In the case of wideband (aperiodic) signals, the classical signal and noise measures used to characterize stochastic resonance do not work because their way of distinguishing signal from noise fails. In a study published earlier (L. B. Kish, 1996), a new way of measuring and identifying noise and aperiodic (wideband) signals during strongly nonlinear transfer was introduced. The method was based on using cross-spectra between the input and the output. According to the study, in the case of linear transfer and sinusoidal signals, the method gives the same results as the classical method and in the case of aperiodic signals it gives a sensible measure. In this paper we refine the theory and present detailed simulations which validate and refine the conclusions reached in that study. As neural and ion channel signal transfer are nonlinear and aperiodic, the new method has direct applicability in membrane biology and neural science (S.M. Bezrukov and I. Vodyanoy, 1997).

In the case of wideband (aperiodic) signals, the classical signal and noise measures used to characterize stochastic resonance do not work because their way of distinguishing signal from noise fails. In a study published earlier (L. B. Kish, 1996), a new way of measuring and identifying noise and aperiodic (wideband) signals during strongly nonlinear transfer was introduced. The method was based on using cross-spectra between the input and the output. According to the study, in the case of linear transfer and sinusoidal signals, the method gives the same results as the classical method and in the case of aperiodic signals it gives a sensible measure. In this paper we refine the theory and present detailed simulations which validate and refine the conclusions reached in that study. As neural and ion channel signal transfer are nonlinear and aperiodic, the new method has direct applicability in membrane biology and neural science (S.M. Bezrukov and I. Vodyanoy, 1997).

Main aim of our thesis is fatigue and stress detection from biological signals of a driver. Introduction contains information on published methods of detection and thoroughly informs readers about theoretical background necessary for our thesis. In the practical application we have firstly worked with a database of measured rides and subsequently chose their most relevant sections. Extraction and selection of features followed afterward. Five different classification models for tiredness and stress detection were used in the thesis and prediction was based on actual data. Lastly, the final section compares the best model of our thesis with the already published results.

