Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.

A Stress-Detection System Based on Physiological Signals and Fuzzy Logic

A stress-detection system is proposed based on physiological signals. Concretely, galvanic skin response (GSR) and heart rate (HR) are proposed to provide information on the state of mind of an individual, due to their nonintrusiveness and noninvasiveness. Furthermore, specific psychological experiments were designed to induce properly stress on individuals in order to acquire a database for training, validating, and testing the proposed system. Such system is based on fuzzy logic, and it described the behavior of an individual under stressing stimuli in terms of HR and GSR. The stress-detection accuracy obtained is 99.5% by acquiring HR and GSR during a period of 10 s, and what is more, rates over 90% of success are achieved by decreasing that acquisition period to 3-5 s. Finally, this paper comes up with a proposal that an accurate stress detection only requires two physiological signals, namely, HR and GSR, and the fact that the proposed stress-detection system is suitable for real-time applications.

Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation

The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity.

Cartography for cooperative manoeuvres with autonomous land vehicles

This article presents a cartographic system to facilitate cooperative manoeuvres among autonomous vehicles in a well-known environment. The main objective is to design an extended cartographic system to help in the navigation of autonomous vehicles. This system has to allow the vehicles not only to access the reference points needed for navigation, but also noticeable information such as the location and type of traffic signals, the proximity to a crossing, the streets en route, etc. To do this, a hierarchical representation of the information has been chosen, where the information has been stored in two levels. The lower level contains the archives with the Universal Traverse Mercator (UTM) coordinates of the points that define the reference segments to follow. The upper level contains a directed graph with the relational database in which streets, crossings, roundabouts and other points of interest are represented. Using this new system it is possible to know when the vehicle approaches a crossing, what other paths arrive at that crossing, and, should there be other vehicles circulating on those paths and arriving at the crossing, which one has the highest priority. The data obtained from the cartographic system is used by the autonomous vehicles for cooperative manoeuvres.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Spatial domain mitigation of out of band strong interferers in HF wide band acquisition using analog beamforming principles

We envision that dynamic multiband transmissions taking advantage of the receiver diversity (even for collocated antennas with different polarization or radiation pattern) will create a new paradigm for these links guaranteeing high quality and reliability. However, there are many challenges to face regarding the use of broadband reception where several out of band (with respect to multiband transmission) strong interferers, but still within the acquisition band, may limit dramatically the expected performance. In this paper we address this problem introducing a specific capability of the communication system that is able to mitigate these interferences using analog beamforming principles. Indeed, Higher Order Crossing (HOCs) joint statistics of the Single Input ? Multiple Output (SIMO) system are shown to effectively determine the angle on arrival of the wavefront even operating over highly distorted signals.

Analysis and design of multirate synchronous sampling schemes for sparse multiband signals

We consider the problem of developing efficient sampling schemes for multiband sparse signals. Previous results on multicoset sampling implementations that lead to universal sampling patterns (which guarantee perfect reconstruction), are based on a set of appropriate interleaved analog to digital converters, all of them operating at the same sampling frequency. In this paper we propose an alternative multirate synchronous implementation of multicoset codes, that is, all the analog to digital converters in the sampling scheme operate at different sampling frequencies, without need of introducing any delay. The interleaving is achieved through the usage of different rates, whose sum is significantly lower than the Nyquist rate of the multiband signal. To obtain universal patterns the sampling matrix is formulated and analyzed. Appropriate choices of the parameters, that is the block length and the sampling rates, are also proposed.

Design of universal multicoset sampling patterns for compressed sensing of multiband sparse signals

Many problems in digital communications involve wideband radio signals. As the most recent example, the impressive advances in Cognitive Radio systems make even more necessary the development of sampling schemes for wideband radio signals with spectral holes. This is equivalent to considering a sparse multiband signal in the framework of Compressive Sampling theory. Starting from previous results on multicoset sampling and recent advances in compressive sampling, we analyze the matrix involved in the corresponding reconstruction equation and define a new method for the design of universal multicoset codes, that is, codes guaranteeing perfect reconstruction of the sparse multiband signal.

Quantum optical signals in telecommunication networks

Introducción a una red óptica metropolitana cuántica donde señales cuánticas y convencionales son multiplexadas en longitud de onda utilizando bandas separadas del espectro óptico. El enrutado se realiza con componentes pasivos para no perturbar los qubits. Se estudian dos redes: red de acceso WDM-PON y red metropolitana completa con backbone DWDM y redes de acceso WDM-PON.

Signals and Systems Students trainer.

