses them all for a better estimation of the noise.The underlying optimization problem is jointly convex in the regression coefficients and the noise variable, with a ``smooth + proximable'' composite structure.It is therefore solvable via standard alternate minimization, for which we apply the improvements detailed in the first part.We provide a theoretical analysis of our objective function, linking it to the smoothing of Schatten norms.We demonstrate the benefits of the proposed approach for source localization on real M/EEG datasets.Our improved solvers and refined modeling of the noise pave the way for a faster and more statistically efficient processing of M/EEG recordings, allowing for interactive data analysis and scaling approaches to larger and larger M/EEG datasets
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Kawaguchi, Hirokazu. "Signal Extraction and Noise Removal Methods for Multichannel Electroencephalographic Data." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/188593.
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Škutková, Helena. "Akustický generátor pro buzení evokovaných potenciálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218222.
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Evoked potentials are electric brain response to external stimulus. They are important diagnostic no visual method in neurology. For their excitation use of different of kinds stimulation, most often: visual, auditory, somatosenzory, olfactory and gustatory. Evoked potentials are objective method for measurement sense perception. This master’s thesis is specialized to auditory evoked potentials and design acoustic generator for their stimulation. Auditory evoked potentials are primary used for objective audiometry, but they have another usage. In the first place, application is specialized on health sector. The aim of this master’s thesis is compact specified medical requirements with available technical resources.
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Hajipour, Sardouie Sepideh. "Signal subspace identification for epileptic source localization from electroencephalographic data." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S185/document.
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Lorsque l'on enregistre l'activité cérébrale en électroencéphalographie (EEG) de surface, le signal d'intérêt est fréquemment bruité par des activités différentes provenant de différentes sources de bruit telles que l'activité musculaire. Le débruitage de l'EEG est donc une étape de pré-traitement important dans certaines applications, telles que la localisation de source. Dans cette thèse, nous proposons six méthodes permettant la suppression du bruit de signaux EEG dans le cas particulier des activités enregistrées chez les patients épileptiques soit en période intercritique (pointes) soit en période critique (décharges). Les deux premières méthodes, qui sont fondées sur la décomposition généralisée en valeurs propres (GEVD) et sur le débruitage par séparation de sources (DSS), sont utilisées pour débruiter des signaux EEG épileptiques intercritiques. Pour extraire l'information a priori requise par GEVD et DSS, nous proposons une série d'étapes de prétraitement, comprenant la détection de pointes, l'extraction du support des pointes et le regroupement des pointes impliquées dans chaque source d'intérêt. Deux autres méthodes, appelées Temps Fréquence (TF) -GEVD et TF-DSS, sont également proposées afin de débruiter les signaux EEG critiques. Dans ce cas on extrait la signature temps-fréquence de la décharge critique par la méthode d'analyse de corrélation canonique. Nous proposons également une méthode d'Analyse en Composantes Indépendantes (ICA), appelé JDICA, basée sur une stratégie d'optimisation de type Jacobi. De plus, nous proposons un nouvel algorithme direct de décomposition canonique polyadique (CP), appelé SSD-CP, pour calculer la décomposition CP de tableaux à valeurs complexes. L'algorithme proposé est basé sur la décomposition de Schur simultanée (SSD) de matrices particulières dérivées du tableau à traiter. Nous proposons également un nouvel algorithme pour calculer la SSD de plusieurs matrices à valeurs complexes. Les deux derniers algorithmes sont utilisés pour débruiter des données intercritiques et critiques. Nous évaluons la performance des méthodes proposées pour débruiter les signaux EEG (simulés ou réels) présentant des activités intercritiques et critiques épileptiques bruitées par des artéfacts musculaires. Dans le cas des données simulées, l'efficacité de chacune de ces méthodes est évaluée d'une part en calculant l'erreur quadratique moyenne normalisée entre les signaux originaux et débruités, et d'autre part en comparant les résultats de localisation de sources, obtenus à partir des signaux non bruités, bruités, et débruités. Pour les données intercritiques et critiques, nous présentons également quelques exemples sur données réelles enregistrées chez des patients souffrant d'épilepsie partielleIn