1,721,034 research outputs found
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
ISOLATION AND CHARACTERIZATION OF MUTANT RESISTANT TO AMINO ACID ANALOGUES OBTAINED FROM SACCHAROMYCES CEREVISIAE STRAINS
No full textIsolation and characterization of mutants resistant to amino acids analogues from Saccharomyces cerevisiae wine yeasts strains.
No full textGuidelines 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
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
The occurrence of high acetoin production in Saccharomyces cerevisiae wine yeasts as a possible process of "Genome renewal"
No full textGENOME RENEWAL: A NATURAL SELECTIVE MECHANISM BY PROVIDINGNEW PHENOTYPES IN SACCHAROMYCES CEREVISIAE WINE STRAINS
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Automatic framework for multiple sclerosis follow-up by magnetic resonance imaging for reducing contrast agents
No full textAn automatic framework for multiple sclerosis (MS) follow-up by Magnetic Resonance Imaging (MRI) is presented. It is based on the identification and segmentation of lesions by using convolutional neural network (CNN) architecture applied to the volumes collected by different imaging modalities and on the registration of the volumes obtained by two consecutive examinations. The resulting binary masks obtained from the identification/segmentation strategy on each examination are used to calculate the volume of each lesions, their status (chronic or active) and, hence, to estimate the progression of the disease. Preliminary results are reported demonstrating that the calculations performed by the proposed framework are capable, when the disease is stable, to gather the same information obtainable when the contrast agent (CA) is administered to the patient
Selezione e caratterizzazione di ceppi batterici per il miglioramento del processo di macerazione della canapa
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