260 research outputs found

    Adaptive techniques for signal enhancement in the human electroencephalogram

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    This thesis describes an investigation of adaptive noise cancelling applied to human brain evoked potentials (EPs), with particular emphasis on visually evoked responses. The chief morphological features and signal properties of EPs are described. Consideration is given to the amplitude and spectral properties of the underlying spontaneous electroencephalogram and the importance of noise reduction techniques in EP studies is empnasised. A number of methods of enhancing EP waveforms are reviewed in the light of the known limitations of coherent signal averaging. These are shown to oe generally inadequate for enhancing individual EP responses. The theory of adaptive filters is reviewed with particular reference to adaptive transversal filters usiny the Widrow-Hoff algorithm. The theory of adaptive noise cancelling using correlated reference sources is presented, and new work is described which relates canceller performance to the magnitude-squared coherence function of the input signals. A novel filter structure, the gated adaptive filter, is presented and shown to yield improved cancellation without signal distortion when applied to repetitive transient signals in stationary noise under the condition of fast adaption. The signal processing software available is shown to be inadequate, and a comprehensive Fortran program developed for use on a PDP-11 computer is described. The properties of human visual evoked potentials and the EEO are investigated in two normal adults using a montage of 7 occipital electrodes. Signal enhancement of EPs is shown to be possible oy adaptive noise cancelling, and improvements in signal to noise in the range 2-10 dB are predicted. A discussion of filter strategies is presented, and a detailed investiyation of adaptive noise cancel liny performed usiny a ranye of typical EP data. Assessment of the results confirms the proposal that substantial improvement in sinyle EP response recoynition is achieved by this technique

    Enhancement of Evoked Potential Waveform using Delay-compensated Wiener Filtering

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    In this paper, the evoked potential(EP) was represented by additive delay model to comply with the variational noisy response of stimulus-event synchronization. The hybrid method of delay compensated-Wiener filtered-ensemble averaging(DWEA) was proposed to enhance the EP signal distortion occurred during averaging procedure due to synchronization timing mismatch. The performance of DWEA has been tested by surrogated simulation, which is composed of synthesized arbitrary delay and arbitrary level of added noise. The performance of DWEA is better than those of Wiener filtered-ensemble averaging and of conventional ensemble averaging. DWEA is endurable up to added noise gain of 7 for 10 % mean square error limit. Throughout the experimentation observation, it has been demonstrated that DWEA can be applied to enhance the evoked potential having the synchronization mismatch with added noise.ope

    Neural Mechanisms of Sensory Integration: Frequency Domain Analysis of Spike and Field Potential Activity During Arm Position Maintenance with and Without Visual Feedback

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    abstract: Understanding where our bodies are in space is imperative for motor control, particularly for actions such as goal-directed reaching. Multisensory integration is crucial for reducing uncertainty in arm position estimates. This dissertation examines time and frequency-domain correlates of visual-proprioceptive integration during an arm-position maintenance task. Neural recordings were obtained from two different cortical areas as non-human primates performed a center-out reaching task in a virtual reality environment. Following a reach, animals maintained the end-point position of their arm under unimodal (proprioception only) and bimodal (proprioception and vision) conditions. In both areas, time domain and multi-taper spectral analysis methods were used to quantify changes in the spiking, local field potential (LFP), and spike-field coherence during arm-position maintenance. In both areas, individual neurons were classified based on the spectrum of their spiking patterns. A large proportion of cells in the SPL that exhibited sensory condition-specific oscillatory spiking in the beta (13-30Hz) frequency band. Cells in the IPL typically had a more diverse mix of oscillatory and refractory spiking patterns during the task in response to changing sensory condition. Contrary to the assumptions made in many modelling studies, none of the cells exhibited Poisson-spiking statistics in SPL or IPL. Evoked LFPs in both areas exhibited greater effects of target location than visual condition, though the evoked responses in the preferred reach direction were generally suppressed in the bimodal condition relative to the unimodal condition. Significant effects of target location on evoked responses were observed during the movement period of the task well. In the frequency domain, LFP power in both cortical areas was enhanced in the beta band during the position estimation epoch of the task, indicating that LFP beta oscillations may be important for maintaining the ongoing state. This was particularly evident at the population level, with clear increase in alpha and beta power. Differences in spectral power between conditions also became apparent at the population level, with power during bimodal trials being suppressed relative to unimodal. The spike-field coherence showed confounding results in both the SPL and IPL, with no clear correlation between incidence of beta oscillations and significant beta coherence.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

    Functional Roles of Alpha-Band Phase Synchronization in Local and Large-Scale Cortical Networks

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    Alpha-frequency band (8–14 Hz) oscillations are among the most salient phenomena in human electroencephalography (EEG) recordings and yet their functional roles have remained unclear. Much of research on alpha oscillations in human EEG has focused on peri-stimulus amplitude dynamics, which phenomenologically support an idea of alpha oscillations being negatively correlated with local cortical excitability and having a role in the suppression of task-irrelevant neuronal processing. This kind of an inhibitory role for alpha oscillations is also supported by several functional magnetic resonance imaging and trans-cranial magnetic stimulation studies. Nevertheless, investigations of local and inter-areal alpha phase dynamics suggest that the alpha-frequency band rhythmicity may play a role also in active task-relevant neuronal processing. These data imply that inter-areal alpha phase synchronization could support attentional, executive, and contextual functions. In this review, we outline evidence supporting different views on the roles of alpha oscillations in cortical networks and unresolved issues that should be addressed to resolve or reconcile these apparently contrasting hypotheses