the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing stages including spike peak detection, extraction of exact time support of spikes and clustering of spikes involved in each source of interest. Two other methods, called Time Frequency (TF)-GEVD and TF-DSS, are also proposed in order to denoise ictal EEG signals for which the time-frequency signature is extracted using the Canonical Correlation Analysis method. We also propose a deflationary Independent Component Analysis (ICA) method, called JDICA, that is based on Jacobi-like iterations. Moreover, we propose a new direct algorithm, called SSD-CP, to compute the Canonical Polyadic (CP) decomposition of complex-valued multi-way arrays. The proposed algorithm is based on the Simultaneous Schur Decomposition (SSD) of particular matrices derived from the array to process. We also propose a new Jacobi-like algorithm to calculate the SSD of several complex-valued matrices. The last two algorithms are used to denoise both interictal and ictal data. We evaluate the performance of the proposed methods to denoise both simulated and real epileptic EEG data with interictal or ictal activity contaminated with muscular activity. In the case of simulated data, the effectiveness of the proposed algorithms is evaluated in terms of Relative Root Mean Square Error between the original noise-free signals and the denoised ones, number of required ops and the location of the original and denoised epileptic sources. For both interictal and ictal data, we present some examples on real data recorded in patients with a drug-resistant partial epilepsy
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Zoefel, Benedikt. "Phase entrainment and perceptual cycles in audition and vision." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30232/document.
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Des travaux récents indiquent qu'il existe des différences fondamentales entre les systèmes visuel et auditif: tandis que le premier semble échantillonner le flux d'information en provenance de l'environnement, en passant d'un "instantané" à un autre (créant ainsi des cycles perceptifs), la plupart des expériences destinées à examiner ce phénomène de discrétisation dans le système auditif ont mené à des résultats mitigés. Dans cette thèse, au travers de deux expériences de psychophysique, nous montrons que le sous-échantillonnage de l'information à l'entrée des systèmes perceptifs est en effet plus destructif pour l'audition que pour la vision. Cependant, nous révélons que des cycles perceptifs dans le système auditif pourraient exister à un niveau élevé du traitement de l'information. En outre, nos résultats suggèrent que du fait des fluctuations rapides du flot des sons en provenance de l'environnement, le système auditif tend à avoir son activité alignée sur la structure rythmique de ce flux. En synchronisant la phase des oscillations neuronales, elles-mêmes correspondant à différents états d'excitabilité, le système auditif pourrait optimiser activement le moment d'arrivée de ses "instantanés" et ainsi favoriser le traitement des informations pertinentes par rapport aux événements de moindre importance. Non seulement nos résultats montrent que cet entrainement de la phase des oscillations neuronales a des conséquences importantes sur la façon dont sont perçus deux flux auditifs présentés simultanément ; mais de plus, ils démontrent que l'entraînement de phase par un flux langagier inclut des mécanismes de haut niveau. Dans ce but, nous avons créé des stimuli parole/bruit dans lesquels les fluctuations de l'amplitude et du contenu spectral de la parole ont été enlevés, tout en conservant l'information phonétique et l'intelligibilité. Leur utilisation nous a permis de démontrer, au travers de plusieurs expériences, que le système auditif se synchronise à ces stimuli. Plus précisément, la perception, estimée par la détection d'un clic intégré dans les stimuli parole/bruit, et les oscillations neuronales, mesurées par Electroencéphalographie chez l'humain et à l'aide d'enregistrements intracrâniens dans le cortex auditif chez le singe, suivent la rythmique "de haut niveau" liée à la parole. En résumé, les résultats présentés ici suggèrent que les oscillations neuronales sont un mécanisme important pour la discrétisation des informations en provenance de l'environnement en vue de leur traitement par le cerveau, non seulement dans la vision, mais aussi dans l'audition. Pourtant, il semble exister des différences fondamentales entre les deux systèmes: contrairement au système visuel, il est essentiel pour le système auditif de se synchroniser (par entraînement de phase) à son environnement, avec un échantillonnage du flux des informations vraisemblablement réalisé à un niveau hiérarchique élevéRecent research indicates fundamental differences between the auditory and visual systems: Whereas the visual system seems to sample its environment, cycling between "snapshots" at discrete moments in time (creating perceptual cycles), most attempts at discovering discrete