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Biomedical subjects

Jose C Principe

Publications and source records attributed to Jose C Principe.

18 recordsLinked to original sources
PubMed2006

Evolving into epilepsy: Multiscale electrophysiological analysis and imaging in an animal model.

Epilepsy research for the design of seizure detection/prediction neuroprosthetics has been faced with the search for electrophysiologic control parameters that can be used to infer the epileptic state of the animal and be leveraged at a later time to deliver neurotherapeutic feedback. The analysis presented here uses multi-microelectrode array technology to provide an electrophysiologic quantification of a hippocampal neural ensemble during the latent period of epileptogenesis. Through the use of signal processing system identification methodologies, we were able to assess the spatial and temporal interrelations of ensembles of hippocampal neurons and relate them to the evolution of the epileptic condition. High-field magnetic resonance (MR) imaging was used to determine the location of electrode placement and to evaluate hippocampal pyramidal cell structural damage. Long-term single unit activity analysis suggests that hippocampal neurons in both CA1-2 and dentate regions increase the number of occurrences and duration of their bursting activity after injury to the contra-lateral hippocampus. The trends inferred from both single neuron and ensemble analysis suggests that the evolution into epilepsy is not abrupt but modulates gradually from the time of injury.

Action Potentials↗
PubMed2006

Predictability analysis for an automated seizure prediction algorithm.

Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable in the intracranial EEG. A series of computer algorithms designed to detect the changes in spatiotemporal dynamics of the EEG signals and to warn of impending seizures have been developed. In this study, we evaluated the performance of a novel adaptive threshold seizure warning algorithm (ATSWA), which detects the convergence in Short-Term Maximum Lyapunov Exponent (STLmax) values among critical intracranial EEG electrode sites, as a function of different seizure warning horizons (SWHs). The ATSWA algorithm was compared to two statistical based naïve prediction algorithms (periodic and random) that do not employ EEG information. For comparison purposes, three performance indices "area above ROC curve" (AAC), "predictability power" (PP) and "fraction of time under false warnings" (FTF) were defined and the effect of SWHs on these indices was evaluated. The results demonstrate that this EEG based seizure warning method performed significantly better (P < 0.05) than both naïve prediction schemes. Our results also show that the performance indexes are dependent on the length of the SWH. These results suggest that the EEG based analysis has the potential to be a useful tool for seizure warning.

Adult↗
PubMed2006

Analysis of biomedical signals by the lempel-Ziv complexity: the effect of finite data size.

The Lempel-Ziv (LZ) complexity and its variants are popular metrics for characterizing biological signals. Proper interpretation of such analyses, however, has not been thoroughly addressed. In this letter, we study the the effect of finite data size. We derive analytic expressions for the LZ complexity for regular and random sequences, and employ them to develop a normalization scheme. To gain further understanding, we compare the LZ complexity with the correlation entropy from chaos theory in the context of epileptic seizure detection from EEG data, and discuss advantages of the normalized LZ complexity over the correlation entropy.

Algorithms↗
PubMed2006

Modeling and inverse controller design for an unmanned aerial vehicle based on the self-organizing map.

The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a self-organizing map (SOM)-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV.

Aircraft↗
PubMed2006

Feature extraction using information-theoretic learning.

A classification system typically consists of both a feature extractor (preprocessor) and a classifier. These two components can be trained either independently or simultaneously. The former option has an implementation advantage since the extractor need only be trained once for use with any classifier, whereas the latter has an advantage since it can be used to minimize classification error directly. Certain criteria, such as Minimum Classification Error, are better suited for simultaneous training, whereas other criteria, such as Mutual Information, are amenable for training the feature extractor either independently or simultaneously. Herein, an information-theoretic criterion is introduced and is evaluated for training the extractor independently of the classifier. The proposed method uses nonparametric estimation of Renyi's entropy to train the extractor by maximizing an approximation of the mutual information between the class labels and the output of the feature extractor. The evaluations show that the proposed method, even though it uses independent training, performs at least as well as three feature extraction methods that train the extractor and classifier simultaneously.

Algorithms↗
PubMed2005

Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface.

Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood. Here, we show that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI. Initially, monkeys controlled the actuator by moving a hand-held pole. During this period, the BMI was trained to predict the actuator velocity. As the monkeys started using their cortical activity to control the actuator, the activity of individual neurons and neuronal populations became less representative of the animal's hand movements while representing the movements of the actuator. As a result of this adaptation, the animals could eventually stop moving their hands yet continue to control the actuator. These results show that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animal's own limb.

Adaptation, Physiological↗
PubMed2005

Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface.

We propose the use of optimized brain-machine interface (BMI) models for interpreting the spatial and temporal neural activity generated in motor tasks. In this study, a nonlinear dynamical neural network is trained to predict the hand position of primates from neural recordings in a reaching task paradigm. We first develop a method to reveal the role attributed by the model to the sampled motor, premotor, and parietal cortices in generating hand movements. Next, using the trained model weights, we derive a temporal sensitivity measure to asses how the model utilized the sampled cortices and neurons in real-time during BMI testing.

Algorithms↗
PubMed2004

Image construction methods for phased array magnetic resonance imaging.

