Alfonso Alba

Time-frequency-topographic analysis of induced power and synchrony of EEG signals during a Go/No-Go task

Induced changes in electroencephalographic power and synchrony between pairs of electrodes were assessed during the Go/No-Go task in 15 young adults. Processes common to both conditions, such as attention, activation of working memory, letter identification, and discrimination processes were characterized by increased power and synchrony in the following frequency ranges: delta band (inhibition of the non-relevant stimuli), theta band (activation of working memory), and low alpha band in occipital regions immediately after the stimulus (withhold or control of the execution of a response), and decreased power in the high alpha band from 300 to 700 ms. However, the most important findings were those specific for each condition. Changes in power in frontal areas were observed immediately after the stimulus in delta and high alpha bands for the Go condition and in the theta band for the No-Go condition. Increased synchrony and power at 1 Hz from 350-500 ms and increased power at 1, 5 and 6 Hz after 300 ms in the No-Go condition may be related to inhibition. Other important difference between conditions was observed in the synchronization increases of the gamma band between 33 and 36 Hz in the Go condition, whereas synchrony decreased at these frequencies in the No-Go condition; these differences may be due to the preparation and execution of the motor response during the Go condition and its inhibition in the No-Go condition.

Exploration of event-induced EEG phase synchronization patterns in cognitive tasks using a time–frequency-topography visualization system

In this paper, we present a method for the study of synchronization patterns measured from EEG scalp potentials in psychophysiological experiments. This method is based on various techniques: a time-frequency decomposition using sinusoidal filters which improve phase accuracy for low frequencies, a Bayesian approach for the estimation of significant synchrony changes, and a time-frequency-topography visualization technique which allows for easy exploration and provides detailed insights of a particular experiment. Particularly, we focus on in-phase synchrony using an instantaneous phase-lock measure. We also discuss some of the most common methods in the literature, focusing on their relevance to long-range synchrony analysis; this discussion includes a comparison among various synchrony measures. Finally, we present the analysis of a figure categorization experiment to illustrate our method.

Image registration using Markov random coefficient and geometric transformation fields

Image registration is central to different applications such as medical analysis, biomedical systems, and image guidance. In this paper we propose a new algorithm for multimodal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model based on coefficient and geometric fields. These coefficients, which represent the local intensity polynomial transformations, as the local geometric transformations, are modeled as prior information by means of Markov random fields. This probabilistic approach allows one to find optimal estimators by minimizing an energy function in terms of both fields, making the registration between the images possible.

Phase correlation based image alignment with subpixel accuracy

The phase correlation method is a well-known image alignment technique with broad applications in medical image processing, image stitching, and computer vision. This method relies on estimating the maximum of the phase-only correlation (POC) function, which is defined as the inverse Fourier transform of the normalized cross-spectrum between two images. The coordinates of the maximum correspond to the translation between the two images. One of the main drawbacks of this method, in its basic form, is that the location of the maximum can only be obtained with integer accuracy. In this paper, we propose a new technique to estimate the location with subpixel accuracy, by minimizing the magnitude of gradient of the POC function around a point near the maximum. We also present some experimental results where the proposed method shows an increased accuracy of at least one order of magnitude with respect to the base method. Finally, we illustrate the application of the proposed algorithm to the rigid registration of digital images.

Evaluation of multimodal medical image registration based on Particle Filter

This paper presents a performance evaluation of a new multimodal image registration algorithm which is based on Bayesian estimation theory, specifically on Particle Filters. The results point to an efficient, easy to implement and robust to noise algorithm. The registration method showed good performance when using partial data, and it was compared with an algorithm based on maximization of mutual information and a Hyperplanes optimization method. Finally, we showed that the algorithm may be parallelizable, so that it is possible to reduce the computation time for image registration.

Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep

This study aims to develop an automatic detector of the A phases of the cyclic alternating pattern, periodic activity that generally occurs during non-REM (NREM) sleep. Eight polysomnographic recordings from healthy subjects were examined. From EEG recordings, five band descriptors, an activity descriptor and a variance descriptor were extracted and used to train different machine-learning algorithms. A visual scoring provided by an expert clinician was used as golden standard. Four alternative mathematical machine-learning techniques were implemented: (1) discriminant classifier, (2) support vector machines, (3) adaptive boosting, and (4) supervised artificial neural network. The results of the classification, compared with the visual analysis, showed average accuracies equal to 84.9 and 81.5% for the linear discriminant and the neural network, respectively, while AdaBoost had a slightly lower accuracy, equal to 79.4%. The SVM leads to accuracy of 81.9%. The performance achieved by the automatic classification is encouraging, since an efficient automatic classifier would benefit the practice in everyday clinics, preventing the physician from the time-consuming activity of the visually scoring of the sleep microstructure over whole 8-h sleep recordings. Finally, the classification based on learning algorithms would provide an objective criterion, overcoming the problems of inter-scorer disagreement.

Phase correlation with sub-pixel accuracy

Six methods for the accurate estimation of phase-correlation maxima are evaluated.Methods are tested under noise, extreme transformations, incomplete data, and for real cases with unknown transformations.Sinc function fitting provides the best average accuracy.Local Center of Mass, and Minimization of the POC gradient provide good balance between accuracy and efficiency. Six methods for the accurate estimation of the phase-correlation maxima are discussed and evaluated in this article for one- and two-dimensional signals. The evaluation was carried out under a rigid image registration framework, where artificially generated transformations were used in order to perform a quantitative assessment of the accuracy of each method and its robustness in the presence of noise, incomplete data, or extreme transformations. Another round of tests were performed with real cases where the true transformation is unknown, and not necessarily rigid; for these tests, quantitative evaluation was achieved by means of the root mean square error of the overlapping area between the two aligned images. While most methods behaved similarly under difficult conditions, three of the methods under study displayed clear advantages under mild levels of noise, low transformation complexity, and small percentages of missing data. These methods are the local center of mass, sinc function fitting, and minimization of the POC gradient magnitude. The other tested methods included quadratic fitting, linear fitting in the frequency domain, and up-sampling; however, these methods did not perform consistently well.

A non-rigid multimodal image registration method based on particle filter and optical flow

Image Registration is a central task to many medical image analysis applications. In this paper, we present a novel iterative algorithm composed of two main steps: a global affine image registration based on particle filter, and a local refinement obtained from a linear optical flow approximation. The key idea is to iteratively apply these simple and robust steps to efficiently solve complex non-rigid multimodal or unimodal image registrations. Finally, we present a set of evaluation experiments demonstrating the accuracy and applicability of the method to medical images.

Phase-correlation guided area matching for realtime vision and video encoding

In computer vision and video encoding applications, one of the first and most important steps is to establish a pixel-to-pixel correspondence between two images of the same scene obtained at slightly different times or points of view. One of the most popular methods to find these correspondences, known as Area Matching, consists in performing a computationally intensive search for each pixel in the first image, around a neighborhood of the same pixel in the second image. In this work we propose a method which significantly reduces the search space to only a few candidates, and permits the implementation of real-time vision and video encoding algorithms which do not require specialized hardware such as GPU’s or FPGA’s. Theoretical and experimental support for this method is provided. Specifically, we present results from the application of the method to the realtime video compression and transmission, as well as the realtime estimation of dense optical flow and stereo disparity maps, where a basic implementation achieves up to 100 fps in a typical dual-core PC.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology--specifically, morphological erosions and dilations--that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level. The method is easily adapted to permutation-based procedures (with the usual restrictions), and therefore does not require strong assumptions about the distribution and spatio-temporal correlation structure of the data. Some examples of applications to synthetic data, including realistic fMRI simulations, as well as to real fMRI and electroencephalographic data are presented, illustrating the power of the presented technique. Comparisons with other methods that combine voxel intensity and cluster size, as well as some extensions of the method presented here based on their basic ideas are presented as well.A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology - specifically, morphological erosions and dilations - that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level. The method is easily adapted to permutation-based procedures (with the usual restrictions), and therefore does not require strong assumptions about the distribution and spatio-temporal correlation structure of the data. Some examples of applications to synthetic data, including realistic fMRI simulations, as well as to real fMRI and electroencephalographic data are presented, illustrating the power of the presented technique. Comparisons with other methods that combine voxel intensity and cluster size, as well as some extensions of the method presented here based on their basic ideas are presented as well.