14525136
Model-based EEG and MEG Source Reconstruction Methods
Neurophysiological activity, as measured with EEG or MEG, has its origin in the brain. For most EEG and MEG experiments it is known beforehand, that the folded gray matter of the cortex is the location of the neurons whose activity is measured. These neurons, the pyramidal cells, are oriented perpendicular to the cortical surface. Triangulation of the segmented cortical surface yields a triangle net holding all anatomical information which is necessary to guide the reconstruction procedure. We have developed dedicated 3D region-growing, surface subsampling, normal estimation, and surface triangulation algorithms for generating the cortical triangle net. Especially, triangle nets encode neighborhood information of the potential source locations. Neighborhood information provides additional source constraints that model the correlation of neuronal activation which is necessary to generate detectable fields or potentials at all. In a surface-based 3D visualization environment, reconstruction results are displayed as color-coded overlays together with the cortical surface, the measurement setup, and arbitrary MR data slices. The algorithms described are part of the source reconstruction software package CURRY.
27343383
Anatomische Abbildung elektrischer Aktivität des ZNS
31316829
Restoration of the Sphere-Cortex Homeomorphism
The proposed algorithm has been developed as a pre-processing tool for inflating cortical surface meshes, which have been created using segmentation and subsequent triangulation of magnetic resonance images (MRI) [1]. It works directly on the triangulated surface and is therefore completely independent from the underlying segmentation. It needs no other information than the triangle mesh itself, which makes it generally applicable for the removal of topological noise. The homeomorphism between cortical surface and sphere is re-established by removing handles and opening connections. Moreover, the presented approach guarantees a manifold mesh by locally examining connectivity in the neighbourhood of each vertex and removing non-manifold components. It will be embedded into the source reconstruction software package CURRY (Compumedics Neuroscan, El Paso, TX, USA).
14497648
An Adaptation Reasoning Approach for Large Scale Component-based Applications
There is a growing demand for context-aware applications that can dynamically adapt to their run-time environment. An application offers a collection of functionalities that can be realized through a composition of software components and/or services that are made available at runtime. With the availability of alternative variants of such components and/or services that provide the basic functionalities, while differ in extra- functional characteristics, characterized by quality of services (QoS), an unforeseen number of application variants can be created. The variant that best fits the current context is selected through adaptation reasoning, which can suffer from the processing capabilities of resource-scarce mobile devices, especially when a huge number of application variants needs to be reason about. In this paper, we present a reasoning approach, which provides a meaningful adaptation decision for adaptive applications having a large number of variants within a reasonable time frame. The approach is validated through two arbitrary applications with large number of variants.
7586216
Linear Regression Models for Solvent Accessibility Prediction in Proteins
The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple and computationally much more efficient linear SVR performs comparably to nonlinear models and thus can be used in order to facilitate further attempts to design more accurate RSA prediction methods, with applications to fold recognition and de novo protein structure prediction methods.
6423442
Rule-based anomaly pattern detection for detecting disease outbreaks
This paper presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. When applied to our scenario, these traditional algorithms discover isolated outliers of particularly strange events, such as someone accidentally shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect anomalous patterns. These patterns are groups with specific characteristics whose recent pattern of illness is anomalous relative to historical patterns. We propose using a rule-based anomaly detection algorithm that characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard detection algorithm by measuring the number of false positives and the timeliness of detection. Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for evaluation. The results indicate that our algorithm has significantly better detection times for common significance thresholds while having a slightly higher false positive rate.
16829900
A Robust Speaking Face Modelling Approach Based on Multilevel Fusion
approach based on multilevel fusion of 3D face biometric information with audio and visual speech information for biometric identity verification applications. The proposed approach combines the information from three audio-video based modules, namely: audio, visual speech, and 3D face and performs tri-module fusion in an automatic, unsupervised and adaptive manner, by adapting to the local performance of each module. This is done by taking the output-score based reliability estimates (confidence measures) of each of the module into account. The module weightings are determined automatically such that the reliability measure of the combined scores is maximised. To test the robustness of the proposed approach, the audio and visual speech (mouth) modalities are degraded to emulate various levels of train/test mismatch; employing additive white Gaussian noise for the audio and JPEG compression for the video signals. The results show improved fusion performance for a range of tested levels of audio and video degradation, compared to the individual module performances. Experiments on a 3D stereovision database AVOZES show that, at severe levels of audio and video mismatch, the audio, mouth, 3D face, and tri-module (audio+mouth+3D face) fusion EERs were 42.9%, 32%, 15%, and 7.3% respectively for biometric speaker identity verification application. I.
15179866
Audio-Video Person Authentication Based on 3D Facial Feature Warping
In this paper we propose a novel feature warping technique based on thin-plate-spline (TPS) analysis for 3D audio-video person authentication systems. The TPS warp features model information related to non-rigid variations on speaking faces, such as expression lines, gestures, and wrinkles, enhancing the performance of the system against impostor and spoof attacks. Experiments with multimodal fusion of acoustic and TPS shape features for two different speaking face data corpus, VidTIMIT and AVOZES, allowed equal error rates (EERs) of less than 0.5 % for imposter attacks, less than 1 % for type-1 replay attacks (still photo and pre-recorded audio) and less than 2% for more complex type-2 replay attacks (pre-recorded video or fake CG animated video).