Abstract: For millennia, the study of the couple brain-mind has fascinated the humanity in order to understand the complex nature of cognitive states. A cognitive state is the state of the mind at a specific time and involves cognition activities to acquire and process information for making a decision, solving a problem, or achieving a goal.
While normal cognitive states assist in the successful accomplishment of tasks; on the contrary, abnormal states of the mind can lead to task failures due to a reduced cognition capability. In this thesis, we focus on the assessment of cognitive states by means of the analysis of ElectroEncephaloGrams (EEG) signals using deep learning methods. EEG records the electrical activity of the brain using a set of electrodes placed on the scalp that output a set of spatiotemporal signals that are expected to be correlated to a specific mental process.
From the point of view of artificial intelligence, any method for the assessment of cognitive states using EEG signals as input should face several challenges. On the one hand, one should determine which is the most suitable approach for the optimal combination of the multiple signals recorded by EEG electrodes. On the other hand, one should have a protocol for the collection of good quality unambiguous annotated data, and an experimental design for the assessment of the generalization and transfer of models. In order to tackle them, first, we propose several convolutional neural architectures to perform data fusion of the signals recorded by EEG electrodes, at raw signal and feature levels. Four channel fusion methods, easy to incorporate into any neural network architecture, are proposed and assessed. Second, we present a method to create an unambiguous dataset for the prediction of cognitive mental workload using serious games and an Airbus-320 flight simulator. Third, we present a validation protocol that takes into account the levels of generalization of models based on the source and amount of test data.
Finally, the approaches for the assessment of cognitive states are applied to two use cases of high social impact: the assessment of mental workload for personalized support systems in the cockpit and the detection of epileptic seizures. The results obtained from the first use case show the feasibility of task transfer of models trained to detect workload in serious games to real flight scenarios. The results from the second use case show the generalization capability of our EEG channel fusion methods at k-fold cross-validation, patient-specific, and population levels.

Abstract: The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.

Abstract: Dagstuhl Workshop

Abstract: En los últimos años, se ha introducido ámpliamente el uso de los sistemas CAD en dominios relacionados con la arquitectura. Dichos sistemas CAD son muy útiles para el arquitecto en el diseño de planos de plantas de edificios. Sin embargo, la utilización eficiente de un CAD requiere un tiempo de aprendizaje, en especial, en la etapa de creación y edición del diseño. Además, una vez familiarizado con un CAD, el arquitecto debe adaptarse a la simbología que éste le permite que, en algunos casos puede ser poco flexible.Con esta motivación, se propone una técnica alternativa de entrada de documentos en sistemas CAD. Dicha técnica se basa en el diseño del plano sobre papel mediante un dibujo lineal hecho a mano alzada a modo de boceto e introducido mediante scanner. Una vez interpretado este dibujo inicial e introducido en el CAD, el arquitecto sólo deber hacer sobre éste los retoques finales del documento.El sistema de entrada propuesto se compone de dos módulos principales: En primer lugar, la extracción de características (puntos característicos, rectas y arcos) de la imagen obtenida mediante scanner. En dicho módulo se aplican principalmente técnicas de procesamiento de imágenes obteniendo como resultado una representaci¢n del dibujo de entrada basada en grafos de atributos. El objetivo del segundo módulo es el de encontrar y reconocer las entidades integrantes del documento (puertas, mesas, etc.) en base a una biblioteca de símbolos definida en el sistema CAD. La implementación de dicho módulo se basa en técnicas de isomorfismo de grafos.El sistema propone una alternativa que permita, mediante el diseño a mano alzada, la introducción de la informaci¢n m s significativa del plano de forma rápida, sencilla y estandarizada por parte del usuario.