An Optimized SWCSP Technique for Feature Extraction in EEG-based BCI System

Brain-computer interface (BCI) is an evolving technology having huge potential for rehabilitation of patients suffering from disorders of the nervous system, besides  many other nonmedical applications. Multichannel electroencephalography (EEG) is widely used to provide input signals to a BCI system. Significant research in methodology employed to implement different stages of BCI system, has led to discovery of new issues and challenges. The raw EEG data includes artifacts from environmental and physiological sources, which is eliminated in preprocessing phase of BCI system. It is then followed by a feature extraction stage to isolate a few relevant features for further classification to a particular motor imagery (MI) activity. A feature extraction approach based on spectrally weighted common spatial pattern (SWCSP) is proposed in this paper to improve overall accuracy of a BCI system. The reported literature uses SWCSP for feature extraction, as it has outperformed other techniques. The proposed approach enhances its performance by optimizing its parameters. The independent component analysis (ICA) method is used for detection and removal of irrelevant data, while linear discriminant analysis (LDA) is used as a classifier. The proposed approach is executed on benchmark data-set 2a of BCI competition IV. It yielded classification accuracy of 70.6% across nine subjects, which is higher than all the reported approaches.&nbsp

Volcanic Seismic Event Classification basedon CNN Architectures

This paper explores the use of convolutional neural network architectures in the context of volcanic seismic event classification through the use of gray-level spectrogram images of longperiod and volcano-tectonic seismic events. We combined the architectures with a set of hyperparameter configurations that produced 720 classification models, which were able to learn the morphological pattern described by the gray-level spectrogram images of seismic events...Este artículo explora el uso de arquitecturas de redes neuronales convolucionales en el contexto de clasificación de eventos sísmicos volcánicos mediante el uso de imágenes de espectrogramas en escala de grises de eventos sísmicos de período largo y volcano-tectónicos. Combinamos las arquitecturas con un conjunto de configuraciones de hiperparámetros que produjeron 720 modelos de clasificación, los cuales fueron capaces de aprender los patrones morfológicos descritos por las imágenes de espectrogramas en escala de grises..

Automatic classification of seismic events within a regional seismograph network

This paper presents a fully automatic method for seismic event classification within a sparse regional seismograph network. The method is based on a supervised pattern recognition technique called the Support Vector Machine (SVM). The classification relies on differences in signal energy distribution between natural and artificial seismic sources. We filtered seismic records via 20 narrow band-pass filters and divided them into four phase windows: P, P coda, S, and S coda. We then computed a short-term average (STA) value for each filter channel and phase window. The 80 discrimination parameters served as a training model for the SVM. We calculated station specific SVM models for 19 on-line seismic stations in Finland. The training data set included 918 positive (earthquake) and 3469 negative (non-earthquake) examples. An independent test period determined method and rules for integrating station-specific classification results into network results. Finally, we applied the network classification rules to independent evaluation data comprising 5435 fully automatic event determinations, 5404 of which had been manually identified as explosions or noise, and 31 as earthquakes. The SVM method correctly identified 94% of the non-earthquakes and all but one of the earthquakes. The result implies that the SVM tool can identify and filter out blasts and spurious events from fully automatic event solutions with a high level of accuracy. The tool helps to reduce the work-load and costs of manual seismic analysis by leaving only a small fraction of automatic event determinations, the probable earthquakes, for more detailed seismological analysis. The self-learning approach presented here is flexible and easily adjustable to the requirements of a denser or wider high-frequency network.Peer reviewe

A new dynamic tactile display for reconfigurable braille: implementation and tests

