Algorithms and Methods for Imaging of Brain Activity from Non-Invasive Techniques

The imaging of brain activity, also called “Functional Neuroimaging”, is used to understand the relationship between activity in certain brain areas and specific functions. These techniques include fMRI (functional Magnetic Resonance Imaging), PET (Positron Emittance Tomography), EIT (Electrical Impedance Tomography), EEG (ElectroEncephaloGraphy) and DOT (Diffuse Optical Tomography) and are widely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In these contexts, usage of classical solutions (fMRI and PET) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons, portable low cost techniques are objects of the proposed thesis’s research, with focus on DOT and EEG. The main contribution of this thesis focuses on the implementation of a numerical solver for DOT based on the radiosity-diffusion model, integrating the anatomical information provided by a structural MRI.In particular, we obtained a 7x speed-up over an single run of isotropic-scattered parallel Monte Carlo engine for a domain of 2 million voxels, with an accuracy comparable to 10 runs of anisotropic scattered Monte Carlo in the same geometry. The speed-up significantly increases for larger domains, allowing one to compute the light distribution of a full human head (about 3 million voxels) in 116 seconds for the platform used. The secondary contribution of this thesis focuses on EEG and it concerns the implementation of software libraries for time-domain source localization in the scope of an open-source framework called Creamino, which can be used to simplify and speed-up the design of BCI systems. It consists of firmware and software libraries that allow designers to connect new EEG platforms to software tools for BCI

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.https://doi.org/10.3390/e1809027

Internet and Biometric Web Based Business Management Decision Support

Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION!!!

The full content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition

NOTIFICATION !!!

All the content of this special edition is retrieved from the conference proceedings published by the European Scientific Institute, ESI. http://eujournal.org/index.php/esj/pages/view/books The European Scientific Journal, ESJ, after approval from the publisher re publishes the papers in a Special edition