PURPOSE: To study image construction in phased array magnetic resonance imaging (MRI) systems from a statistical signal processing point of view. MATERIALS AND METHODS: Three new approaches for image combination with multiple coils are proposed: 1) one based on the singular value decomposition of the measurement matrix, which is asymptotically optimal in the signal-to-noise ratio sense; 2) one based on a maximum-likelihood formulation, incorporating a priori information on the coil sensitivities in a Bayesian manner; and 3) one based on a least-squares formulation, which incorporates a smoothness constraint on the coil sensitivities. RESULTS: Numerical examples using synthetic and real data are presented to illustrate the performance of these new approaches. Results on the synthetic data show improvement in signal-to-error ratio, while results on the real data (a 4.7 T four-coil image of a cat spinal cord) show that the proposed methods can improve the SNR in the final image by up to 3 dB in the regions of interest compared to conventional sum-of-squares processing. CONCLUSION: It is demonstrated that phased array MRI reconstruction performance can be improved by the use of more elaborate statistical signal processing algorithms.

Algorithms↗
PubMed2004

Ascertaining the importance of neurons to develop better brain-machine interfaces.

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.

Action Potentials↗
PubMed2004

Advanced search algorithms for information-theoretic learning with kernel-based estimators.

Recent publications have proposed various information-theoretic learning (ITL) criteria based on Renyi's quadratic entropy with nonparametric kernel-based density estimation as alternative performance metrics for both supervised and unsupervised adaptive system training. These metrics, based on entropy and mutual information, take into account higher order statistics unlike the mean-square error (MSE) criterion. The drawback of these information-based metrics is the increased computational complexity, which underscores the importance of efficient training algorithms. In this paper, we examine familiar advanced-parameter search algorithms and propose modifications to allow training of systems with these ITL criteria. The well known algorithms tailored here for ITL include various improved gradient-descent methods, conjugate gradient approaches, and the Levenberg-Marquardt (LM) algorithm. Sample problems and metrics are presented to illustrate the computational efficiency attained by employing the proposed algorithms.

Algorithms↗
PubMed2004

Feature selection in MLPs and SVMs based on maximum output information.

This paper presents feature selection algorithms for multilayer perceptrons (MLPs) and multiclass support vector machines (SVMs), using mutual information between class labels and classifier outputs, as an objective function. This objective function involves inexpensive computation of information measures only on discrete variables; provides immunity to prior class probabilities; and brackets the probability of error of the classifier. The maximum output information (MOI) algorithms employ this function for feature subset selection by greedy elimination and directed search. The output of the MOI algorithms is a feature subset of user-defined size and an associated trained classifier (MLP/SVM). These algorithms compare favorably with a number of other methods in terms of performance on various artificial and real-world data sets.

Algorithms↗
PubMed2003

SNR-optimality of sum-of-squares reconstruction for phased-array magnetic resonance imaging.

We consider the commonly used "Sum-of-Squares" (SoS) reconstruction method for phased-array magnetic resonance imaging with unknown coil sensitivities. We show that the signal-to-noise ratio (SNR) in the image produced by SoS is asymptotically (as the input SNR--> infinity ) equal to that of maximum-ratio combining, which is the best unbiased reconstruction method when the coil sensitivities are known. Finally, we discuss the implications of this result.

Computer Simulation↗
PubMed2003

Adaptive epileptic seizure prediction system.

Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.

Algorithms↗
PubMed1999

Modeling the precedence effect for speech using the gamma filter.

The ability of the human auditory system to localize the direction of a sound source in a reverberant environment extends from simple clicks to complex speech signals. The perceived location of the auditory event is dominated by the direct sound, for small enough time intervals between the direct sound and reflection-a phenomenon known as the precedence effect. In this paper we present a computer simulation of the precedence effect for speech, implemented using Matlab and tested with speech signals from the TIMIT database. The model is based on the biological assumption that the rate of change of a signal (onset) is critical in triggering the precedence effect. The model demonstrates the precedence effect on speech signals, as opposed to prior models that have only been tested on clicks. In addition, a novel onset enhancement method is described and implemented using the gamma filter, a new class of linear systems for adaptive signal processing. This method can either use fixed coefficients and obtain similar results to prior methods of onset enhancement, or use an adaptive framework. The adaptive framework is promising in its ability to enhance onsets and to reduce the steady state portions of the signal.

Journal Article↗
PubMedDate not supplied

A new classifier based on information theoretic learning with unlabeled data.

Supervised learning is conventionally performed with pairwise input-output labeled data. After the training procedure, the adaptive system's weights are fixed while the testing procedure with unlabeled data is performed. Recently, in an attempt to improve classification performance unlabeled data has been exploited in the machine learning community. In this paper, we present an information theoretic learning (ITL) approach based on density divergence minimization to obtain an extended training algorithm using unlabeled data during the testing. The method uses a boosting-like algorithm with an ITL based cost function. Preliminary simulations suggest that the method has the potential to improve the performance of classifiers in the application phase.

Algorithms↗
PubMedDate not supplied

Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models.

This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network.

Artificial Intelligence↗
PubMedDate not supplied

Stochastic error whitening algorithm for linear filter estimation with noisy data.

Mean squared error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem. However, MSE gives biased results when the input signals are noisy. This paper presents a novel stochastic gradient algorithm based on the recently proposed error whitening criterion (EWC) to tackle the problem of linear filter estimation in the presence of additive white disturbances. We will briefly motivate the theory behind the new criterion and derive an online stochastic gradient algorithm. Convergence proof of the stochastic gradient algorithm is derived making mild assumptions. Further, we will propose some extensions to the stochastic gradient algorithm to ensure faster, step-size independent convergence. We will perform extensive simulations and compare the results with MSE as well as total-least squares in a parameter estimation problem. The stochastic EWC algorithm has many potential applications. We will use this in designing robust inverse controllers with noisy data.

Algorithms↗