A cinc rates se’ls va realitzar una polisomnografia convencional durant 48 hores. Els EEG dels animals es van agrupar d'acord amb l'hemisferi preferit envers el no preferit. Una vegada analitzat els EEG es va trobar una diferència significativa entre els resultats de llum / foscor. Els canvis en la dominància hemisfèrica es troben en el període de son NREM, a la banda delta; en les bandes theta i beta durant el son REM; durant la vigília en les bandes alfa-1, alfa-2, i theta. Els canvis han estat interpretats com una resposta a les variacions dels reptes mediambientals. Els pacients amb apnea del son també mostren variacions temporals en la dominància interhemisfèrica. La magnitud de la coherència quadrada, i l’índex d'interdependència interhemisfèrica de fase, troben un augment de l'amplitud de la fase delta de l’EEG durant els episodis d'apnea, mentre que l'índex de retard de fase, es redueix a zero. L'índex de L, que mesura la sincronització interhemisfèrica generalitzada no lineal de l'EEG, augmenta durant els episodis d'apnea. Per tant, els tres índexs mostren canvis significatius i congruents en la simetria interhemisfèrica en funció de l'estat de les vies respiratòries. En conclusió, els pacients apneics durant la respiració tenen un canvi petit però significatiu en el domini interhemisfèric.

Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable time to administer medications and other such anti-seizure countermeasures to help reduce the damaging effects of this crippling disorder. The time-varying dynamics and high inter-individual variability make early prediction of a seizure state a challenging task. Many studies have shown that EEG signals do have valuable information that, if correctly analyzed, could help in the prediction of seizures in epileptic patients before their occurrence. Several mathematical transforms have been analyzed for its correlation with seizure onset prediction and a series of experiments were done to certify their strengths. New algorithms are presented to help clarify, monitor, and cross-validate the classification of EEG signals to predict the ictal (i.e. seizure) states, specifically the preictal, interictal, and postictal states in the brain. These new methods show promising results in detecting the presence of a preictal phase prior to the ictal state.

A brain-computer interface, BCI, is a technical system that allows a person to control the external world without relying on muscle activity. This thesis presents an EEG based BCI designed for automatic classification of two dimensional hand movements. The long-term goal of the project is to build an intuitive communication system for operation by people with severe motor impairments. If successful, such system could for example be used by a paralyzed patient to control a word processor or a wheelchair. The developed BCI was tested in an offine pilot study. In response to an external cue, a test subject moved a joystick in one of four directions. During the movement, EEG was recorded from seven electrodes mounted on the subject's scalp. An autoregressive model was fitted to the data, and the extracted coefficients were used as input features to a neural network based classifier. The classifier was trained to recognize the direction of the movements. During the first half of the experiment, real physical movements were performed. In the second half, subjects were instructed just to imagine the hand moving the joystick, but to avoid any muscle activity. The results of the experiment indicate that the EEG signals do in fact contain extractable and classifiable information about the performed movements, during both physical and imagined movements.

Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable time to administer medications and other such anti-seizure countermeasures to help reduce the damaging effects of this crippling disorder. The time-varying dynamics and high inter-individual variability make early prediction of a seizure state a challenging task. Many studies have shown that EEG signals do have valuable information that, if correctly analyzed, could help in the prediction of seizures in epileptic patients before their occurrence. Several mathematical transforms have been analyzed for its correlation with seizure onset prediction and a series of experiments were done to certify their strengths. New algorithms are presented to help clarify, monitor, and cross-validate the classification of EEG signals to predict the ictal (i.e. seizure) states, specifically the preictal, interictal, and postictal states in the brain. These new methods show promising results in detecting the presence of a preictal phase prior to the ictal state.

Visual attention has long been described in terms of the spotlight metaphor, which assumes that two-dimensional regions of the visual field are selectively processed. However, evidence suggests that attention can be distributed to depth in addition to two-dimensional space (Andersen and Kramer, 1993; Gawryszewski, Riggio, Rizzolatti, and Umiltà, 1987). Research supporting this idea has induced depth through binocular disparity. Thus, the results of previous research may be specific to stereoscopic stimuli and not apply generally to the perception of depth. Three experiments were conducted in order to determine if visual attention could be distributed to a non-stereoscopic apparent depth. In these experiments, the perceptual experience of depth was induced in a visual scene using only pictorial depth cues. Subjects were required to attend either a near or far depth in this scene. Experiments 1 and 2 employed electrophysiological recordings and found a reliable modulation in the amplitude of the attention sensitive visual component, P1, when subjects directed attention to far depths. Behavioral measurements in Experiment 3 supported this result, finding speeded reaction time to attended far depth stimuli. No P1 modulation or reaction time facilitation was found when the pictorial depth cues of the visual scene were attenuated. These results suggest that visual attention may be distributed to pictorial depth and are further consistent with a viewer-centered asymmetry in attending to depth.

