Real-Time, Hardware Efficient Ocular Artifact Removal From Single Channel EEG data Using a Hybrid Algebraic and Wavelet Algorithm

Electroencephalography (EEG) is a promising technique to record brain activities in natural settings. EEG signal usually gets contaminated by Ocular Artifacts (OA), removal of which is critical for the feature extraction and classification. With the increasing interest in wearable technologies, single channel EEG systems are becoming more prevalent that often require real-time signal processing for immediate feedback. In this context, a new hybrid algorithm to detect OA and subsequently remove OA from single channel streaming EEG data is proposed here. The algorithm first detects the OA zones using Algebraic approach, and then removes artifact from the detected OA zones using Discrete Wavelet Transform (DWT) decomposition method. De-noising technique is applied only to the OA zone that minimizes interference to neural information outside of OA zone. The microcontroller hardware implemented hybrid OA removal algorithm demonstrated real-time execution with sufficient accuracy in both OA detection and removal. The performance evaluation was carried out qualitatively and quantitatively for 0.5 sec epoch in overlapping manner using time-frequency analysis, mean square coherence, Correlation Coefficient (CC) and Mutual Information statistics. Matlab implementation resulted in average CC of 0.3242 and average MI of 1.0042, while microcontroller implementation resulted in average CC of 0.4033 for all blinks. Successful implementation of OA removal from single channel real-time EEG data using the proposed algorithm shows promise for real-time feedabck applications of wearable EEG devices

Chronic iEEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states

Background and Objectives: Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Methods: Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterised the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states (SNSs). We then compared SNS occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. Results: In most patients, the occurrence or duration of at least one SNS was associated with the time since implantation. Some patients had one or more SNSs that were associated with phases of circadian and/or multidien spike rate cycles. A given SNS's occurrence and duration were usually not associated with the same timescale. Discussion: Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a SNS's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures, and similar principles likely apply to other neurological conditions

Engineering Approaches for Improving Cortical Interfacing and Algorithms for the Evaluation of Treatment Resistant Epilepsy

abstract: Epilepsy is a group of disorders that cause seizures in approximately 2.2 million people in the United States. Over 30% of these patients have epilepsies that do not respond to treatment with anti-epileptic drugs. For this population, focal resection surgery could offer long-term seizure freedom. Surgery candidates undergo a myriad of tests and monitoring to determine where and when seizures occur. The “gold standard” method for focus identification involves the placement of electrocorticography (ECoG) grids in the sub-dural space, followed by continual monitoring and visual inspection of the patient’s cortical activity. This process, however, is highly subjective and uses dated technology. Multiple studies were performed to investigate how the evaluation process could benefit from an algorithmic adjust using current ECoG technology, and how the use of new microECoG technology could further improve the process. Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures. Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids. Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

The importance of uncoupling troponin I phosphorylation from Ca2+ sensitivity in the pathogenesis of cardiomyopathy

Heart muscle contraction is regulated via the β adrenergic response that results to phosphorylation of Protein Kinase A (PKA), which in turn decreases the Ca2+ sensitivity of the cardiac myofilament, which is very important for the heart muscle to relax. Mutations in the thin filament that cause Dilated Cardiomyopathy (DCM) and some that cause Hypertrophic Cardiomyopathy (HCM) abolish this relationship, so that the Ca2+ sensitivity becomes independent of Troponin I (TnI) phosphorylation (uncoupling). The aim of the thesis is to unravel the molecular mechanism of the uncoupling phenomenon. It is known that there is a specific interaction between the phosphorylatable TnI N terminal peptide and the Ca2+ binding site on TnC, that is weakened by phosphorylation and we hypothesize that it is disrupted in case of a DCM or HCM mutation, giving rise to the uncoupling phenomenon. Ca2+ sensitisers and desensitisers change the Ca2+ sensitivity of the cardiac muscle like mutations do but their relationship with TnI phosphorylation has never been studied before. Using the in vitro motility assay I showed that the Ca2+ sensitisers EMD 57033 and Bepridil increased the Ca2+ sensitivity of donor thin filaments and additionally they uncoupled the Ca2+ sensitivity from the TnI phosphorylation. Epigallocatechin-3-gallate (EGCG) decreased the Ca2+ sensitivity of donor thin filaments whilst retaining the coupling. On the other hand, EGCG reduced the Ca2+ sensitivity of phosphorylated but not dephosphorylated mutant thin filaments restoring the Ca2+ sensitivity change to TnI phosphorylation. EGCG re-coupled 5 DCM (TPM1 E54K and E40K, TNNI3 K36Q, TNNC1 G159D, ACTC E361G) mutants and 3 HCM (TPM1 E180G, TNNT2 K280N, ACTC E99K) mutants which were originally uncoupled. We were given 30 analogue compounds structurally similar to EGCG and nine of them were able to re-couple uncoupled TPM1 E180G HCM mutant thin filaments. The working compounds re-coupled DCM mutation TPM1 E54K and HCM mutation ACTC E99K. I show for the first time that it is possible to mimic and reverse the effect of DCM and HCM mutations on troponin pharmacologically. EGCG and its analogue compounds might have significant implications for the effective treatment of thin filament cardiomyopathies that uncouple the Ca2+ sensitivity from TnI phosphorylation. In a separate study I investigated 11 mutations in skeletal muscle tropomyosin associated with various myopathies. I found that 7 mutations cause a gain of function that could be accounted for at the molecular level due to destabilising specific actin-tropomyosin interactions. Gain of function at the molecular level correlates with a hypercontractile phenotype in patients.Open Acces

Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery

Entropy as an estimate of complexity of the electroencephalogram is an effective parameter for monitoring the depth of anesthesia (DOA) during surgery. Multiscale entropy (MSE) is useful to evaluate the complexity of signals over different time scales. However, the limitation of the length of processed signal is a problem due to observing the variation of sample entropy (SE) on different scales. In this study, the adaptive resampling procedure is employed to replace the process of coarse-graining in MSE. According to the analysis of various signals and practical EEG signals, it is feasible to calculate the SE from the adaptive resampled signals, and it has the highly similar results with the original MSE at small scales. The distribution of the MSE of EEG during the whole surgery based on adaptive resampling process is able to show the detailed variation of SE in small scales and complexity of EEG, which could help anesthesiologists evaluate the status of patients.The Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by National Science Council (Grant Number: NSC 100-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science & Technology in Taiwan (Grant Numbers: CSIST-095-V101 and CSIST-095-V102). Furthermore, it was supported by the National Science Foundation of China (No.50935005)

NEW APPROACHES FOR ASSESSING TIME-VARYING FUNCTIONAL BRAIN CONNECTIVITY USING FMRI DATA

It was long assumed that functional connectivity (FC) among brain regions did not vary substantially during a single resting-state functional magnetic resonance imaging (rs-fMRI) run. However, an increasing number of studies have reported on the existence of time-varying functional connectivity (TVC) in rs-fMRI data taking place in a considerably shorter time window than previously thought (i.e., on the order of seconds and minutes). However, the study of TVFC is a relatively new research area and there remain a number of unaddressed problems hindering its ability to fulfill its promise of increasing our knowledge of human brain function. First, while it has previously been shown that autocorrelation can negatively impact estimates of static functional connectivity, its impact on TVC estimates has not been established. Understanding the influence of autocorrelation on TVFC is of high importance, as we hypothesize the autocorrelation within a time series can inflate the sampling variability of TVC estimated using sliding window techniques, leading to the increase of risk of misinterpreting noise as true TVC and negatively impact subsequent estimation of whole-brain time varying functional connectivity. We thus study the impact of autocorrelation on TVC and how to mitigate it. Second, there is a need for new analytic approaches for estimating TVC. Most studies use a sliding window approach, where the correlation between region is computed locally within a specific time window that is moved across time. A shortcoming of this approach is the need to select an a priori window length for analysis. To circumvent this issue, we focus on the use of instantaneous phase synchronization (IPS), which offers single time-point resolution of time-resolved fMRI connectivity. The use of IPS necessitates bandpass filtering the data to obtain valid results. We seek to show how bandpass filtering affects the estimates of IPS metrics such as phase locking value (PLV) and phase coherence. Further, as current metrics discard the temporal transitions from positive to negative associations common in IPS analysis we introduce a new approach within IPS framework for circumventing this issue. Third, the choice of cut-off frequencies when bandpass filtering in IPS analysis is to some extend arbitrary. We seek to compare standard phase synchronization using the Hilbert transform with empirical mode decomposition (EMD) which eliminates the need for bandpass filtering in a data driven manner. While the use of EMD has a number of benefits compared to the Hilbert transform, it has a couple shortcomings: the susceptibility of the EMD to the SNR of the signal and untangling frequencies close to one another. To circumvent this issue and improve the assessment of IPS, we propose the use of an alternative decomposition approach, multivariate variational mode decomposition (MVMD) for phase synchronization analysis.

Integration of Human Walking Gyroscopic Data Using Empirical Mode Decomposition

The present study was aimed at evaluating the Empirical Mode Decomposition (EMD) method to estimate the 3D orientation of the lower trunk during walking using the angular velocity signals generated by a wearable inertial measurement unit (IMU) and notably flawed by drift. The IMU was mounted on the lower trunk (L4-L5) with its active axes aligned with the relevant anatomical axes. The proposed method performs an offline analysis, but has the advantage of not requiring any parameter tuning. The method was validated in two groups of 15 subjects, one during overground walking, with 180° turns, and the other during treadmill walking, both for steady-state and transient speeds, using stereophotogrammetric data. Comparative analysis of the results showed that the IMU/EMD method is able to successfully detrend the integrated angular velocities and estimate lateral bending, flexion-extension as well as axial rotations of the lower trunk during walking with RMS errors of 1 deg for straight walking and lower than 2.5 deg for walking with turns

Serotonergic innervation of the amygdala is increased in autism spectrum disorder and decreased in Williams syndrome.

BackgroundWilliams syndrome (WS) and autism spectrum disorder (ASD) are neurodevelopmental disorders that demonstrate overlapping genetic associations, dichotomous sociobehavioral phenotypes, and dichotomous pathological differences in neuronal distribution in key social brain areas, including the prefrontal cortex and the amygdala. The serotonergic system is critical to many processes underlying neurodevelopment and is additionally an important neuromodulator associated with behavioral variation. The amygdala is heavily innervated by serotonergic projections, suggesting that the serotonergic system is a significant mediator of neuronal activity. Disruptions to the serotonergic system, and atypical structure and function of the amygdala, are implicated in both WS and ASD.MethodsWe quantified the serotonergic axon density in the four major subdivisions of the amygdala in the postmortem brains of individuals diagnosed with ASD and WS and neurotypical (NT) brains.ResultsWe found opposing directions of change in serotonergic innervation in the two disorders, with ASD displaying an increase in serotonergic axons compared to NT and WS displaying a decrease. Significant differences (p < 0.05) were observed between WS and ASD data sets across multiple amygdala nuclei.LimitationsThis study is limited by the availability of human postmortem tissue. Small sample size is an unavoidable limitation of most postmortem human brain research and particularly postmortem research in rare disorders.ConclusionsDifferential alterations to serotonergic innervation of the amygdala may contribute to differences in sociobehavioral phenotype in WS and ASD. These findings will inform future work identifying targets for future therapeutics in these and other disorders characterized by atypical social behavior