The McGurk effect is an illusion that occurs when an auditory /ba/ is combined with a visual /ga/. The two stimuli fuse together which leads to the perception of /da/, a sound in between /ba/ and /ga/. The purpose of this study was to determine whether music performance and visual arts majors process mismatched auditory and visual stimuli, like the McGurk effect, differently. Nine syllable pairs were presented to 10 native English speakers (5 music performance majors and 5 visual arts majors between the ages of 18 and 28 years) in a four-forced-choice response paradigm. Data from event-related potentials were recorded for each participant. Results demonstrate that there are differences in the electrophysiological responses to viewing the mismatched syllable pairs. The /ga/ phoneme in the music performance group produced more differences while the /da/ phoneme produced more differences in the visual arts group. The McGurk effect is processed differently in the music performance majors and the visual arts majors; processing begins in the earliest latency epoch in the visual arts group but in the late latency epoch in the music performance group. These results imply that the music performance group has a more complex decoding system than the visual arts group. It also may suggest that the visual arts group is better able to integrate the visual and auditory information to resolve the conflict when mismatched signals are presented.

Numerous agencies have accumulated evidence since 1964 which implicates habitual cigarette smoking as a causal or facilitating factor in the development of many circulatory and respiratory diseases. This study sought to identify those psychological variables which possibly contribute to the maintenance of cigarette smoking and therefore, had two main purposes. First, this study investigated the individual and simultaneous physiological changes, i.e., Electroencephalography, Electromyography, Heart Rate, Blood Pressure and Skin Temperature that occurred during and immediately after the smoking of one cigarette. Second, the study investigated the hypothesis that smoking frequency would decrease when individuals were trained via biofeedback procedures to increase 8-12 Hz occipital EEG activity as a substitute for smoking. Three male, very heavy smokers (35 or more cigarettes per day ) and three male moderate smokers (15-24 cigarettes per day) physiologies were monitored while smoking, non-smoking and while they were provided with 8-12 Hz occipital EEG biofeedback training using a multiple baseline design. Results of the study indicate that of the six smokers physiologically monitored, four or more of the smokers demonstrated the following physiological changes while actually smoking one cigarette: the percent of time producing 4-8 cycles per second bra .in waves increased (S2 ,S3 ,S5); heart rate (beats per minute) increased (Sl,S2,S3,S4,S5,S6); and the percent of time producing 8-12 cycles per second (Hz) brain waves decreased (Sl,S2,S3,S4,S5,S6). Immediately after the smoking of one cigarette, four or more of the smokers demonstrated an increase in their rates (Sl,S2,S3,S4,S5, S6) and subjects 1,4,5 and 6 demonstrated an over-the-entire-session decrease in their skin temperatures. There did not appear to be any specific consistent brain wave changes across the subjects. However, the following subject-specific brain wave changes were evident: Subject 1 data indicates an increase in Alpha brain waves (8-12 Hz), a decrease in Theta brain waves (4-8 Hz), and a decrease in Beta brain waves (12-20 Hz). Subject 2 data indicates a decrease in Alpha brain waves, an increase in Theta brain waves, and a decrease in Beta waves. Subject 3 data indicates an Alpha wave decrease, Theta wave increase, and Beta wave increase. Subject 4 data indicates an Alpha wave decrease, Theta wave increase, and no observable change in Beta activity. Subject 5 data indicates an Alpha increase, a Theta decrease, and no observable change in Beta activity. Subject 6 data indicates an Alpha decrease, a non-observable change in Theta production and an increase in Beta activity. During the training period, when the smokers were given music feedback whenever they produced 8-12 Hz, four of the six smokers learned to increase the percent of time producing 8-12 Hz, (Sl,S2,S5, S6). Two of these four smokers were able to continue producing high levels of 8-12 Hz activity without the use of biofeedback equipment (Sl,S2). These smokers had quit smoking completely at the end of a six-month follow-up period. These two smokers were contacted by phone at the eight-month follow-up period and reported they were still absent from any cigarette smoking. The four smokers who could not increase their 8-12 Hz activity without the use of 8-12 Hz auditory feedback (Phase D) decreased their frequency of cigarette smoking at the six-month follow-up period as follows: Subject 3, from 38 to 15 cigarettes smoked per day; Subject 4, from 50 to 44 cigarettes smoker per day; Subject 5, from 18 to 8 cigarettes smoked per day; and Subject 6, from 17 to 10 cigarettes smoked per day. Possible reasons why Subjects 1 and 2 quit smoking are discussed and directions for future research are presented.

