1,721,063 research outputs found
Guidelines for effective automatic multiple sclerosis lesion segmentation by magnetic resonance imaging
No full textGeneral constraints for automatic identification/segmentation of multiple sclerosis (MS) lesions by Magnetic Resonance Imaging (MRI) are discussed and guidelines for effective training of a supervised technique are presented. In particular, system generalizability to different imaging sequences and scanners from different manufacturers, misalignment between images from different modalities and subjectivity in generating labelled images, are indicated as the main limitations to high accuracy automatic MS lesions identification/segmentation. A convolutional neural network (CNN) based method is used by applying the suggested guidelines and preliminary results demonstrate the improvements. The method has been trained, validated and tested on publicly available labelled MRI datasets. Future developments and perspectives are also presented
Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives
Full text linkA fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities
A fast and scalable framework for automated artifact recognition from EEG signals represented in scalp topographies of Independent Components
No full textBackground and objectives: Electroencephalography (EEG) measures the electrical brain activity in real-time by using sensors placed on the scalp. Artifacts due to eye movements and blinking, muscular/cardiac activity and generic electrical disturbances, have to be recognized and eliminated to allow a correct interpretation of the Useful Brain Signals (UBS). Independent Component Analysis (ICA) is effective to split the signal into Independent Components (IC) whose re-projection on 2D topographies of the scalp (images also called Topoplots) allows to recognize/separate artifacts and UBS. Topoplot analysis, a gold standard for EEG, is usually carried out offline either visually by human experts or through automated strategies, both unenforceable when a fast response is required as in online Brain-Computer Interfaces (BCI). We present a fully automatic, effective, fast, scalable framework for artifacts recognition from EEG signals represented in IC Topoplots to be used in online BCI. Methods: The proposed architecture, optimized to contain three 2D Convolutional Neural Networks (CNN), divides Topoplots in 4 classes: 3 types of artifacts and UBS. The framework architecture is described and the results are presented, discussed and indirectly compared with those obtained from state-of-the-art competitive strategies. Results: Experiments on public EEG datasets showed overall accuracy, sensitivity and specificity greater than 98%, taking 1.4 s on a standard PC for 32 Topoplots, i.e. for an EEG system with at least 32 sensors. Conclusions: The proposed framework is faster than other automatic methods based on IC analysis and fast enough to be used in EEG-based online BCI. In addition, its scalable architecture and ease of training are necessary conditions to apply it in BCI, where difficult operating conditions caused by uncontrolled muscle spasms, eye rotations or head movements, produce specific artifacts that need to be recognized and dealt with
A Web application for characterizing spontaneous emotions using long EEG recording sessions
No full textEmotions are important in daily life and in several research fields, especially in Brain Computer Interface (BCI) and Affective Computing. Usually, emotions are studied by analyzing the brain activity of a subject, monitored by Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI) or functional Near Infrared Spectroscopy (fNIRS), after some external stimulation. This approach could lead to characterization inaccuracies, due to the secondary activations produced by the artificial elicitation and to the subjective emotional response. In this work, we design a web application to support spontaneous emotions characterization. It is based on a database for EEG signals where a large amount of data from long recording sessions, collected from subjects during their daily life, are stored. In this way, EEG signals can be explored to characterize different spontaneous emotional states felt by several people. The application is also designed to extract features of specific emotions, and to compare different emotional states. Researchers all over the world could share both raw data and classification results. Since large datasets are treated, the application is based on strategies commonly used in big data managing. In particular, a column-oriented database is used to store a huge amount of raw EEG signals, while a relational database is employed to keep metadata information. A web application interface allows the user to communicate with the repository and a computational module performs the features extraction
Graph model of phase lag index for connectivity analysis in EEG of emotions
No full textEmotion recognition is useful in several fields, starting from medical diagnosis to driving a Brain-Computer Interface (BCI) or helping people with disabilities. During the last decades, many researchers applied automatic strategies to identify emotional states based on data acquired by electroen-cephalography (EEG). However, the task is very hard and results have been often ambiguous. This work aims to perform brain connectivity studies of EEG data of four self-stimulated emotional classes ('relax', 'anger', 'happiness', 'sadness') using a graph model of the Phase Lag Index (PLI), being PLI a measurement of connection insensitive to volume conduction effect. Qualitative results show that, for the analyzed emotions, connectivity analysis indicates some relevant differences both in the active brain regions and in the bandwidths involved in the activation. This method for connectome generation and analysis shows that useful information can be derived and used for contributing to disambiguating the problem of automatic emotion recognition
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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