One of the goals of Electroencephalography (EEG) is to correctly localize brain activities by the help of voltage measurements taken on scalp. However, due to computational difficulties of the problem and technological limitations, the accuracy level of the activity localization is not perfect and should be improved. To increase accuracy level of the solution, realistic, i.e. patient dependent, head models should be created. Such head models are created via assigning realistic conductivity values of head tissues onto realistic tissue positions. This study initially focuses on obtaining patient dependent spatial information from T1-weighted Magnetic Resonance (MR) head images. Existing segmentation algorithms are modified according to our needs for classifying eye tissues, white matter, gray matter, cerebrospinal fluid, skull and scalp from volumetric MR head images. Determination of patient dependent conductivity values, on the other hand, is not considered as a part of this study, and isotropic conductivity values anticipated in literature are assigned to each segmented MR-voxel accordingly. Upon completion of the tissue classification, forward problem of EEG is solved using the Finite Difference (FD) method employing a realistic head model. Utilization of the FD method aims to lower computational complexity and to simplify the process of mesh creation for brain, which has a very complex boundary. Accuracy of the employed numerical method is investigated both on Electrical Impedance Tomography (EIT) and EEG forward problems, for which analytical solutions are available. The purpose of EIT forward problem integration into this study is to evaluate reciprocal solution of the EEG forward problem.

In the human brain, electrical activities are created due to the body functions. These electrical activities create potentials and magnetic fields which can be monitored elec- trically (Electroencephalography - EEG) or magnetically (Magnetoencephalography - MEG). Electrical activities in human brain are usually modeled by electrical dipoles. The purpose of Electro-magnetic source imaging (EMSI) is to determine the position, orientation and strength of dipoles. The first stage of EMSI is to model the human head numerically. In this study, The Finite Element Method (FEM) is chosen to han- dle anisotropy in the brain. The second stage of EMSI is to solve the potentials and magnetic fields for an assumed dipole configuration (forward problem). Realistic con- ductivity distribution of human head is required for more accurate forward problem solutions. However, to our knowledge, conductivity distribution for an individual has not been computed yet. The aim of this thesis study is to investigate the feasibility of a new approach to update the initially assumed conductivity distribution by using the evoked potentials and fields acquired during EMSI studies. This will increase the success of source localization problem, since more realistic conductivity distribution of the head will be used in the forward problem. This new method can also be used as a new imaging modality, especially for inhomogeneities where the conductivity value deviates. In this thesis study, to investigate the sensitivity of measurements to conductivity perturbations, a FEM based sensitivity matrix approach is used. The performance of the proposed method is tested using three different head models - homogeneous spherical, 4 layer concentric sphere and realistic head model. For spherical head models rectangular grids are preferred in the middle and curved elements are used nearby the head boundary. For realistic cases, head models are developed using uniform grids. Tissue boundary information is obtained by applying segmentation algorithms to the Magnetic Resonance (MR) images. A paralel computer cluster is employed to assess the feasibility of this new approach. PETSc library is used for forward problem calculations and linear system solutions. The performance of this novel approach depends on many factors such as the head model, number of dipoles and sensors used in the calculation, noise in the measure- ments, etc. In this thesis study, a number of simulations are performed to investigate the effects of each of these parameters. Increase in the number of elements in the head model leads to the increase in the number of unknows for linear system solu- tions. Then, accuracy of the solution is improved with increased number of dipoles or sensors. The performance of the adopted approach is investigated using noise-free measurements as well as noisy measurements. For EEG, measurement noise decreases the accuracy of the approach. For MEG, the effect of measurement noise is more pronounced and may lead to a larger error in tissue conductivity calculation.