    Dynamic coupling between whisking, barrel cortex, and hippocampus during texture discrimination: A role for slow rhythms

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    Increasing amounts of work have demonstrated that brain rhythms might constitute clocking mechanisms against which to coordinate sequences of neural firing; such rhythms may be essential to the coding operations performed by the local networks. The sequence of operations underlying a tactile discrimination task in rats requires the animal to integrate two streams of information, those coming from the environment and, from reference memory the rules that dictate the correct response. The current study is a follow up on the work which has described the hippocampal representation of the tactile guided task. We have used a well-established texture discrimination task, in which rats have to associate two stimuli with two different reward locations. We placed microelectrodes in primary somatosensory cortex and the CA1 region of hippocampus to perform recordings of spiking activity and local field potentials when the animal touched the discriminandum as well as when he was in a resting state. We also performed recording on an arena in which the animal moved freely and did not perform any task. Earlier work has demonstrated that tactile signals reach the hippocampus during texture discrimination, presumably through the somatosensory cortex. We predicted that neurons in the primary somatosensory cortex (S1) are entrained to the oscillatory theta rhythm that permeates the hippocampus. Our expectation is that such coherence could serve to increase the reliability of synaptic transmission, linking the acquisition of new sensory information with associative processes. We addressed the following issues: Is the timing of action potentials in S1 modulated by the ongoing hippocampal theta rhythm? If so, is the occurrence of this modulation aligned in time to the period in which the hippocampus acquires tactile signals? We also predicted that the 10-Hz whisking that characterizes the acquisition of texture information would be more strongly phase locked to theta rhythm than the whisking in the air that is not accompanied by any explicit tactile task. We speculate that such phase locking could be a means to synchronize sensory and hippocampal processing. The notion that the coordination between brain areas might be related to the rhythmic of sensorimotor cycles is particularly appealing. We have found that the firing of 18% of barrel cells was significantly modulated by hippocampal theta during the half-second period of active tactile discrimination. Importantly, we found that during periods of rest interleaved in the session, neurons significantly decreased the degree of phase-locking with respect to touch. We hypothesize that areas involved with motivational processes as basal ganglia could gate the entrainment during task related epochs. S1 neurons were classified as those excited by contact with the discriminandum, and those not excited by contact. The firing of both sorts of neurons was modulated by CA1 theta rhythm during exploration of the texture. However the theta phase to which they fired preferentially was opposite; contact-responsive neurons tended to fire in the upward phases of the cycle whereas contact non-responsive neurons tended to fire in the downward phase of the cycle suggesting that theta rhythm might have the function of temporally separating sensory cortical neurons according to their functional properties and the information they carry. By clustering touch-sensitive neurons to a certain time window and separating them from \u2018non-informative\u2019 neurons, theta rhythm could increase the efficiency not only of information tranfer to hippocampus but also the efficiency of information encoding/decoding. We also found phase and amplitude relationships between whisking and hippocampal theta during the goal-directed tactile task; the relationships disappear when the animal moves along an open arena, still actively whisking but not engaged in the texture discrimination task. We were able to show, for the first time to our knowledge, that CA1 theta rhythm can exert a behavioral state-dependent modulatory effect on sensory cortex. S1 neuron firing and whisking activity are entrained to hippocampal theta rhythm when the animal collects meaningful tactile information from the environment

    Independent component analysis and source analysis of auditory evoked potentials for assessment of cochlear implant users

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    Source analysis of the Auditory Evoked Potential (AEP) has been used before to evaluate the maturation of the auditory system in both adult and children; in the same way, this technique could be applied to ongoing EEG recordings, in response to acoustic specific frequency stimuli, from children with cochlear implants (CI). This is done in oder to objectively assess the performance of this electronic device and the maturation of the child?s hearing. However, these recordings are contaminated by an artifact produced by the normal operation of the CI; this artifact in particular makes the detection and analysis of AEPs much harder and generates errors in the source analysis process. The artifact can be spatially filtered using Independent Component Analysis (ICA); in this research, three different ICA algorithms were compared in order to establish the more suited algorithm to remove the CI artifact. Additionally, we show that pre-processing the EEG recording, using a temporal ICA algorithm, facilitates not only the identification of the AEP peaks but also the source analysis procedure. From results obtained in this research and limited dataset of CI vs normal recordings, it is possible to conclude that the AEPs source locations change from the inferior temporal areas in the first 2 years after implantation to the superior temporal area after three years using the CIs, close to the locations obtained in normal hearing children. It is intended that the results of this research are used as an objective technique for a general evaluation of the performance of children with CIs

    A Subspace Method for Dynamical Estimation of Evoked Potentials

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    It is a challenge in evoked potential (EP) analysis to incorporate prior physiological knowledge for estimation. In this paper, we address the problem of single-channel trial-to-trial EP characteristics estimation. Prior information about phase-locked properties of the EPs is assesed by means of estimated signal subspace and eigenvalue decomposition. Then for those situations that dynamic fluctuations from stimulus-to-stimulus could be expected, prior information can be exploited by means of state-space modeling and recursive Bayesian mean square estimation methods (Kalman filtering and smoothing). We demonstrate that a few dominant eigenvectors of the data correlation matrix are able to model trend-like changes of some component of the EPs, and that Kalman smoother algorithm is to be preferred in terms of better tracking capabilities and mean square error reduction. We also demonstrate the effect of strong artifacts, particularly eye blinks, on the quality of the signal subspace and EP estimates by means of independent component analysis applied as a prepossessing step on the multichannel measurements

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

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    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

    Comparison of beamformer implementations for MEG source localization

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    Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3-15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.Peer reviewe
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