perception in the auditory system failed. Here, we show in two psychophysical experiments that subsampling the very input to the visual and auditory systems is indeed more disruptive for audition; however, the existence of perceptual cycles in the auditory system is possible if they operate on a relatively high level of auditory processing. Moreover, we suggest that the auditory system, due to the rapidly fluctuating nature of its input, might rely to a particularly strong degree on phase entrainment, the alignment between neural activity and the rhythmic structure of its input: By using the low and high excitability phases of neural oscillations, the auditory system might actively control the timing of its "snapshots" and thereby amplify relevant information whereas irrelevant events are suppressed. Not only do our results suggest that the oscillatory phase has important consequences on how simultaneous auditory inputs are perceived; additionally, we can show that phase entrainment to speech sound does entail an active high-level mechanism. We do so by using specifically constructed speech/noise sounds in which fluctuations in low-level features (amplitude and spectral content) of speech have been removed, but intelligibility and high-level features (including, but not restricted to phonetic information) have been conserved. We demonstrate, in several experiments, that the auditory system can entrain to these stimuli, as both perception (the detection of a click embedded in the speech/noise stimuli) and neural oscillations (measured with electroencephalography, EEG, and in intracranial recordings in primary auditory cortex of the monkey) follow the conserved "high-level" rhythm of speech. Taken together, the results presented here suggest that, not only in vision, but also in audition, neural oscillations are an important tool for the discretization and processing of the brain's input. However, there seem to be fundamental differences between the two systems: In contrast to the visual system, it is critical for the auditory system to adapt (via phase entrainment) to its environment, and input subsampling is done most likely on a hierarchically high level of stimulus processing
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Ramos, Camila Davi. "Caracterização do eletroencefalograma normal em situação de vigília: elaboração da base de dados e análise quantitativa." Universidade Federal de Uberlândia, 2017. https://repositorio.ufu.br/handle/123456789/19571.
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O sinal EEG, cujas informações descrevem o comportamento elétrico do córtex cerebral, apesar de ser bastante utilizado para diagnósticos, principalmente de patologias como epilepsia, no Brasil ainda não é usual o monitoramento contínuo em ambiente de UTI em hospitais públicos. Diante disso, e partindo do pressuposto que estudos sobre o EEG normal, registrado em pessoas sem problemas neurológicos, são escassos, a criação de uma base de registros de EEG normal e análise quantitativa da mesma se faz necessária para que, por meio dos resultados obtidos, padrões normais possam ser estabelecidos e por meio deles a identificação de parâmetros patológicos se torne mais eficaz. Nesse projeto foi elaborada uma base de dados de EEG, com total de 100 registros válidos, advindos de voluntários normais e saudáveis. E a partir desses registros a situação de vigília e olhos fechados foi analisada sob o aspecto de três quantificadores distintos, sendo eles, Porcentagem de Contribuição de Potência (PCP), Frequência Mediana (FM) e Coerência, ambos avaliando o sinal no domínio da frequência. A fim de obter comparações para os resultados obtidos pela análise dos dados do EEG normal, foram utilizados 128 registros de EEG em situação de coma, com diferentes tipos de etiologias e desfechos. Os ritmos que apresentaram maiores distinções entre normal e coma foram Delta e Alfa, principalmente para o quantificador FM. Notou-se que o PCP avaliou características de potência e portanto sintetizou as informações de energia de cada ritmo cerebral tanto em EEG normal quanto em EEG coma. Já FM traz informações de valores de frequências em que há maior concentração de potência, e por fim o quantificador coerência informa o grau de semelhança entre o hemisfério direito e o esquerdo do cérebro. Sendo assim não foi possível afirmar qual dos quantificadores apresentou melhores resultados, visto que cada um trata-se de uma características distintas.The EEG signal, whose information describes the electrical behavior of the cerebral cortex, although it is widely used for diagnoses, mainly of pathologies such as epilepsy, in Brazil it is still not usual to monitor the ICU environment in public hospitals. Considering this, and assuming that studies on normal EEG, registered in people without neurological problems, are scarce, the creation of a base of normal EEG registers and quantitative analysis of it is necessary so that, through the obtained results, Normal patterns can be established and through them, the identification of pathological parameters becomes more effective. In this project, an EEG database was developed, with 100 valid records from normal and healthy volunteers. In addition, from these records, the waking and closed eyes situation was analyzed under the aspect of three distinct quantifiers, being: Power Contribution Percentage (PCP), Median Frequency (FM) and Coherence, both evaluating the signal in the frequency domain. In order to obtain comparisons for the results obtained by the analysis of the normal EEG data, 128 EEG records were used in coma, with different types of etiologies and outcomes. The rhythms that presented the highest distinctions between normal and coma were Delta and Alpha, mainly for the FM quantifier. It was noted that PCP evaluated power characteristics and therefore synthesized the energy information of each brain rhythm in both normal EEG and EEG coma. Already FM brings information of values of frequencies in which there is greater concentration of power, and finally the quantifier coherence informs the degree of similarity between the right and left hemisphere of the brain. Thus, it was not possible to say which of the quantifiers presented better results, since each one is a distinct characterization.
Dissertação (Mestrado)
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Perrot, Xavier. "Modulation centrale du fonctionnement cochléaire chez l’humain : activation et plasticité." Thesis, Lyon 2, 2009. http://www.theses.fr/2009LYO29998.
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Le système auditif possède deux particularités. En périphérie, les mécanismes cochléaires actifs (MCA), sous-tendus par la motilité des cellules ciliées externes (CCE), interviennent dans la sensibilité auditive et la sélectivité fréquentielle. Sur le versant central, le système efférent olivocochléaire médian (SEOCM), qui se projette sur les CCE et module les MCA, améliore la perception auditive en milieu bruité. Sur le plan exploratoire, ces deux processus peuvent être évalués grâce aux otoémissions acoustiques provoquées (OEAP) et leur suppression controlatérale. Par ailleurs, des résultats expérimentaux chez l’animal ont montré l’existence d’un rétrocontrôle exercé par le système auditif corticofuge descendant (SACD) sur la cochlée, via le SEOCM.Le présent travail comporte trois études réalisées chez l’humain, visant à explorer les interactions entre SACD, SEOCM et MCA. Les études 1 et 2, utilisant une méthodologie innovante chez des patients épileptiques réalisant une stéréo-électroencéphalographie, ont révélé un effet atténuateur différentiel de la stimulation électrique intracérébrale sur l’amplitude des OEAP, en fonction des modalités de stimulation, ainsi qu’une variabilité de cet effet selon les caractéristiques de l’épilepsie. L’étude 3 a montré un renforcement bilatéral de l’activité du SEOCM chez des musiciens professionnels.Pris dans leur ensemble, ces résultats fournissent d’une part, des arguments directs et indirects en faveur de l’existence d’un SACD fonctionnel chez l’humain. D’autre part, des phénomènes de plasticité à long terme, pathologique ou supranormale, seraient susceptibles de modifier l’activité de cette voie cortico-olivocochléaireThe auditory system has two special features. At peripheral level, active cochlear micromechanisms (ACM), underlain by motility of outer hair cells (OHC), are involved in auditory sensitivity and frequency selectivity. At central level, the medial olivocochlear efferent system (MOCES), which directly projects onto OHC to modulate ACM, improves auditory perception in noise. From an exploratory point of view, both processes can be assessed through transient evoked otoacoustic emissions (TEOAE) and the procedure of contralateral suppression. In addition, experimental data in animals have disclosed a top-down control exerted by corticofugal descending auditory system (CDAS) on cochlea, via MOCES.The present work comprises three studies carried out in human, aiming to investigate interactions between CDAS, MOCES and ACM. The first and second studies, based on an innovative experimental procedure in epileptic patients undergoing presurgical stereoelectroencephalography, have revealed a differential attenuation effect of intracerebral electrical stimulation on TEOAE amplitude depending on stimulation modalities, as well as a variability of this effect depending on the clinical history of epilepsy. The third study has shown a bilateral enhancement of MOCES activity in professional musicians.Taking together, these results provide direct and indirect evidence for the existence of a functional CDAS in humans. Moreover, possible long-term plasticity phenomenon, either pathological –as in epileptic patients– or supernormal –as in professional musicians– may change cortico-olivocochlear activity
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Liu, Fong-Kuo, and 劉豐國. "Assessment of human response to noise using electroencephalography." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/30514917290605534100.