Epilepsy is a neurological disorder characterized by recurrent seizures. Sleep problems can cooccur with epilepsy, and adversely affect seizure diagnosis and treatment. In fact, the relationship between sleep and seizures in individuals with epilepsy is a complex one. Seizures disturb sleep and sleep deprivation aggravates seizures. Antiepileptic drugs may also impair sleep quality at the cost of controlling seizures. In general, particular vigilance states may inhibit or facilitate seizure generation, and changes in vigilance state can affect the predictability of seizures. A clear understanding of sleep-seizure interactions will therefore benefit epilepsy care providers and improve quality of life in patients. Notable progress in neuroscience research—and particularly sleep and epilepsy—has been achieved through experimentation on animals. Experimental models of epilepsy provide us with the opportunity to explore or even manipulate the sleep-seizure relationship in order to decipher different aspects of their interactions. Important in this process is the development of techniques for modeling and tracking sleep dynamics using electrophysiological measurements. In this dissertation experimental and computational approaches are proposed for modeling vigilance dynamics and their utility demonstrated in nonepileptic control mice. The general framework of hidden Markov models is used to automatically model and track sleep state and dynamics from electrophysiological as well as novel motion measurements. In addition, a closed-loop sensory stimulation technique is proposed that, in conjunction with this model, provides the means to concurrently track and modulate 3 vigilance dynamics in animals. The feasibility of the proposed techniques for modeling and altering sleep are demonstrated for experimental applications related to epilepsy. Finally, preliminary data from a mouse model of temporal lobe epilepsy are employed to suggest applications of these techniques and directions for future research. The methodologies developed here have clear implications the design of intelligent neuromodulation strategies for clinical epilepsy therapy.

Language is an essential component of human behavior. It is ubiquitous, but more importantly, it is malleable and it is constantly changing. Part of the dynamic nature of informal communication is the introduction and adoption of new linguistic elements. Online communication provides a window into this informal public discourse; therefore, it may be useful for testing hypotheses about the processes underlying the acquisition and use of new words. The comprehension of informal language may lead to an understanding of how these new informal words are integrated into our mental lexicon. The current study was an electroencephalographic (EEG) investigation of the brain processes that underlie informal language. We recorded event-related potentials while participants engaged in a lexical decision task. For this experiment, participants made judgments about Twitter targets primed with semantically related or unrelated words. Classic psycholinguistic studies have shown very specific event-related potentials (ERPs) for semantic processing. Most notably, the N400 event-related potential component is an index of lexical expectancy and semantic relatedness. In contrast to the literature, we did not find classic N400 priming effects. However, our results revealed marked differences between informal and traditional targets. Our results suggest that informal language is more difficult to process than traditional language.

