1,720,972 research outputs found
Design of a Framework for Personalised 3D Modelling from Medical Images
No full textIn two previous works, we introduced 3D Bio-IPF, a general purpose framework to support the three-dimensional reconstruction, rendering and processing of biomedical images. Moreover, one of its structured components, the Implant plug-in, to model customised dental implants on a three-dimensional representation of the oral cavity derived from diagnostic images has also been presented. In this paper, we provide final details of the 3D Bio-IPF framework and, at the same time, we complete the description of the Implant plug-in. We used Implant to evaluate different functionalities of the whole framework, and a characteristic result chosen among these carried out is reported. Results show that the framework and the linked plug-in are very effective for dental surgery planning, implant design and positioning. Moreover, if integrated with a position indicator system and a numerically positionable drilling machine, the system could be employed for semi-automatic surgery
Design of a framework for personalised 3D modelling from medical images
No full textIn two previous works, we introduced 3D Bio-IPF, a general purpose framework to support the three-dimensional reconstruction, rendering and processing of biomedical images. Moreover, one of its structured components, the Implant plug-in, to model customised dental implants on a three-dimensional representation of the oral cavity derived from diagnostic images has also been presented. In this paper, we provide final details of the 3D Bio-IPF framework and, at the same time, we complete the description of the Implant plug-in. We used Implant to evaluate different functionalities of the whole framework, and a characteristic result chosen among these carried out is reported. Results show that the framework and the linked plug-in are very effective for dental surgery planning, implant design and positioning. Moreover, if integrated with a position indicator system and a numerically positionable drilling machine, the system could be employed for semi-automatic surgery
A Classification Algorithm for Electroencephalography Signals by Self-Induced Emotional Stimuli
Full text linkThe aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain-computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize the signals and to discriminate among different conditions. The proposed method is completely parameterized, aiming at a multiclass classification and it might be considered in the framework of machine learning. It is a two stages algorithm. The first stage is offline and it is devoted to the determination of a suitable set of features and to the training of a classifier. The second stage, the real-time one, is to test the proposed method on new data. In order to avoid redundancy in the set of features, the principal components analysis is adapted to the specific EEG signal characteristics and it is applied; the classification is performed through the support vector machine. Experimental tests on ten subjects, demonstrating the good performance of the algorithm in terms of both accuracy and efficiency, are also reported and discussed
A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People
No full textA Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel
A real time classification algorithm for EEG-based BCI driven by self-induced emotions
Full text linkBackground and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. Conclusions: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities
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
A poll oriented classifier for affective brain computer interfaces
No full textAffective Computing and Brain Computer Interface (BCI) are two innovative and rapidly growing fields of research. Affective Computing aims at equipping machines with the human capabilities of observe, understand and express affecting features; BCI aims at discovering novel communication channels and protocols, through the monitoring of the brain activity. Emotion recognition plays a central role in both these research fields. In this work we present an EEG poll based classification algorithm for self-induced emotional states used for BCI. We tested the approach using three emotions: The disgust produced by remembering an unpleasant odor (a stink), the pleasantness induced by the memory of a fragrance and a relaxing state. Preliminary experimental results are also reported
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
A virtual system for postural stability assessment based on a TOF camera and a mirror
No full textPostural stability is often compromised in many pathological states and decreases with age. In clinical practice, an objective tool for balance is fundamental. Recently, virtual tools, based on the use of depth cameras, have been presented. In this paper, a new virtual system for postural stability assessment was presented, involving the use of a Time of Flight camera (TOF) and of a mirror for the reduction of the occlusions errors by allowing the camera to see the hidden body surface. The validity of the tool was assessed through some experimental results. Data were also compared with those measured by a physical force platform and those calculated with another virtual stability assessment system, in order to highlight the error reduction while maintaining simplicity and low-cos
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