A key issue in understanding how the brain functions is the ability to correlate functional information with anatomical localisation. Functional information can be provided by a variety of techniques like positron emission tomography (PET), functional MRI (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) or transcranial magnetic stimulation (TMS). All these methods provide different, but complementary, information about the functional areas of the brain. PET and fMRI provide spatially accurate picture of brain regions involved in a given task. TMS permits to infer the contribution of the stimulated brain area to the task under investigation. EEG and MEG, which reflects brain activity directly, have temporal accuracy of the order of a millisecond. TMS, EEG and MEG are offset by their low spatial resolution. In this thesis, we propose two methods to improve the spatial accuracy of method based on TMS and EEG. The first part of this thesis presents an automatic method to improve the localisation of TMS points. The method enables real-time visualisation and registration of TMS evoked responses and MRI. A MF digitiser is used to sample approximately 200 points on the subject's head following a specific digitisation pattern. Registration is obtained by minimising the RMS point to surface distance, computed efficiently using the Euclidean distance transform. Functional maps are created from TMS evoked responses projected onto the brain surface previously segmented from MRI. The second part presents the possibilities to set up a brain-computer interface (BCI) based on reconstructed sources of EEG activity and the parameters to adjust. Reconstructed sources could improve the EEG spatial accuracy as well as add biophysical information on the origin of the signal. Both informations could improve the BCI classification step. Eight BCIs are built to enable comparison between electrode-based and reconstructed source-based BCIs. Tests on detection of laterality of upcoming hand movement demonstrate the interest of reconstructed sources.

Rett syndrome is a neurodevelopmental disorder that is predominately caused by mutations of the MECP2 gene. As neuronal apoptosis is not observed in RTT patients and MeCP2-deficient mice, the neurological deficits may be reversible. To address this, we reactivated MeCP2 expression ubiquitously in MeCP2-deficient mice after symptom onset. Our results showed that life span, behavioural performances, EEG activity, thermoregulation, and daily rhythmic activity were significantly improved after MeCP2 reactivation. Furthermore, the extent of improvement was dependent upon the efficiency of MeCP2 reactivation. To assess the role of the catecholaminergic system in Rett syndrome pathophysiology, we selectively preserved MeCP2 function within tyrosine hydroxylase expressing cells. We observed a significant improvement in the life span of male rescue mice and reduced sudden unexplained death rates in female rescue mice. Behavioural performances and EEG patterns were also significantly improved.

Rett syndrome is a neurodevelopmental disorder that is predominately caused by mutations of the MECP2 gene. As neuronal apoptosis is not observed in RTT patients and MeCP2-deficient mice, the neurological deficits may be reversible. To address this, we reactivated MeCP2 expression ubiquitously in MeCP2-deficient mice after symptom onset. Our results showed that life span, behavioural performances, EEG activity, thermoregulation, and daily rhythmic activity were significantly improved after MeCP2 reactivation. Furthermore, the extent of improvement was dependent upon the efficiency of MeCP2 reactivation. To assess the role of the catecholaminergic system in Rett syndrome pathophysiology, we selectively preserved MeCP2 function within tyrosine hydroxylase expressing cells. We observed a significant improvement in the life span of male rescue mice and reduced sudden unexplained death rates in female rescue mice. Behavioural performances and EEG patterns were also significantly improved.