Η ηλεκτροεγκεφαλογραφία (EEG) και η μαγνητοεγκεφαλογραφία (MEG) είναι ιδιαίτερα χρήσιμες μέθοδοι εγκεφαλικών απεικονίσεων διότι έχουν πολύ καλή χρονική ανάλυση, της τάξεως του sec. Επειδή οι εγκεφαλικές διεργασίες εκτελούνται με μικρότερους ρυθμούς, οι εγκεφαλικές απεικονίσεις μέσω της EEG και MEG δίνουν τη δυνατότητα να παρακολουθούμε το λειτουργικό εγκέφαλο. Στο ευθύ πρόβλημα EEG γνωρίζουμε τη νευρωνική διέγερση που αναπτύσσεται εσωτερικά του εγκεφάλου και υπολογίζουμε το παραγόμενο ηλεκτρικό δυναμικό σε κάθε σημείο στο εξωτερικό ή στο εσωτερικό του εγκεφάλου. Αντίστοιχα στο ευθύ πρόβλημα MEG υπολογίζουμε το μαγνητικό δυναμικό εξωτερικά του εγκεφάλου. Στο αντίστροφο πρόβλημα της EEG γνωρίζουμε το ηλεκτρικό δυναμικό, από μετρήσεις, εξωτερικά του εγκεφάλου, στην επιφάνεια του κρανίου, και ζητάμε να προσδιορίσουμε τη νευρωνική διέγερση που το προκάλεσε. Για τη MEG γνωρίζουμε το μαγνητικό δυναμικό που καταγράφεται εξωτερικά του κρανίου και ζητάμε τη νευρωνική διέγερση που αναπτύχθηκε εσωτερικά του εγκεφάλου. Στην παρούσα εργασία επιλύονται οκτώ προβλήματα. Το ευθύ πρόβλημα και το αντίστροφο πρόβλημα της EEG και της MEG σε δύο περιπτώσεις. Στην πρώτη περίπτωση η νευρωνική διέγερση εντοπίζεται σε ένα μικρό ευθύγραμμο τμήμα, δηλαδή το ρεύμα που δημιουργήθηκε εσωτερικά του εγκεφάλου λόγω μιας εγκεφαλικής διεργασίας αναπαρίσταται με δίπολα που κατανέμονται κατά μήκος ενός μικρού ευθύγραμμου τμήματος. Επιλύουμε αρχικά το ευθύ πρόβλημα της EEG και στη συνέχεια το αντίστροφο πρόβλημα. Καταλήγουμε σε ένα μη γραμμικό σύστημα που, στη γενική περίπτωση, επιλύεται αριθμητικά για να υπολογίσει τη θέση και τη ροπή της διπολικής πηγής, τον προσανατολισμό και το μήκος του ευθύγραμμου τμήματος. Αναλυτικά επιλύουμε δύο ειδικές περιπτώσεις και βρίσκουμε μοναδική λύση. Στην πρώτη ειδική περίπτωση το ευθύγραμμο τμήμα είναι παράλληλο στον άξονα, ενώ στη δεύτερη στο άξονα και το κέντρο του, και στις δύο περιπτώσεις, είναι πάνω στο άξονα . Το ευθύ και το αντίστροφο πρόβλημα το επιλύουμε και για την περίπτωση της MEG δίνοντας αναλυτικά τη λύση στην περίπτωση που το ευθύγραμμο τμήμα είναι παράλληλο στο άξονα και το κέντρο του είναι επάνω στο άξονα. Στη δεύτερη περίπτωση μελετάμε ακριβώς τα ίδια προβλήματα όταν η νευρωνική διέγερση εντοπίζεται σε ένα μικρό κυκλικό δίσκο που το επίπεδό του είναι κάθετο στο διάνυσμα θέσης του κέντρου του δίσκου. Επιλύουμε το πρόβλημα σε συγκριμένη θέση του δίσκου για να απλοποιηθούν οι υπολογισμοί. Εφαρμόζουμε κατάλληλες στροφές Euler ώστε το επίπεδό του να βρεθεί σε θέση παράλληλη στο επίπεδο και το κέντρο του δίσκου να βρίσκεται πάνω στον άξονα. Στη συνέχεια εφαρμόζουμε στροφές Euler και το επαναφέρουμε στην αρχική θέση. Αφού επιλύσουμε τα ευθύ προβλήματα της EEG και MEG ξεχωριστά, προσδιορίζουμε, επιλύοντας το αντίστροφο, τη θέση του κυκλικού δίσκου. Καταλήγουμε σε ένα σύστημα μη γραμμικό που απαιτεί αριθμητική επίλυση τόσο για την EEG όσο και για τη MEG Αναλυτικά, επιλύουμε το αντίστροφο για την EEG σε μια ειδική περίπτωση κατά την οποία ο δίσκος είναι παράλληλος στο επίπεδο και το κέντρο του βρίσκεται πάνω στον άξονα και βρίσκουμε τη μοναδική λύση η οποία προσδιορίζει τη θέση του δίσκου, την ακτίνα του και τη ροπή της διπολικής πηγής. Με την επίλυση των προβλημάτων αυτών επιβεβαιώνουμε και τα αποτελέσματα της εργασίας των Albanese και Monk. Συγκεκριμένα, έδειξαν ότι δε μπορεί να προσδιοριστεί ο φορέας του ρεύματος που εντοπίζεται σε χώρο τριών διαστάσεων. Στην παρούσα διατριβή καθορίζουμε την έκτασης της νευρωνικής διέγερσης όταν η διάσταση του φορέα της είναι μικρότερη του τρία. / Electroencephalography (EEG) and Magnetoencephalography (MEG) are the two brain imaging modalities which have the necessary temporal resolution, sec for the study of the functional brain. Albanese and Monk have demonstrated that it is impossible to identify the extent of a localized three-dimensional current distribution lying inside a three-dimensional conductive medium. The purpose of the present work is to show that, as already predicted by Albanese and Monk, this result is not true if the current distribution is restricted on a one or two- dimensional set. The calculation of the values of the electric potential on the surface of the head defines the forward problem of EEG, while the calculation of the magnetic flux density a few centimeters outside the head defines the forward problem of MEG. The inverse EEG problem seeks to identify the neuronal current within the brain from the knowledge of the electric potential on the surface of the head. The corresponding inverse MEG problem seeks this neuronal current from the knowledge of the magnetic flux outside the head. In the present dissertation we study eight particular problems. They concern the forward and the inverse problem of EEG and MEG in two special geometric cases. In one geometrical case the neuronal current is supported on a small line segment and the neuronal current is represented by a dipole distribution along this line segment. First we solve the forward EEG problem and then we solve the inverse problem of identifying the location, the orientation, the size and the average dipolar moment over the line segment. We arrive at a nonlinear algebraic system which we solve analytically in two special cases. Next we solve the corresponding forward and the inverse MEG problems for the same structure. A second case concerns the relative EEG and MEG problems when the current is supported on a small disc normal to a radius of the conducting sphere. As before, we solve the EEG and MEG problems separately and then we solve the inverse EEG and MEG problems which determine the position, the orientation and the size of the disk supporting the primary neuronal current.