Showing papers in "International Journal of Neural Systems in 2013"
TL;DR: Five types of beats are automatically classified using HOS features (higher order cumulants) using two different approaches to detect cardiac abnormalities in ECG recordings and the developed system is ready clinically to run on large datasets.
Abstract: Electrocardiogram (ECG) is the electrical activity of the heart indicated by P, Q-R-S and T wave. The minute changes in the amplitude and duration of ECG depicts a particular type of cardiac abnormality. It is very difficult to decipher the hidden information present in this nonlinear and nonstationary signal. An automatic diagnostic system that characterizes cardiac activities in ECG signals would provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect cardiac abnormalities in ECG recordings. Application of higher order spectra (HOS) features is a seemingly promising approach because it can capture the nonlinear and dynamic nature of the ECG signals. In this paper, we have automatically classified five types of beats using HOS features (higher order cumulants) using two different approaches. The five types of ECG beats are normal (N), right bundle branch block (RBBB), left bundle branch block (LBBB), atrial premature contraction (APC) and ventricular premature contraction (VPC). In the first approach, cumulant features of segmented ECG signal were used for classification; whereas in the second approach cumulants of discrete wavelet transform (DWT) coefficients were used as features for classifiers. In both approaches, the cumulant features were subjected to data reduction using principal component analysis (PCA) and classified using three layer feed-forward neural network (NN) and least square — support vector machine (LS-SVM) classifiers. In this study, we obtained the highest average accuracy of 94.52%, sensitivity of 98.61% and specificity of 98.41% using first approach with NN classifier. The developed system is ready clinically to run on large datasets.
157 citations
TL;DR: This work proposes a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform, Higher Order Spectra and textures, and observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results.
Abstract: Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.
118 citations
TL;DR: This work automatically classified normal, interictal and ictal EEG signals using the features derived from the ITD representation, and the developed system is clinically ready for mass screening in resource constrained and emerging economy scenarios.
Abstract: Intrinsic time-scale decomposition (ITD) is a new nonlinear method of time-frequency representation which can decipher the minute changes in the nonlinear EEG signals. In this work, we have automatically classified normal, interictal and ictal EEG signals using the features derived from the ITD representation. The energy, fractal dimension and sample entropy features computed on ITD representation coupled with decision tree classifier has yielded an average classification accuracy of 95.67%, sensitivity and specificity of 99% and 99.5%, respectively using 10-fold cross validation scheme. With application of the nonlinear ITD representation, along with conceptual advancement and improvement of the accuracy, the developed system is clinically ready for mass screening in resource constrained and emerging economy scenarios.
104 citations
TL;DR: An electroencephalogram (EEG)-based wheelchair which can be steered by users' own thoughts without any other involvements implies that people can steer wheelchair only by their thoughts, and may have a potential perspective in daily application for disabled people.
Abstract: Integration of brain–computer interface (BCI) technique and assistive device is one of chief and promising applications of BCI system. With BCI technique, people with disabilities do not have to communicate with external environment through traditional and natural pathways like peripheral nerves and muscles, and could achieve it only by their brain activities. In this paper, we designed an electroencephalogram (EEG)-based wheelchair which can be steered by users' own thoughts without any other involvements. We evaluated the feasibility of BCI-based wheelchair in terms of accuracies and real-world testing. The results demonstrate that our BCI wheelchair is of good performance not only in accuracy, but also in practical running testing in a real environment. This fact implies that people can steer wheelchair only by their thoughts, and may have a potential perspective in daily application for disabled people.
102 citations
TL;DR: Bilateral MTL DBS may herald superior efficacy in unilateral MTL epilepsy, and a switch to bilateral DBS was proposed when unilateral DBS failed to decrease seizures by > 90%, long-term results from patients with medial temporal lobe (MTL) epilepsy treated with deep brain stimulation are presented.
Abstract: In this study, we present long-term results from patients with medial temporal lobe (MTL) epilepsy treated with deep brain stimulation (DBS). Since 2001, 11 patients (8M) with refractory MTL epilepsy underwent MTL DBS. When unilateral DBS failed to decrease seizures by > 90%, a switch to bilateral MTL DBS was proposed. After a mean follow-up of 8.5 years (range: 67-120 months), 6/11 patients had a ≥ 90% seizure frequency reduction with 3/6 seizure-free for > 3 years; three patients had a 40%-70% reduction and two had a < 30% reduction. In 3/5 patients switching to bilateral DBS further improved outcome. Uni- or bilateral MTL DBS did not affect neuropsychological functioning. This open study with an extended long-term follow-up demonstrates maintained efficacy of DBS for MTL epilepsy. In more than half of the patients, a seizure frequency reduction of at least 90% was reached. Bilateral MTL DBS may herald superior efficacy in unilateral MTL epilepsy.
