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.

Aspectos de la medición dinámica instantánea de emisiones de motores. Aplicación al desarrollo de un equipo portátil y una metodología para estudios de contaminación de vehículos en tráfico real.

Esta Tesis Doctoral se encuadra en el ámbito de la medida de emisiones contaminantes y de consumo de combustible en motores de combustión interna alternativos cuando se utilizan como plantas de potencia para propulsión de vehículos ligeros de carretera, y más concretamente en las medidas dinámicas con el vehículo circulando en tráfico real. En este ámbito, el objetivo principal de la Tesis es estudiar los problemas asociados a la medición en tiempo real con equipos embarcados de variables medioambientales, energéticas y de actividad, de vehículos ligeros propulsados por motores térmicos en tráfico real. Y como consecuencia, desarrollar un equipo y una metodología apropiada para este objetivo, con el fin de realizar consiguientemente un estudio sobre los diferentes factores que influyen sobre las emisiones y el consumo de combustible de vehículos turismo en tráfico real. La Tesis se comienza realizando un estudio prospectivo sobre los trabajos de otros autores relativos al desarrollo de equipos portátiles de medida de emisiones (Portable Emission Measurement Systems – PEMS), problemas asociados a la medición dinámica de emisiones y estudios de aplicación en tráfico real utilizando este tipo de equipos. Como resultado de este estudio se plantea la necesidad de disponer de un equipo específicamente diseñado para ser embarcado en un vehículo que sea capaz de medir en tiempo real las concentraciones de emisiones y el caudal de gases de escape, al mismo tiempo que se registran variables del motor, del vehículo y del entorno como son la pendiente y los datos meteorológicos. De esta forma se establecen las especificaciones y condiciones de diseño del equipo PEMS. Aunque al inicio de esta Tesis ya existían en el mercado algunos sistemas portátiles de medida de emisiones (PEMS: Portable Emissions Measurement Systems), en esta Tesis se investiga, diseña y construye un nuevo sistema propio, denominado MIVECO – PEMS. Se exponen, discuten y justifican todas las soluciones técnicas incorporadas en el sistema que incluyen los subsistema de análisis de gases, subsistemas de toma de muestra incluyendo caudalímetro de gases de escape, el subsistema de medida de variables del entorno y actividad del vehículo y el conjunto de sistemas auxiliares. El diseño final responde a las hipótesis y necesidades planteadas y se valida en uso real, en banco de rodillos y en comparación con otro equipos de medida de emisiones estacionarios y portátiles. En esta Tesis se presenta también toda la investigación que ha conducido a establecer la metodología de tratamiento de las señales registradas en tiempo real que incluye la sincronización, cálculos y propagación de errores. La metodología de selección y caracterización de los recorridos y circuitos y de las pautas de conducción, preparación del vehículo y calibración de los equipos forma también parte del legado de esta Tesis. Para demostrar la capacidad de medida del equipo y el tipo de resultados que pueden obtenerse y que son útiles para la comunidad científica, y las autoridades medioambientales en la parte final de esta Tesis se plantean y se presentan los resultados de varios estudios de variables endógenas y exógenas que afectan a las emisiones instantáneas y a los factores de emisión y consumo (g/km) como: el estilo de conducción, la infraestructura vial, el nivel de congestión del tráfico, tráfico urbano o extraurbano, el contenido de biocarburante, tipo de motor (diesel y encendido provocado), etc. Las principales conclusiones de esta Tesis son que es posible medir emisiones másicas y consumo de motores de vehículos en uso real y que los resultados permiten establecer políticas de reducción de impacto medio ambiental y de eficiencia energética, pero, se deben establecer unas metodologías precisas y se debe tener mucho cuidado en todo el proceso de calibración, medida y postratamientos de los datos. Abstract This doctoral thesis is in the field of emissions and fuel consumption measurement of reciprocating internal combustion engines when are used as power-trains for light-duty road vehicles, and especially in the real-time dynamic measurements procedures when the vehicle is being driven in real traffic. In this context, the main objective of this thesis is to study the problems associated with on-board real-time measuring systems of environmental, energy and activity variables of light vehicles powered by internal combustion engines in real traffic, and as a result, to develop an instrument and an appropriate methodology for this purpose, and consequently to make a study of the different factors which influence the emissions and the fuel consumption of passenger cars in real traffic. The thesis begins developing a prospective study on other authors’ works about development of Portable Emission Measurement Systems (PEMS), problems associated with dynamic emission measurements and application studies on actual traffic using PEMS. As a result of this study, it was shown that a measuring system specifically designed for being on-board on a vehicle, which can measure in real time emission concentrations and exhaust flow, and at the same time to record motor vehicle and environment variables as the slope and atmospheric data, is needed; and the specifications and design parameters of the equipment are proposed. Although at the beginning of this research work there were already on the market some PEMS, in this Thesis a new system is researched, designed and built, called MIVECO – PEMS, in order to meet such measurements needs. Following that, there are presented, discussed and justify all technical solutions incorporated in the system, including the gas analysis subsystem, sampling and exhaust gas flowmeter subsystem, the subsystem for measurement of environment variables and of the vehicle activity and the set of auxiliary subsystems. The final design meets the needs and hypotheses proposed, and is validated in real-life use and chassis dynamometer testing and is also compared with other stationary and on-board systems. This thesis also presents all the research that has led to the methodology of processing the set of signals recorded in real time including signal timing, calculations and error propagation. The methodology to select and characterize of the routes and circuits, the driving patterns, and the vehicle preparation and calibration of the instruments and sensors are part of the legacy of this thesis. To demonstrate the measurement capabilities of the system and the type of results that can be obtained and that are useful for the scientific community and the environmental authorities, at the end of this Thesis is presented the results of several studies of endogenous and exogenous variables that affect the instantaneous and averaged emissions and consumption factors (g/km), as: driving style, road infrastructure, the level of traffic congestion, urban and extra-urban traffic, biofuels content, type of engine (diesel or spark ignition) etc. The main conclusions of this thesis are that it is possible to measure mass emissions and consumption of vehicle engines in actual use and that the results allow us to establish policies to reduce environmental impact and improve energy efficiency, but, to establish precise methodologies and to be very careful in the entire process of calibration, measurement and data post-treatment is necessary.

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.

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

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.

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.