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碩士嘉南藥理科技大學
產業安全衛生與防災研究所
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In this study, an attempt was made to evaluate human response to noise using electroencephalography (EEG) power indices and P300 component of event-related potential (ERP). Three different volume (80, 85, and 90 dBA) of noise sources including three frequencies of pure tone (250, 1000, 4000 Hz), were generated via function generator, amplified and output form speaker. Twenty subjects conducted EEG measures and acquired the ERP induced from a modified Flanker task under various sound environment including silence, and 9 noises conditions, 80dBA in 250, 1000, 4000Hz; 85dBA in 250, 1000, 4000Hz; 90dBA in 250, 1000, 4000Hz. Behavior response and EEG measurement were recorded on a personal computer. For EEG power, three basic indices and three ratio indices were calculated from preprocessed EEG signals. The heart beat and blood pressure of participant was measured after EEG measurement. The basic indices of θ and α bands showed significant increase and decrease respectively between silence and noise conditions at 90dBA in 250Hz, 1000Hz, 4000Hz. The basic index of β revealed no significant difference between silence and noise conditions at 90dBA in various frequencies. On the other hand, ratio indices of β/α and(α+θ)/β showed indistinct variation in different frequencies; The latency and the amplitude of ERP P300, exhibit an increased tendency, especially under noise condition at 90dBA in 1000Hz. For Eriksen flanker task, the number of mistake has significant increase at 85dBA in 250Hz, 1000Hz and 4000Hz; 90dBA in 250Hz, 1000Hz and 4000Hz, respectively. Moreover, reaction times were decreased in different noise conditions. For the blood pressure, only at 90dBA in 1000Hz has significant increase. However, the heartbeat revealed indistinct variation in different noise condition. The study finds that EEG power, error ratio, reaction time and blood pressure has significant increase at 90dBA of three stimulate noise source compared with background noise, the results indicate the subjects’ arousal and attention levels were decreased, especially at 90dBA in 1000Hz the level decreased more than the other two frequencies. The study would offer the references for physiological indices of noise effect and management for no
Book chapters on the topic "Noise Electroencephalography"
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Saikia, Angana, and Sudip Paul. "Application of Deep Learning for EEG." In Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering, 106–23. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2120-5.ch007.
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Deep learning is a relatively new branch of machine learning, which has been used in a variety of biomedical applications. It has been used to analyze different physiological signals and gain better understanding of human physiology for automated diagnosis of abnormal conditions. It is used in the classification of electroencephalography signals. Most of the present research has continued to use manual feature extraction methods followed by a traditional classifier, such as support vector machine or logistic regression. This is largely due to the low number of samples per experiment, high-dimensional nature of the data, and the difficulty in finding appropriate deep learning architectures for classification of EEG signals. One of the challenges in modeling cognitive events from EEG data is finding representations that are invariant to inter- and intra-subject differences as well as the inherent noise associated with EEG data collection. Herein, the authors explore the capabilities of the recent deep learning techniques for modeling cognitive events from EEG data.