86 citations
TL;DR: The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.
Abstract: Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi–Sugeno–Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.
80 citations
TL;DR: This work showed, for the 1st time, the use of a single-switch BCI based on passive and imagined movements for communication in auditory scanning mode.
Abstract: We investigate whether an electroencephalography technique could be used for yes/no communication with auditory scanning. To be usable by the target group, i.e. minimally conscious individuals, such a brain-computer interface (BCI) has to be very simple and robust. This leads to the concept of a single-switch BCI (ssBCI). With an ssBCI it is possible to reliably detect one certain, individually trained, brain pattern of the individual, and use it to control all kinds of applications using yes/no responses. A total of 10 healthy volunteers (20–27 years) participated in an initial cue-based session with a motor imagery (MI) task after brisk passive feet/hand movement. Four of them reached MI classification accuracies above 70% and, thus, fulfilled the inclusion criterion for participation in the 2nd session. In the 2nd session, MI was used to communicate yes/no answers to a series of questions in an auditory scanning mode. Two of the three participants of the 2nd session were able to reliably communicate their intent with 90% or above correct and 0% false responses. This work showed, for the 1st time, the use of a ssBCI based on passive and imagined movements for communication in auditory scanning mode.
72 citations
TL;DR: An approach to select the multi-domain feature of an ERP among all extracted features is proposed and determination of numbers of extracted components in NCPD and NTD regarding the ERP context is discussed.
Abstract: Non-negative Canonical Polyadic decomposition (NCPD) and non-negative Tucker decomposition (NTD) were compared for extracting the multi-domain feature of visual mismatch negativity (vMMN), a small event-related potential (ERP), for the cognitive research. Since signal-to-noise ratio in vMMN is low, NTD outperformed NCPD. Moreover, we proposed an approach to select the multi-domain feature of an ERP among all extracted features and discussed determination of numbers of extracted components in NCPD and NTD regarding the ERP context.
70 citations
TL;DR: Transcranial magnetic stimulation was used to study the effect of recurrent seizures on cortical excitability over time in epilepsy and found the refractory group was associated with a broad increase in cortex excitability.
Abstract: Transcranial magnetic stimulation was used to study the effect of recurrent seizures on cortical excitability over time in epilepsy. 77 patients with firm diagnoses of idiopathic generalized epilepsy (IGE) or focal epilepsy were repeatedly evaluated over three years. At onset, all groups had increased cortical excitability. At the end of follow-up the refractory group was associated with a broad increase in cortical excitability. Conversely, cortical excitability decreased in all seizure free groups after introduction of an effective medication.
68 citations
TL;DR: TMS can modulate EDs in patients with epileptogenic foci in the cortical convexity and is associated with reversal of ED-induced changes in brain connectivity.
Abstract: Background: TMS is being increasingly used as a noninvasive brain stimulation technique for the therapeutic management of partial epilepsies. However, the acute effects of TMS on epileptiform discharges (EDs, i.e. interictal epileptiform activity and subclinical electrographic seizure patterns) remain unexplored. Objective: To investigate whether TMS can modulate EDs in partial epilepsy. Methods: In Experiment Set 1, the safety of the TMS protocol was investigated in 10 well-controlled by anti-epileptic drugs (AEDs) epileptic patients. In Experiment Set 2, the effects of TMS on EDs were studied in three subjects with intractable frontal lobe epilepsies, characterized by particularly frequent EDs. TMS was applied over the electrographic focus with a circular and a figure of eight coil while recording EEG with a 60-channel TMS-compatible EEG system. The effectiveness of TMS in aborting EDs was investigated using survival analysis and brain connectivity analysis. Results: The TMS protocol was well-tolerated....
67 citations
TL;DR: An electroencephalogram (EEG) analysis system is proposed for single-trial classification of motor imagery (MI) data and the results indicate that the proposed method achieves 86.7% average classification accuracy, which is promising in BCI applications.
Abstract: An electroencephalogram (EEG) analysis system is proposed for single-trial classification of motor imagery (MI) data in this study. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system mainly consists of enhanced active segment selection, feature extraction, feature selection and classification. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. We then extract several features, including spectral power and asymmetry ratio, coherence and phase-locking value, and multiresolution fractal feature vector, for subsequent classification. Next, genetic algorithm (GA) is used to select features from the combination of above-mentioned features. Finally, support vector machine (SVM) is used for classification. Compared with "without enhanced active segment selection," several potential features and linear discriminant analysis (LDA) on MI data from two data sets for 10 subjects, the results indicate that the proposed method achieves 86.7% average classification accuracy, which is promising in BCI applications.