Higher education students demand fast feedback about their assignments and the opportunity to repeat them in case they do in a wrong way. Here a computer based trainer for Signals and Systems students is presented. An application, that automatically generates and assesses thousands of numerically different versions of several Signals and Systems problems have been developed. This applet guides the students to find the solution and automatically assesses and grades the students proposed solution. The students can use the application to practice in solving several types of Signals and Systems basic problems. After selecting the problem type, the student introduces a seed and the application generates a numerical version of the selected problem. Then the application presents a sequence of questions that the students must solve and the application automatically assess their answers. After solving a given problem, the students can repeat the same numerical variation of the problem by introducing the same seed to the application. In this way, they can review their solution with the help of the hints given by the application for wrong solutions. This application can also be used as an automatic assessment tool by the instructor. When the assessment is made in a controlled environment (examination classroom or laboratory) the instructor can use the same seed for all students. Otherwise, different seeds can be assigned to different students and in this way they solve different numerical variation of the proposed problem, so cheating becomes an arduous task. Given a problem type, the mathematical or conceptual difficulty of the problem can vary depending on the numerical values of the parameters of the problem. The application permits to easily select groups of seeds that yield to numerical variations with similar mathematical or conceptual difficulty. This represents an advantage over a randomised task assignment where students are asked to solve tasks with different difficulty.

Codificador de voz MIDI

El presente proyecto tiene el objetivo de facilitar la composición de canciones mediante la creación de las distintas pistas MIDI que la forman. Se implementan dos controladores. El primero, con objeto de transcribir la parte melódica, convierte la voz cantada o tarareada a eventos MIDI. Para ello, y tras el estudio de las distintas técnicas del cálculo del tono (pitch), se implementará una técnica con ciertas variaciones basada en la autocorrelación. También se profundiza en el segmentado de eventos, en particular, una técnica basada en el análisis de la derivada de la envolvente. El segundo, dedicado a la base rítmica de la canción, permite la creación de la percusión mediante el golpe rítmico de objetos que disponga el usuario, que serán asignados a los distintos elementos de percusión elegidos. Los resultados de la grabación de estos impactos serán señales de corta duración, no lineales y no armónicas, dificultando su discriminación. La herramienta elegida para la clasificación de los distintos patrones serán las redes neuronales artificiales (RNA). Se realizara un estudio de la metodología de diseño de redes neuronales especifico para este tipo de señales, evaluando la importancia de las variables de diseño como son el número de capas ocultas y neuronas en cada una de ellas, algoritmo de entrenamiento y funciones de activación. El estudio concluirá con la implementación de dos redes de diferente naturaleza. Una red de Elman, cuyas propiedades de memoria permiten la clasificación de patrones temporales, procesará las cualidades temporales analizando el ataque de su forma de onda. Una red de propagación hacia adelante feed-forward, que necesitará de robustas características espectrales y temporales para su clasificación. Se proponen 26 descriptores como los derivados de los momentos del espectro: centroide, curtosis y simetría, los coeficientes cepstrales de la escala de Mel (MFCCs), y algunos temporales como son la tasa de cruces por cero y el centroide de la envolvente temporal. Las capacidades de discriminación inter e intra clase de estas características serán evaluadas mediante un algoritmo de selección, habiéndose elegido RELIEF, un método basado en el algoritmo de los k vecinos mas próximos (KNN). Ambos controladores tendrán función de trabajar en tiempo real y offline, permitiendo tanto la composición de canciones, como su utilización como un instrumento más junto con mas músicos. ABSTRACT. The aim of this project is to make song composition easier by creating each MIDI track that builds it. Two controllers are implemented. In order to transcribe the melody, the first controler converts singing voice or humming into MIDI files. To do this a technique based on autocorrelation is implemented after having studied different pitch detection methods. Event segmentation has also been dealt with, to be more precise a technique based on the analysis of the signal's envelope and it's derivative have been used. The second one, can be used to make the song's rhythm . It allows the user, to create percussive patterns by hitting different objects of his environment. These recordings results in short duration, non-linear and non-harmonic signals. Which makes the classification process more complicated in the traditional way. The tools to used are the artificial neural networks (ANN). We will study the neural network design to deal with this kind of signals. The goal is to get a design methodology, paying attention to the variables involved, as the number of hidden layers and neurons in each, transfer functions and training algorithm. The study will end implementing two neural networks with different nature. Elman network, which has memory properties, is capable to recognize sequences of data and analyse the impact's waveform, precisely, the attack portion. A feed-forward network, needs strong spectral and temporal features extracted from the hit. Some descriptors are proposed as the derivates from the spectrum moment as centroid, kurtosis and skewness, the Mel-frequency cepstral coefficients, and some temporal features as the zero crossing rate (zcr) and the temporal envelope's centroid. Intra and inter class discrimination abilities of those descriptors will be weighted using the selection algorithm RELIEF, a Knn (K-nearest neighbor) based algorithm. Both MIDI controllers can be used to compose, or play with other musicians as it works on real-time and offline.