Epilepsy accounts for 0.5% of the global burden of disease, and primary prevention of epilepsy represents one of the three 2007 NINDS Epilepsy Research Benchmarks. Efforts to understand and intervene in the process of epileptogenesis have yielded fruitful preventative strategies in animal models. This article reviews the current understanding of epileptogenesis, introduces the concept of a "critical period" for epileptogenesis, and examines strategies for epilepsy prevention in animal models of both acquired and genetic epilepsies. As proof of principle, we investigated whether early preventative treatment during epileptogenesis in the WAG/Rij rat model of primary generalized epilepsy would persistently suppress the epilepsy phenotype in adulthood. Oral ethosuximide was given from age p21 to 5 months, covering the established period for epileptogenesis in this model. We then assessed the epilepsy phenotype by performing electroencephpalogram (EEG) recordings at serial time points after treatment cessation and by immunocytochemically measuring the cortical expression of ion channels Nav1.1, Nav1.6, and HCN1, which are dysregulated in epileptic WAG/Rij rats. Treatment both persistently suppressed seizures, even up to 3 months after treatment cessation, and blocked ion channel dysregulation. These findings indicated that treatment during epileptogenesis prevented the development of the epileptic phenotype. Subsequently, we investigated the C3H/HeJ mouse model of genetic epilepsy as a candidate for future studies in preventative treatment during epileptogenesis. Serial EEG recordings were performed from p5 to 3 months of age. We found that C3H/HeJ mice underwent three distinct, stereotyped phases of seizure development, which suggests that this model would be an appropriate candidate for future research on prevention of epileptogenesis.

Thesis (Ph.D.)--University of Chicago, 2001. / Includes bibliographical references. Also available on the Internet.

The relationship between infants’ neural processing of and visual preferences for attractive and unattractive faces was investigated through the integration of event-related potential and preferential looking methods. Six-month-olds viewed color images of female faces previously rated by adults for attractiveness. The faces were presented in contrasting pairs of attractiveness (attractive/unattractive) for 1.5-second durations. The results showed that compared to attractive faces, unattractive faces elicited larger N290 amplitudes at left hemisphere electrode sites (PO9) and smaller P400 amplitudes at electrode sites across both hemispheres (PO9 and PO10). There were no significant differences between infants’ overall looking times based on attractiveness, however, a significant relationship was found between amplitude and trial looking time; larger N290 amplitudes were associated with longer trial looking times. The results suggest that compared to attractive faces, unattractive faces require greater cognitive resources and longer initial attention for visual processing. / text

In this thesis, we develop and test a technology for computer-assisted assessments of newborn brain maturity from sleep electroencephalogram (EEG). Brain maturation of newborns is reflected in rapid development of EEG patterns over a number of weeks after conception. Observing the maturational patterns, experts can assess newborn’s EEG maturity with an accuracy ±2 weeks of newborn’s stated age. A mismatch between the EEG patterns and newborn’s physiological age alerts clinicians about possible neurological problems. Analysis of newborn EEG requires specialised skills to recognise the maturity-related waveforms and patterns and interpret them in the context of newborns age and behavioural state. It is highly desirable to make the results of maturity assessment most accurate and reliable. However, the expert analysis is limited in capability to estimate the uncertainty in assessments. To enable experts quantitatively evaluate risks of brain dysmaturity for each case, we employ the Bayesian model averaging methodology. This methodology, in theory, provides the most accurate assessments along with the estimates of uncertainty, enabling experts to take into account the full information about the risk of decision making. Such information is particularly important when assessing the EEG signals which are highly variable and corrupted by artefacts. The use of decision tree models within the Bayesian averaging enables interpreting the results as a set of rules and finding the EEG features which make the most important contribution to assessments. The developed technology was tested on approximately 1,000 EEG recordings of newborns aged 36 to 45 weeks post conception, and the accuracy of assessments was comparable to that achieved by EEG experts. In addition, it was shown that the Bayesian assessment can be used to quantitatively evaluate the risk of brain dysmaturity for each EEG recording.