TL;DR: The robustness of the system to long duration "seizure-like" artifacts, in particular those due to respiration, is improved and the number of false detections per hour is decreased, while maintaining the correct detection of seizure burden at 70%.
Abstract: Adaptive probabilistic modeling of the EEG background is proposed for seizure detection in neonates with hypoxic ischemic encephalopathy. The decision is made based on the temporal derivative of the seizure probability with respect to the adaptively modeled level of background activity. The robustness of the system to long duration "seizure-like" artifacts, in particular those due to respiration, is improved. The system was developed using statistical leave-one-patient-out performance assessment, on a large clinical dataset, comprising 38 patients of 1479 h total duration. The developed technique was then validated by a single test on a separate totally unseen randomized prospective dataset of 51 neonates totaling 2540 h of duration. By exploiting the proposed adaptation, the ROC area is increased from 93.4% to 96.1% (41% relative improvement). The number of false detections per hour is decreased from 0.42 to 0.24, while maintaining the correct detection of seizure burden at 70%. These results on the unseen data were predicted from the rigorous leave-one-patient-out validation and confirm the validity of our algorithm development process.
TL;DR: The results suggest that reduced subsets of complementary features and classifiers with high generalization ability could provide high-performance screening tools in the context of sleep apnea hypopnea syndrome (SAHS).
Abstract: This study is aimed at assessing the usefulness of different feature selection and classification methodologies in the context of sleep apnea hypopnea syndrome (SAHS) detection. Feature extraction, selection and classification stages were applied to analyze blood oxygen saturation (SaO2) recordings in order to simplify polysomnography (PSG), the gold standard diagnostic methodology for SAHS. Statistical, spectral and nonlinear measures were computed to compose the initial feature set. Principal component analysis (PCA), forward stepwise feature selection (FSFS) and genetic algorithms (GAs) were applied to select feature subsets. Fisher's linear discriminant (FLD), logistic regression (LR) and support vector machines (SVMs) were applied in the classification stage. Optimum classification algorithms from each combination of these feature selection and classification approaches were prospectively validated on datasets from two independent sleep units. FSFS + LR achieved the highest diagnostic performance using a small feature subset (4 features), reaching 83.2% accuracy in the validation set and 88.7% accuracy in the test set. Similarly, GAs + SVM also achieved high generalization capability using a small number of input features (7 features), with 84.2% accuracy on the validation set and 84.5% accuracy in the test set. Our results suggest that reduced subsets of complementary features (25% to 50% of total features) and classifiers with high generalization ability could provide high-performance screening tools in the context of SAHS.
TL;DR: Two main approaches are proposed: the combination of features extracted from electroencephalogram (EEG) signal by different extraction methods, and the use of stacked sequential learning to incorporate predicted information from nearby sleep stages in the final classifier.
Abstract: This work proposes a methodology for sleep stage classification based on two main approaches: the combination of features extracted from electroencephalogram (EEG) signal by different extraction methods, and the use of stacked sequential learning to incorporate predicted information from nearby sleep stages in the final classifier. The feature extraction methods used in this work include three representative ways of extracting information from EEG signals: Hjorth features, wavelet transformation and symbolic representation. Feature selection was then used to evaluate the relevance of individual features from this set of methods. Stacked sequential learning uses a second-layer classifier to improve the classification by using previous and posterior first-layer predicted stages as additional features providing information to the model. Results show that both approaches enhance the sleep stage classification accuracy rate, thus leading to a closer approximation to the experts' opinion.
TL;DR: This work presents an efficient wrapper-based methodology for feature selection and least squares discrimination of high-dimensional EEG data with low computational complexity and shows that this approach gives better results than current state-of-the-art approaches in terms of recognition rates and computational requirements.
Abstract: Discriminative features have to be properly extracted and selected from the electroencephalographic (EEG) signals of each specific subject in order to achieve an adaptive brain–computer interface (BCI) system. This work presents an efficient wrapper-based methodology for feature selection and least squares discrimination of high-dimensional EEG data with low computational complexity. Features are computed in different time segments using three widely used methods for motor imagery tasks and, then, they are concatenated or averaged in order to take into account the time course variability of the EEG signals. Once EEG features have been extracted, proposed framework comprises two stages. The first stage entails feature ranking and, in this work, two different procedures have been considered, the least angle regression (LARS) and the Wilcoxon rank sum test, to compare the performance of each one. The second stage selects the most relevant features using an efficient leave-one-out (LOO) estimation based on the Allen's PRESS statistic. Experimental comparisons with the state-of-the-art BCI methods shows that this approach gives better results than current state-of-the-art approaches in terms of recognition rates and computational requirements and, also with respect to the first ranking stage, it is confirmed that the LARS algorithm provides better results than the Wilcoxon rank sum test for these experiments.