Alta velocidad ferroviaria en California(USA): quinta parte (V) LAV San Francisco–Sacramento (Bay Crossing Alternative) = High speed railway in California (USA): fith part (V) HSRL San Francisco–Sacramento (Bay Crossing Alternative)

Implantación de la Red de Alta velocidad Ferroviaria en California. Tramo San Francisco-Sacramento. Este artículo de la serie “Alta velocidad Ferroviaria en California (CHSRS), se ocupa de la línea San Francisco– Sacramento “Bay Crossing Alternative”, que cierra la red de alta velocidad ferroviaria del Estado de California, permitiendo en la terminal HSR de Sacramento, conectar con la línea Fresno–Sacramento, en coincidencia de trazados para en el futuro prolongar la red californiana de alta velocidad ferroviaria hasta su entronque con la del Estado de Nevada, vía Tahoe Lake–Reno. La línea San Francisco–Sacramento “Bay Crossing Alternative”, consta de tres trayectos: El primero de ellos “San Francisco urbano” va desde la terminal HSR “San Francisco Airport”, donde termina la alternativa “Golden Gate” de la línea Fresno–San Francisco, hasta el viaducto de acceso al Paso de la Bahía, que constituye el segundo trayecto “San Francisco–Richmond”, trayecto estrella de la red, de 15,48 Km de longitud sobre la Bahía de San Francisco, con desarrollo a través de 11,28 Km en puente colgante múltiple, con vanos de 800 m de luz y 67 m de altura libre bajo el tablero que permite la navegación en la Bahía. El tercer trayecto “Richmond–Sacramento” cruza la Bahía de San Pablo con un puente colgante de 1,6 Km de longitud y tipología similar a los múltiples de la Bahía de San Francisco, pasa por Vallejo (la por plazo breve de tiempo, antigua capital del Estado de California) y por la universitaria Davis, antes de finalmente llegar a la HSR Terminal Station de Sacramento Roseville. This article of the series “California High Speed Railway System”(CHSRS) treats on Line San Francisco–Sacramento “Bay Crossing Alternative” (BCA). This line closes the system of California high speed state railway, and connects with the line Fresno–Sacramento “Stockton Arch Alternative”, joining its alignments in the HSR Terminal of Sacramento Roseville. From this station it will be possible, in the future, to extend the Californian railway system till the Nevada railway system, vía Tahoe Lake and Reno. The BCA consists of three sections: The first one passing through San Francisco city, goes from HSR San Francisco Airport Terminal Station (where the line Fresno–San Francisco “Golden Gate Alternative” ends), up to the Viaduct access at the Bay Crossing. The second section San Francisco–Richmond, constitutes the star section of the system, with 15,48 Km length on the San Francisco Bay, where 11,28 Km in multi suspension bridge, 800 m span and 67 m gauge under panel, to allow navigation through the Bay. The third section Richmond–Sacramento crosses the San Pablo Bay through another suspension bridge of similar typology to that of San Francisco Bay crossing; pass through Vallejo (the ancient and for a short time Head of the State of California) and through Davis, university city, to arrive to the HSR Terminal Station of Sacramento Roseville.

Secure transmission of information by digital chaotic signals

Protecting signals is one of the main tasks in information transmission. A large number of different methods have been employed since many centuries ago. Most of them have been based on the use of certain signal added to the original one. When the composed signal is received, if the added signal is known, the initial information may be obtained. The main problem is the type of masking signal employed. One possibility is the use of chaotic signals, but they have a first strong limitation: the need to synchronize emitter and receiver. Optical communications systems, based on chaotic signals, have been proposed in a large number of papers. Moreover, because most of the communication systems are digital and conventional chaos generators are analogue, a conversion analogue-digital is needed. In this paper we will report a new system where the digital chaos is obtained from an optically programmable logic structure. This structure has been employed by the authors in optical computing and some previous results in chaotic signals have been reported. The main advantage of this new system is that an analogue-digital conversion is not needed. Previous works by the authors employed Self-Electrooptical Effect Devices but in this case more conventional structures, as semiconductor laser amplifiers, have been employed. The way to analyze the characteristics of digital chaotic signals will be reported as well as the method to synchronize the chaos generators located in the emitter and in the receiver.