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"Electroencephalographic coherence for exposure to low-frequency noise." In Management, Information and Educational Engineering, 461–64. CRC Press, 2015. http://dx.doi.org/10.1201/b18558-100.
Full textConference papers on the topic "Noise Electroencephalography"
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Wu, Menglu, and Xiaolin Chen. "Tikhonov Regularization Methods for the Inverse Scalp Electroencephalography." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-10538.
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Electroencephalography (EEG) source localization of brain activity is of high diagnostic value. Noninvasive numerical procedures can be developed to help reconstruct the cortical brain activities from the low-spatial-resolution scalp EEG measurement. In this paper, Tikhonov regularization methods are employed to tackle the solution difficulty associated with the ill-posed reconstruction problem. Three different techniques, namely the L-curve method, the generalized cross validation (GCV) and the discrepancy principle (DP), are implemented to help identify an optimum parameter for the numerical regularization. The numerical procedures are verified by comparing reconstruction results with available theoretical potential solutions for a simplified concentric sphere head model. All three parameter selection methods achieve good results and the L-curve method produces the best regularization effect among the three when the noise level is high in the contaminated scalp data input. More studies are performed on a computational model of an anatomically realistic human head. Our results show that the combination of Tikhonov regularization with the L-curve parameter selection method can effectively regularize the ill-posed inverse EEG problem for brain potential reconstruction.
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Alam, MD Erfanul, and Biswanath Samanta. "Performance Evaluation of Empirical Mode Decomposition for EEG Artifact Removal." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71647.
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Electroencephalography measures the sum of the post-synaptic potentials generated by many neurons having the same radial orientation with respect to the scalp. The electroen-cephalographic signals (EEG) are weak and often contaminated with different artifacts that have biological and external sources. Reliable pre-processing of the noisy, non-linear, and non-stationary brain activity signals is needed for successful extraction of characteristic features in motor imagery based brain-computer interface (MI-BCI). In this work, a signal processing technique, namely, empirical mode decomposition (EMD), has been proposed for processing EEG signals acquired from volunteer subjects for characterization and identification of motor imagery (MI) activities. EMD has been used for removal of artifacts like electrooculography (EOG) that strongly appears in frontal electrodes of EEG and the power line noise that is mainly produced by the fluorescent light. The performance of EMD has been compared with two extensions, ensemble empirical mode decomposition (EEMD) and multivariate empirical mode decomposition (MEMD)using signal to noise ratio (SNR). The maximum SNR values found for EMD, EEMD and MEMD are 4.30, 7.64 and 10.62 respectively for the EEG signals considered.
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Ghorbanian, Parham, Subramanian Ramakrishnan, and Hashem Ashrafiuon. "Stochastic Oscillator Model of EEG Based on Information Content and Complexity." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-5929.
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In this study, a stochastic Duffing - van der Pol coupled two oscillator system is designed to produce output matching the information content, complexity measure, and frequency content of actual electroencephalography (EEG) signals. This is achieved by deriving the oscillator model parameters and noise intensity using an optimization scheme whose objective is to minimize a weighed average of errors in sample entropy, Shannon entropy, and powers of the major brain frequency bands. The signals produced by the optimal model are then compared with the EEG signal using phase portrait reconstruction. The study shows that the model can effectively reproduce signals that match EEG recorded under different brain states with respect to multiple metrics.
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Bedoya, Carol, Daniel Estrada, Sandra Trujillo, Natalia Trujillo, David Pineda, and Jose D. Lopez. "Automatic component rejection based on fuzzy clustering for noise reduction in electroencephalographic signals." In 2013 XVIII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA). IEEE, 2013. http://dx.doi.org/10.1109/stsiva.2013.6644922.
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