TL;DR: Two nonlinear features derived from fractal geometry for epileptic EEG analysis are introduced and it is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs.
Abstract: The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.
TL;DR: An adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models, which approximates the unknown system based on input-output data from it and can be successfully applied to model the two nonlinear systems.
Abstract: This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input–output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.
TL;DR: Compared with without artifact elimination, feature selection using a genetic algorithm (GA), feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.
Abstract: In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.
TL;DR: A framework where in phase synchronization between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks is proposed.
Abstract: It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.
TL;DR: A recurrent adaptive control architecture in which an adaptive feedback controller guarantees a precise, compliant, and stable control during the manipulation of objects is presented in which a bio-inspired module (cerebellar circuitry) with a machine learning component (LWPR).
Abstract: In this work, a basic cerebellar neural layer and a machine learning engine are embedded in a recurrent loop which avoids dealing with the motor error or distal error problem. The presented approach learns the motor control based on available sensor error estimates (position, velocity, and acceleration) without explicitly knowing the motor errors. The paper focuses on how to decompose the input into different components in order to facilitate the learning process using an automatic incremental learning model (locally weighted projection regression (LWPR) algorithm). LWPR incrementally learns the forward model of the robot arm and provides the cerebellar module with optimal pre-processed signals. We present a recurrent adaptive control architecture in which an adaptive feedback (AF) controller guarantees a precise, compliant, and stable control during the manipulation of objects. Therefore, this approach efficiently integrates a bio-inspired module (cerebellar circuitry) with a machine learning component (LWPR). The cerebellar-LWPR synergy makes the robot adaptable to changing conditions. We evaluate how this scheme scales for robot-arms of a high number of degrees of freedom (DOFs) using a simulated model of a robot arm of the new generation of light weight robots (LWRs).
TL;DR: The method can be used to record VNS-induced electrophysiological responses in rats and provides an objective biomarker for electrical stimulation with various parameters in an experimental set-up.
Abstract: The mechanism of action of vagus nerve stimulation (VNS) for pharmacoresistant epilepsy is unknown and the therapeutic outcome is highly variable. We investigated stimulation-induced vagus nerve electrophysiological responses in rats using various stimulation parameters. Conduction velocity, I(50), rheobase and chronaxie were calculated. We identified an early and late component corresponding to an afferent compound action potential (CAP) and a remote laryngeal motor-evoked potential (LMEP), respectively. The conduction velocity (CAP: 26.2 ± 1.4 m/s; LMEP: 32.4 ± 2.4 m/s) and I(50) (CAP: 2.4 ± 0.3 mA; LMEP: 1.8±0.2 mA) were significantly different for both components, the rheobase (CAP: 140±30 μA; LMEP: 110±26 μA) and chronaxie (CAP: 66±7 μs; LMEP: 73±9 μs) were not. Using a pulse of 10 μs, the CAP saturated between 4-5 mA. Our method can be used to record VNS-induced electrophysiological responses in rats and provides an objective biomarker for electrical stimulation with various parameters in an experimental set-up. Our findings are potentially useful for clinical purposes in the sense that combination of VNS and recording of vagal nerve CAPs may help clinicians to determine the individual optimal intensity required to fully activate fast-conducting afferent fibers.
TL;DR: Qualitative and quantitative analysis demonstrates the performance of feedback controller based on slow variable is more efficient compared with traditional feedback control based on fast variable, pointing to the potential value of model-based design of feedback controllers for Parkinson's disease.
Abstract: A novel closed-loop control strategy is proposed to control Parkinsonian state based on a computational model. By modeling thalamocortical relay neurons under external electric field, a slow variable feedback control is applied to restore its relay functionality. Qualitative and quantitative analysis demonstrates the performance of feedback controller based on slow variable is more efficient compared with traditional feedback control based on fast variable. These findings point to the potential value of model-based design of feedback controllers for Parkinson's disease.
TL;DR: Common noninvasive techniques are reviewed, then the value of multimodal analysis to localize seizure onset for targeted treatment is illustrated.