Η εξόρυξη χώρο-χρονικών δεδομένων αποτελεί πλέον μία από τις σημαντικότερες κατευθύνσεις του κλάδου της εξόρυξης γνώσης. Κάποια από τα βασικά προβλήματα που καλείται να αντιμετωπίσει είναι η ανακάλυψη περιοχών που εμφανίζουν ομοιότητες στην χρονική τους εξέλιξη, η αναγνώριση προτύπων που εμφανίζονται τόσο στην χωρική όσο και στη χρονική πληροφορία, η πρόβλεψη μελλοντικών τιμών και η αποθήκευση σε εξειδικευμένες βάσεις δεδομένων με σκοπό την αποδοτική απάντηση χωροχρονικών ερωτημάτων. Οι μέθοδοι που προσεγγίζουν τα παραπάνω προβλήματα καθώς και οι βασικές εργασίες της εξόρυξης γνώσης, όπως η κατηγοριοποίηση και η ομαδοποίηση, εμφανίζονται στον πυρήνα της πλειονότητας των εργαλείων ανάλυσης και επεξεργασίας χώρο-χρονικών δεδομένων. Βασικός στόχος της παρούσας εργασίας είναι η εφαρμογή μεθόδων εξόρυξης χώρο-χρονικών δεδομένων στο Ηλεκτροεγκεφαλογράφημα (ΗΕΓ), το οποίο αποτελεί μία από τις πιο διαδεδομένες τεχνικές ανάλυσης της εγκεφαλικής λειτουργίας. Τα δεδομένα που προκύπτουν από το ΗΕΓ περιέχουν τόσο χωρική όσο και χρονική πληροφορία καθώς αποτελούνται από ηλεκτρικά σήματα που προέρχονται από ηλεκτρόδια τοποθετημένα σε συγκεκριμένες θέσεις στο κρανίο. Τα βασικά προβλήματα που μελετήθηκαν στην επεξεργασία του ΗΕΓ είναι η μοντελοποίηση και η συσταδοποίηση χώρο-χρονικών δεδομένων, τα οποία οδήγησαν στην ανάπτυξη των αντίστοιχων μεθόδων. Στα πλαίσια της παρούσας εργασίας μελετήθηκε επίσης το πρόβλημα της διαχείρισης των δεδομένων ΗΕΓ και τη ανάλυσης ροών δεδομένων σε πραγματικό χρόνο. Η ενασχόληση με τα συγκεκριμένα προβλήματα οδήγησε α) στη δημιουργία καινοτόμων μεθόδων μοντελοποίησης και συσταδοποίησης χωρο-χρονικών δεδομένων, β) στον σχεδιασμό μιας βάσης δεδομένων, γ) στην μελέτη της βιβλιογραφίας στο θέμα της εξόρυξης και της διαχείρισης ροών δεδομένων και δ) στην δημιουργία μιας εφαρμογής για την ανάλυση δεδομένων σε πραγματικό χρόνο πάνω σε ένα σύστημα διαχείρισης ροών δεδομένων. Η παρούσα εργασία περιλαμβάνει ένα ένα σύνολο μεθόδων και εργαλείων ανάλυσης και διαχείρισης δεδομένων που εξετάστηκαν και χρησιμοποιήθηκαν προκειμένου να μελετηθεί η καταλληλότητά της εφαρμογής τους στις καταγραφές ΗΕΓ. Με τον τρόπο αυτό επιτυγχάνεται ο πρωταρχικός στόχος της εργασίας: η προώθηση υπαρχόντων και η δημιουργία καινοτόμων μεθόδων ανάλυσης από τον κλάδο της εξόρυξης γνώσης στα δεδομένα του ηλεκτροεγκεφαλογραφήματος. / Mining spatiotemporal data is one of the most significant topics in the field of data mining and knowledge discovery. Detecting locations that exhibit similarities in their temporal evolution, recognizing patterns that appear in both spatial and temporal information and storing spatiotemporal data in specialized databases are some of the fundamental problems tackled by researchers in this specific area. Methods and algorithms that address such problems along with the common data mining tasks (e.g. classification and clustering) are critical in the development of applications for analyzing spatiotemporal data, fact that highlights the necessity of continuous advancements of these algorithms in terms of usability, accuracy and performance. The most significant objective of the work performed during this thesis is the application of spatiotemporal data mining methods on the analysis of EEG, in order to exploit the both the spatial and the temporal nature of these data (i.e. electrodes placed on specific locations on the scalp that continuously record the electrical activity of the brain). Towards this direction the problems of modeling and clustering spatiotemporal data were extensively studied and the major outcome was the development of two corresponding methods. Furthermore, during this work the problem of managing EEG data was investigated both in the offline and the online scenario and within the latter, the state of the art in mining data streams was studied. The outcomes of this thesis related to the aforementioned problems include a) the development of a graph-based method for modeling spatiotemporal data, b) a method for clustering spatiotemporal data based on this model, c) the design of a database schema for storing eeg recording data and meta-data and d) the development of an application for online spindle detection over a data stream management system. Finally, this work aims towards the development of new and the adaptation of existing data mining methods in the context of spatiotemporal EEG analysis.