Η λειτουργία του εγκεφάλου βασίζεται στο σύνολο της ηλεκτροχημικής δραστηριότητας των νευρώνων, που αποτελούν το δομικό λίθο του νευρικού συστήματος. Η καταγραφή των βιολογικών σημάτων, ή βιοσημάτων, τα οποία προκύπτουν από την ηλεκτρική δραστηριότητα των νευρώνων του εγκεφάλου, αποτελούν το αντικείμενο της Ηλεκτροεγκεφαλογραφίας, η οποία είναι μια μη επεμβατική μέθοδος καταγραφής του παραγόμενου, από τον εγκέφαλο, ηλεκτρικού πεδίου. Η πρώτη επιτυχημένη καταγραφή βιοδυναμικών του ανθρώπινου εγκεφάλου αποδίδεται στο Γερμανό φυσιολόγο Hans Berger, ο οποίος στα τέλη της δεκαετίας του 1930, πραγματοποίησε για πρώτη φορά τη μέτρηση διαφορών δυναμικού από την επιφάνεια του κεφαλιού. Σήμερα, το Ηλεκτροεγκεφαλογράφημα (ΗΕΓ) αποτελεί μια ευρέως διαδεδομένη τεχνική κλινικής εξέτασης που χρησιμοποιείται με κύριο σκοπό τη διάγνωση. Στην παρούσα εργασία περιγράφεται η λειτουργία του νευρικού συστήματος, ενώ παρατίθενται στοιχεία ανατομίας του εγκεφάλου. Ακόμα, αναλύεται η φύση και τα χαρακτηριστικά των βιοσημάτων, που λαμβάνονται από την εξωτερική δερματική επιφάνεια του κεφαλιού, ενώ παρουσιάζονται οι βασικές αρχές της Ηλεκτροεγκεφαλογραφίας, ο τρόπος καταγραφής και τα κυριότερα χαρακτηριστικά του σήματος του ΗΕΓ. Εν συνεχεία, περιγράφεται η καταγραφή Ηλεκτροεγκεφαλογραφήματος, που έγινε με τη χρήση του συστήματος MP150 της Biopac. Ο MP150, σε συνδυασμό με το γραφικό περιβάλλον του Acqknowledge 3.8.2 αποτελεί ένα πλήρες σύγχρονο σύστημα καταγραφής πολλών ειδών βιοδυναμικών από διαφορετικές περιοχές του ανθρώπινου σώματος. Αξιοποιώντας τις δυνατότητες του συστήματος αυτού, καταφέραμε να καταγράψουμε και να παρατηρήσουμε τη ρυθμική δραστηριότητα του εγκεφάλου. Επίσης, γίνεται αναφορά στα σημαντικότερα πακέτα εργαλείων του Matlab, που έχουν σαν αντικείμενο την ανάλυση του Ηλεκτροεγκεφαλογραφήματος, ενώ επιχειρείται η περαιτέρω επεξεργασία του σήματος που καταγράφηκε, με τη χρήση ενός από αυτά (eeglab). Επιπλέον, στην εργασία αυτή παρουσιάζονται τα κυριότερα χαρακτηριστικά κάποιων από τις σημαντικότερες μεθόδους ανάλυσης του σήματος που προκύπτει από το ηλεκτροεγκεφαλογράφημα, ενώ παράλληλα γίνεται μια προσπάθεια σύγκρισης των δυνατοτήτων τους, με σκοπό την εξαγωγή χρήσιμων συμπερασμάτων που μπορούν να αξιοποιηθούν σε επόμενες μελέτες. / The entire function of the human brain is based on the electrochemical activity of the neurons, which form the whole nervous system. The recording of the biological signals resulting from the electrical activity of the brain forms the content of Electroencephalography, which is a non invasive method of recording the electric field produced in the human brain. The first successful recording of human brain’s biodynamics was made by German physiologist Hans Berger, who was the first to measure the electrical potential difference on the human head. Nowadays, Electroencephalography (EEG) is a very common clinical examination technique, which is used for the purpose of diagnosis. One of the objects of the present thesis is the description of the nervous system function, while there are some basic elements of the human brain’s anatomy. There is a brief analysis of the nature and features of the biological signals produced by the brain, as well as a presentation of the basic principles of the Electroencephalography. In addition, there is a description of the recording of the EEG signal, which was made with the use of Biopac MP150 system. MP150 and its graphic interface Acqknowledge 3.8.2 form a complete modern data acquisition system, which offers the possibility to record biodynamics from different parts of the human body. Making use of the Biopac system, we were able to record and analyze the brain rhythmic activity. Moreover, the most common Matlab toolboxes that are used for EEG signal processing are presented, while a further analysis of our EEG signal was attempted through eeglab. Furthermore, in the present thesis, the basic features of some of the most important methods of EEG signal analysis are presented. This quite extended presentation is made in order to be used as information for future study and research on EEG signal analysis. Key words: nervous system, biological signals, Electroencephalography, MP150, Acqknowledge, Matlab toolboxes, eeglab, signal analysis

The main objectives of this thesis are to study theory of signal averaging, filtered residue method and methods of stretching/shrinking signal. It will also test the functionality of those methods. Thesis contains theoretical analysis, explanation of principles and testing of behaving of used methods.

This thesis deals with the problem with cluster analysis and biosignal classification options. The principle of cluster analysis, methods for calculating distances between objects and the standard process in the implementation of clustering are described in the first part. For biosignals processing,it is necessary to get familiar with the primary parameters of these signals in the following sections of thesis, process biosignals and methods for recording of action potentials described. Based on studying different clustering methods is presented a program with the applied method kmedoid in the next section of this thesis. The steps of this program are described in detail and in the end of thesis functionality is tested on a database of signals ÚBMI.