Abstract: Approximately 30% of epilepsy patients are medically intractable. Epilepsy surgery may offer cure or palliation, and neuromodulation and direct drug delivery are being developed as alternatives. Successful treatment requires correct localization of seizure onset zones and understanding surrounding functional cortex to avoid iatrogenic disability. Several neurophysiologic and imaging localization techniques have inherent individual weaknesses which can be overcome by multimodal analysis. We review common noninvasive techniques, then illustrate the value of multimodal analysis to localize seizure onset for targeted treatment.
TL;DR: A novel gray-box neural network model, including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme that directly inherits system dynamics and separately models system nonlinearities.
Abstract: A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.
TL;DR: To explore the influence of chunking on the capacity limits of working memory, a model for chunking in sequential working memory is proposed, using hierarchical bidirectional inhibition-connected neural networks with winnerless competition.
Abstract: To explore the influence of chunking on the capacity limits of working memory, a model for chunking in sequential working memory is proposed, using hierarchical bidirectional inhibition-connected neural networks with winnerless competition. With the assumption of the existence of an upper bound to the inhibitory weights in neurobiological networks, it is shown that chunking increases the number of memorized items in working memory from the "magical number 7" to 16 items. The optimal number of chunks and the number of the memorized items in each chunk are the "magical number 4".
TL;DR: The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions, therefore, the proposed scheme is not vulnerable to initial design assumptions.
Abstract: In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not vulnerable to initial design assumptions. Simulations on selected examples of well-known benchmarks illustrate the potency of the method.
TL;DR: A comprehensive models for the control of horizontal saccades is presented using a new muscle fiber model for the lateral and medial rectus muscles that is robust and accounts for the neural activity for both large and small saccade activity.
Abstract: A comprehensive model for the control of horizontal saccades is presented using a new muscle fiber model for the lateral and medial rectus muscles. The importance of this model is that each muscle fiber has a separate neural input. This model is robust and accounts for the neural activity for both large and small saccades. The muscle fiber model consists of serial sequences of muscle fibers in parallel with other serial sequences of muscle fibers. Each muscle fiber is described by a parallel combination of a linear length tension element, viscous element and active state tension generator.
TL;DR: The results validate the use of background NLA in the neurodynamical study of epileptiform transitions and suggest that what is considered "neuronal noise" is amenable to synchronization effects in the spatiotemporal domain.
Abstract: Organized brain activity is the result of dynamical, segregated neuronal signals that may be used to investigate synchronization effects using sophisticated neuroengineering techniques. Phase synchrony analysis, in particular, has emerged as a promising methodology to study transient and frequency-specific coupling effects across multi-site signals. In this study, we investigated phase synchronization in intracellular recordings of interictal and ictal epileptiform events recorded from pairs of cells in the whole (intact) mouse hippocampus. In particular, we focused our analysis on the background noise-like activity (NLA), previously reported to exhibit complex neurodynamical properties. Our results show evidence for increased linear and nonlinear phase coupling in NLA across three frequency bands [theta (4-10 Hz), beta (12-30 Hz) and gamma (30-80 Hz)] in the ictal compared to interictal state dynamics. We also present qualitative and statistical evidence for increased phase synchronization in the theta, beta and gamma frequency bands from paired recordings of ictal NLA. Overall, our results validate the use of background NLA in the neurodynamical study of epileptiform transitions and suggest that what is considered "neuronal noise" is amenable to synchronization effects in the spatiotemporal domain.
TL;DR: The study indicates which parameters provide the maximal seizure reduction with minimal intervention and an adaptive scheme is proposed that optimizes the stimulation parameters in nonstationary situations.
Abstract: We aim to derive fully autonomous seizure suppression paradigms based on reactive control of neuronal dynamics. A previously derived computational model of seizure generation describing collective degrees of freedom and featuring bistable dynamics is used. A novel technique for real-time control of epileptogenicity is introduced. The reactive control reduces practically all seizures in the model. The study indicates which parameters provide the maximal seizure reduction with minimal intervention. An adaptive scheme is proposed that optimizes the stimulation parameters in nonstationary situations.
TL;DR: A neural mass model for the memorization of sequences is presented, which exploits three layers of cortical columns that generate a theta/gamma rhythm and recovers sequences and accounts for the phase-precession phenomenon.
Abstract: A neural mass model for the memorization of sequences is presented. It exploits three layers of cortical columns that generate a theta/gamma rhythm. The first layer implements an auto-associative memory working in the theta range; the second segments objects in the gamma range; finally, the feedback interactions between the third and the second layers realize a hetero-associative memory for learning a sequence. After training with Hebbian and anti-Hebbian rules, the network recovers sequences and accounts for the phase-precession phenomenon.