Different tactile interfaces have been proposed to represent either text (braille) or, in a few cases, tactile large-area screens as replacements for visual displays. None of the implementations so far can be customized to match users' preferences, perceptual differences and skills. Optimal choices in these respects are still debated; we approach a solution by designing a flexible device allowing the user to choose key parameters of tactile transduction. We present here a new dynamic tactile display, a 8 × 8 matrix of plastic pins based on well-established and reliable piezoelectric technology to offer high resolution (pin gap 0.7mm) as well as tunable strength of the pins displacement, and refresh rate up to 50s(−1). It can reproduce arbitrary patterns, allowing it to serve the dual purpose of providing, depending on contingent user needs, tactile rendering of non-character information, and reconfigurable braille rendering. Given the relevance of the latter functionality for the expected average user, we considered testing braille encoding by volunteers a benchmark of primary importance. Tests were performed to assess the acceptance and usability with minimal training, and to check whether the offered flexibility was indeed perceived by the subject as an added value compared to conventional braille devices. Different mappings between braille dots and actual tactile pins were implemented to match user needs. Performances of eight experienced braille readers were defined as the fraction of correct identifications of rendered content. Different information contents were tested (median performance on random strings, words, sentences identification was about 75%, 85%, 98%, respectively, with a significant increase, p < 0.01), obtaining statistically significant improvements in performance during the tests (p < 0.05). Experimental results, together with qualitative ratings provided by the subjects, show a good acceptance and the effectiveness of the proposed solution

Emotion Recognition from Acted and Spontaneous Speech

Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Druhá část se zabývá rozpoznáním emočních stavů z databáze spontánní řeči, která byla získána ze záznamů hovorů z reálných call center. Poznatky z analýzy a návrhu metod rozpoznání z hrané řeči byly využity pro návrh nového systému pro rozpoznání sedmi spontánních emočních stavů. Jádrem navrženého přístupu je komplexní klasifikační architektura založena na fúzi různých systémů. Práce se dále zabývá vlivem emočního stavu mluvčího na úspěšnosti rozpoznání pohlaví a návrhem systému pro automatickou detekci úspěšných hovorů v call centrech na základě analýzy parametrů dialogu mezi účastníky telefonních hovorů.Doctoral thesis deals with emotion recognition from speech signals. The thesis is divided into two main parts; the first part describes proposed approaches for emotion recognition using two different multilingual databases of acted emotional speech. The main contributions of this part are detailed analysis of a big set of acoustic features, new classification schemes for vocal emotion recognition such as “emotion coupling” and new method for mapping discrete emotions into two-dimensional space. The second part of this thesis is devoted to emotion recognition using multilingual databases of spontaneous emotional speech, which is based on telephone records obtained from real call centers. The knowledge gained from experiments with emotion recognition from acted speech was exploited to design a new approach for classifying seven emotional states. The core of the proposed approach is a complex classification architecture based on the fusion of different systems. The thesis also examines the influence of speaker’s emotional state on gender recognition performance and proposes system for automatic identification of successful phone calls in call center by means of dialogue features.

Methodology for the recognition of non-stationary seismic-volcanic patterns using adaptive learning techniques

graficas, tablasEl monitoreo volcánico constituye una tarea imprescindible en el contexto de prevención y gestión del riesgo; en este sentido, los observatorios vulcanológicos y sismológicos cumplen una misión trascendental en la declaración de alertas tempranas de erupción volcánica. Y dentro de esta labor, la correcta clasificación de la sismicidad representa un insumo indispensable para la interpretación del fenómeno volcánico y la caracterización de dinámicas eruptivas; por tal motivo, es necesario que la clasificación se lleve a cabo de manera ágil y confiable. A través de la sismicidad correctamente etiquetada, los expertos analistas pueden caracterizar los procesos que estarían ocurriendo al interior de un volcán, e identificar precursores de una erupción. Sin embargo, la acertada discriminación de eventos sísmicos suele verse afectada por la migración de fuentes sísmicas, alteraciones en la dinámica de fluidos, cambios en los mecanismos de generación de grietas, entre otras situaciones, que pueden modificar la distribución de probabilidad de los registros sísmicos (cambios de concepto), y por tanto, incrementar la no estacionariedad de estas señales. Durante las últimas dos décadas, en las áreas de Aprendizaje Automático y Reconocimiento de Patrones se han desarrollado múltiples técnicas y herramientas aplicadas a enfoques de representación y clasificación de sismos volcánicos, entre las cuales destacan las redes neuronales, las máquinas de vectores de soporte, los modelos ocultos de Markov, entre otros, enmarcados (incluso) en contexto muy actuales como el Aprendizaje Profundo. En general, los estudios hallados al respecto en el estado del arte muestran resultados optimistas; sin embargo, se detalla que éstos son consecuencia de configuraciones experimentales restrictivas que disminuyen la complejidad del problema de clasificación planteado; una condición común es el uso de datos procedentes de periodos cortos de registro y poco representativos de la actividad volcánica. Esta limitación simula un entorno estacionario donde los modelos predictivos tradicionales funcionan eficazmente, pero que van en detrimento al actuar por un tiempo prolongado cuando los cambios de concepto se hacen evidentes. Siendo notable la necesidad de disponer de sistemas automáticos de clasificación que satisfagan las ``condiciones realistas'' del problema, como requerimiento esencial en la vigilancia volcánica, en esta tesis se propone el desarrollo de una metodología de reconocimiento de patrones sísmicos, a partir de registros de eventos volcánicos, que considere la adaptación de la clasificación a entornos y condiciones realistas y cambiantes. Para ello, se diseñó un modelo de clasificación centrado en el área del aprendizaje adaptativo y basado en aprendizaje incremental (aún no explorados en datos sísmicos), con el cual se trata el paradigma del cambio del concepto, de tal manera que algunas propiedades como la recurrencia continua de datos adquiridos, la naturaleza multiclase de los registros, los efectos geológicos y las restricciones de generalización en la clasificación, sean contempladas, aprovechadas y eventualmente contrarrestadas al momento de hacer la clasificación automática de los sismos (Texto tomado de la fuente)Volcanic monitoring is an essential task in the context of prevention and risk management; in this sense, the volcanological and seismological observatories fulfill a transcendental mission in the declaration of early warnings of volcanic eruptions. And within this labor, the correct classification of seismicity represents an indispensable supply for the interpretation of the volcanic phenomenon and the characterization of eruptive dynamics; for this reason, it is necessary to carry out the classification in an agile and reliable manner. Through correctly labeled seismicity, expert analysts may characterize the processes that would be taking place inside a volcano, and identify precursors of an eruption. However, the accurate discrimination of seismic events is usually affected by the migration of seismic sources, alterations in fluid dynamics, changes in crack generation mechanisms, among other situations. These conditions may modify the probability distribution of seismic records (concept drifts), and therefore, strengthen the non-stationarity of these signals. During the last two decades, multiple techniques and tools have been developed in Machine Learning and Pattern Recognition areas, and applied to representation and classification approaches of volcanic earthquakes. Neural networks, support vector machines, hidden Markov models are the most outstanding methods that have even been framed in very current contexts such as Deep Learning. In general, the studies found in this regard in the state of the art show optimistic results, however, they are the consequence of restrictive experimental configurations that decrease the complexity of the posed classification problem. A common condition is data usage from short periods of registration and unrepresentative of the volcanic activity. This limitation simulates a stationary environment where traditional predictive models work effectively, but their performance deteriorates when acting for a long time because concept changes become evident. The need to have automatic classification systems that satisfy the ``realistic conditions'' of the problem becomes evident, as an essential requirement in volcanic monitoring and eruption prediction. Therefore, this thesis proposes the development of a seismic pattern recognition methodology, based on records of volcanic events, which considers the adaptation of the classification to realistic and changing environments and conditions. For this, a classification model focused on the area of adaptive learning and based on incremental learning (not yet explored in seismic data) was designed, with which the concept drift paradigm is treated. This way, some properties such as the continuous arrival of acquired data, the multiclass nature of the records, the geological effects and the generalization restrictions in the classification are considered, exploited and eventually counteracted when automatically classifying the volcanic earthquakes.Este trabajo se ha llevado a cabo gracias al patrocinio económico del Programa Nacional de Formación de Investigadores, modalidad Doctorado Nacional, Convocatoria 617, de MINCIENCIAS (antes COLCIENCIAS).Declaración. Me permito afirmar que he realizado la presente tesis de manera autónoma y con la única ayuda de los medios permitidos y no diferentes a los mencionados en la propia tesis. Todos los pasajes que se han tomado de manera textual o figurativa de textos publicados y no publicados, los he reconocido en el presente trabajo. Ninguna parte del presente trabajo se ha empleado en ningún otro tipo de tesis.DoctoradoDoctor en IngenieríaReconocimiento de patronesEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizale