Objective: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities. Methods: Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6. ×. 6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects. Results: BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN). Conclusions: The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate). Significance: P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.

Au cours des deux dernières décennies, l'intérêt porté aux interfaces cérébrales ou Brain Computer Interfaces (BCI) s’est considérablement accru, avec un nombre croissant de laboratoires de recherche travaillant sur le sujet. Depuis le projet Brain Computer Interface, où la BCI a été présentée à des fins de réadaptation et d'assistance, l'utilisation de la BCI a été étendue à d'autres applications telles que le neurofeedback et l’industrie du jeux vidéo. Ce progrès a été réalisé grâce à une meilleure compréhension de l'électroencéphalographie (EEG), une amélioration des systèmes d’enregistrement du EEG, et une augmentation de puissance de calcul.Malgré son potentiel, la technologie de la BCI n’est pas encore mature et ne peut être utilisé en dehors des laboratoires. Il y a un tas de défis qui doivent être surmontés avant que les systèmes BCI puissent être utilisés à leur plein potentiel. Ce travail porte sur des aspects importants de ces défis, à savoir la spécificité des systèmes BCI aux capacités physiques des utilisateurs, la robustesse de la représentation et de l'apprentissage du EEG, ainsi que la suffisance des données d’entrainement. L'objectif est de fournir un système BCI qui peut s’adapter aux utilisateurs en fonction de leurs capacités physiques et des variabilités dans les signaux du cerveau enregistrés.À ces fins, deux voies principales sont explorées : la première, qui peut être considérée comme un ajustement de haut niveau, est un changement de paradigmes BCI. Elle porte sur la création de nouveaux paradigmes qui peuvent augmenter les performances de la BCI, alléger l'inconfort de l'utilisation de ces systèmes, et s’adapter aux besoins des utilisateurs. La deuxième voie, considérée comme une solution de bas niveau, porte sur l’amélioration des techniques de traitement du signal et d’apprentissage statistique pour améliorer la qualité du signal EEG, la reconnaissance des formes, ainsi que la tache de classification.D'une part, une nouvelle méthodologie dans le contexte de la robotique d'assistance est définie : il s’agit d’une approche hybride où une interface physique est complémentée par une interface cérébrale pour une interaction homme-machine plus fluide. Ce système hybride utilise les capacités motrices résiduelles des utilisateurs et offre la BCI comme un choix optionnel : l'utilisateur choisit quand utiliser la BCI et peut alterner entre les interfaces cérébrales et musculaire selon le besoin.D'autre part, pour l’amélioration des techniques de traitement du signal et d'apprentissage statistique, ce travail utilise un cadre Riemannien. Un frein majeur dans le domaine de la BCI est la faible résolution spatiale du EEG. Ce problème est dû à l'effet de conductance des os du crâne qui agissent comme un filtre passe-bas non linéaire, en mélangeant les signaux de différentes sources du cerveau et réduisant ainsi le rapport signal-à-bruit. Par conséquent, les méthodes de filtrage spatial ont été développées ou adaptées. La plupart d'entre elles – à savoir la Common Spatial Pattern (CSP), la xDAWN et la Canonical Correlation Analysis (CCA) – sont basées sur des estimations de matrice de covariance. Les matrices de covariance sont essentielles dans la représentation d’information contenue dans le signal EEG et constituent un élément important dans leur classification. Dans la plupart des algorithmes d'apprentissage statistique existants, les matrices de covariance sont traitées comme des éléments de l'espace euclidien. Cependant, étant symétrique et défini positive (SDP), les matrices de covariance sont situées dans un espace courbe qui est identifié comme une variété riemannienne. Utiliser les matrices de covariance comme caractéristique pour la classification des signaux EEG, et les manipuler avec les outils fournis par la géométrie de Riemann, fournit un cadre solide pour la représentation et l'apprentissage du EEG. / In the last two decades, interest in Brain-Computer Interfaces (BCI) has tremendously grown, with a number of research laboratories working on the topic. Since the Brain-Computer Interface Project of Vidal in 1973, where BCI was introduced for rehabilitative and assistive purposes, the use of BCI has been extended to more applications such as neurofeedback and entertainment. The credit of this progress should be granted to an improved understanding of electroencephalography (EEG), an improvement in its measurement techniques, and increased computational power.Despite the opportunities and potential of Brain-Computer Interface, the technology has yet to reach maturity and be used out of laboratories. There are several challenges that need to be addresses before BCI systems can be used to their full potential. This work examines in depth some of these challenges, namely the specificity of BCI systems to users physical abilities, the robustness of EEG representation and machine learning, and the adequacy of training data. The aim is to provide a BCI system that can adapt to individual users in terms of their physical abilities/disabilities, and variability in recorded brain signals.To this end, two main avenues are explored: the first, which can be regarded as a high-level adjustment, is a change in BCI paradigms. It is about creating new paradigms that increase their performance, ease the discomfort of using BCI systems, and adapt to the user’s needs. The second avenue, regarded as a low-level solution, is the refinement of signal processing and machine learning techniques to enhance the EEG signal quality, pattern recognition and classification.On the one hand, a new methodology in the context of assistive robotics is defined: it is a hybrid approach where a physical interface is complemented by a Brain-Computer Interface (BCI) for human machine interaction. This hybrid system makes use of users residual motor abilities and offers BCI as an optional choice: the user can choose when to rely on BCI and could alternate between the muscular- and brain-mediated interface at the appropriate time.On the other hand, for the refinement of signal processing and machine learning techniques, this work uses a Riemannian framework. A major limitation in this filed is the EEG poor spatial resolution. This limitation is due to the volume conductance effect, as the skull bones act as a non-linear low pass filter, mixing the brain source signals and thus reducing the signal-to-noise ratio. Consequently, spatial filtering methods have been developed or adapted. Most of them (i.e. Common Spatial Pattern, xDAWN, and Canonical Correlation Analysis) are based on covariance matrix estimations. The covariance matrices are key in the representation of information contained in the EEG signal and constitute an important feature in their classification. In most of the existing machine learning algorithms, covariance matrices are treated as elements of the Euclidean space. However, being Symmetric and Positive-Definite (SPD), covariance matrices lie on a curved space that is identified as a Riemannian manifold. Using covariance matrices as features for classification of EEG signals and handling them with the tools provided by Riemannian geometry provide a robust framework for EEG representation and learning.

<p dir="ltr">A Human-Machine Interaction (HMI) refers to a mechanism to support the direct interactions of humans and machines with the objective for the synthesis of machine intelligence and autonomy. The demand to advance in this field of study for intelligence controls is continuously growing. Brain-Computer Interface (BCI) is one type of HMIs that utilizes a human brain to enable direct communication of the human subject with a machine. This technology is widely explored in different fields to control external devices using brain signals.</p><p dir="ltr">This thesis is driven by two key observations. The first one is the limited number of Degrees of Freedom (DoF) that existing BCI controls can control in an external device; it becomes necessary to assess the controllability when choosing a control instrument. The second one is the differences of decision spaces of human and machine when both of them try to control an external device. To fill the gaps in these two aspects, there is a need to design an additional functional module that is able to translate the commands issued by human into high-frequency control commands that can be understood by machines. These two aspects has not been investigated thoroughly in literatures.</p><p dir="ltr">This study focuses on training, detecting, and using humans’ intents to control intelligent machines. It uses brain signals which will be trained and detected in form of Electroencephalography (EEG), brain signals will be used to extract and classify human intents. A selected instrument, Emotiv Epoc X, is used for pattern training and recognition based on its controllability and features among other instruments. A functional module is then developed to bridge the gap of frequency differences between human intents and motion commands of machine. A selected robot, TinkerKit Braccio, is then used to illustrate the feasibility of the developed module through fully controlling the robotic arm using human’s intents solely.</p><p dir="ltr">Multiple experiments were done on the prototyped system to prove the feasibility of the proposed model. The accuracy to send each command, and hence the accuracy of the system to extract each intent, exceeded 75%. Then, the feasibility of the proposed model was also tested through controlling the robot to follow pre-defined paths, which was obtained through designing a Graphical-User Interface (GUI). The accuracy of each experiment exceeded 90%, which validated the feasibility of the proposed control model.</p>

Introduction: Memory is a complex cognitive process formerly linked to mechanisms of brain plasticity that can be estimated in the left dorsolateral prefrontal cortex (DLPFC) using transcranial magnetic stimulation and electroencephalography (TMS-EEG). Also, cortical thickness in the DLPFC may be a potential proxy measure of brain plasticity as previous literature reports a link between better memory and thicker cortex. However, the link between brain plasticity and memory performance as well as DLPFC thickness remains to be clarified. Methods: Intermittent theta burst stimulation (iTBS) probed plasticity-like mechanisms in the left DLPFC in 17 cognitively healthy participants. TMS-EEG recordings were performed before and after sham and active iTBS to quantify plasticity via transcranial magnetic stimulation-evoked potentials (TEPs). Composite memory scores for each domain (verbal episodic, visual episodic and working memory) were obtained using the Cambridge Neuropsychological Test Automated Battery. Anatomical T1 images were acquired by magnetic resonance imaging and processed by open-source software (CIVET) and the Automated Anatomical Labeling atlas to extract cortical thickness of the DLPFC. All statistical analyses (linear mixed model, Tukey's post hoc test and Pearson's correlations) were completed in R Studio. Results: iTBS resulted in increased TEP amplitude P30 (F= 5.239, p = 0.029), as shown by a significant interaction between condition (iTBS, sham) and time (pre- and post-condition). Specifically, Tukey's post hoc test revealed that the P30 increase was near trending significant post-iTBS compared to pre-iTBS for the active condition (p = 0.166) but not for the sham condition (p = 0.294). A trending significant relationship was observed between the magnitude of P30 change post-iTBS and thicker left DLPFC (r = 0.488; p = 0.108). Lastly, no significant relationships between P30 change and memory performance were observed. Conclusion: These preliminary findings suggest there could be a relationship between increased capacity for brain plasticity and a thicker left DLPFC. To further investigate these relationships, we plan to recruit additional cognitively healthy participants. Our preliminary findings support the foundation for future clinical studies in which DLPFC thickness could be explored as a predictive factor for response to plasticity-targeting iTBS treatment.

L’objectif de cette thèse est l’étude du développement de l’attention auditive et des capacités de discrimination langagière chez l’enfant né prématurément ou à terme. Les derniers mois de grossesse sont particulièrement importants pour le développement cérébral de l’enfant et les conséquences d’une naissance prématurée sur le développement peuvent être considérables. Les enfants nés prématurément sont plus à risque de développer une variété de troubles neurodéveloppementaux que les enfants nés à terme. Même en l’absence de dommages cérébraux visibles, de nombreux enfants nés avant terme sont à risque de présenter des troubles tels que des retards langagiers ou des difficultés attentionnelles. Dans cette thèse, nous proposons donc une méthode d’investigation des processus préattentionnels auditifs et de discrimination langagière, à l’aide de l’électrophysiologie à haute densité et des potentiels évoqués auditifs (PEAs). Deux études ont été réalisées. La première visait à mettre sur pied un protocole d’évaluation de l’attention auditive et de la discrimination langagière chez l’enfant en santé, couvrant différents stades de développement (3 à 7 ans, 8 à 13 ans, adultes ; N = 40). Pour ce faire, nous avons analysé la composante de Mismatch Negativity (MMN) évoquée par la présentation de sons verbaux (syllabes /Ba/ et /Da/) et non verbaux (tons synthétisés, Ba : 1578 Hz/2800 Hz ; Da : 1788 Hz/2932 Hz). Les résultats ont révélé des patrons d’activation distincts en fonction de l’âge et du type de stimulus présenté. Chez tous les groupes d’âge, la présentation des stimuli non verbaux a évoqué une MMN de plus grande amplitude et de latence plus rapide que la présentation des stimuli verbaux. De plus, en réponse aux stimuli verbaux, les deux groupes d’enfants (3 à 7 ans, 8 à 13 ans) ont démontré une MMN de latence plus tardive que celle mesurée dans le groupe d’adultes. En revanche, en réponse aux stimuli non verbaux, seulement le groupe d’enfants de 3 à 7 ans a démontré une MMN de latence plus tardive que le groupe d’adulte. Les processus de discrimination verbaux semblent donc se développer plus tardivement dans l’enfance que les processus de discrimination non verbaux. Dans la deuxième étude, nous visions à d’identifier les marqueurs prédictifs de déficits attentionnels et langagiers pouvant découler d’une naissance prématurée à l’aide des PEAs et de la MMN. Nous avons utilisé le même protocole auprès de 74 enfants âgés de 3, 12 et 36 mois, nés prématurément (avant 34 semaines de gestation) ou nés à terme (au moins 37 semaines de gestation). Les résultats ont révélé que les enfants nés prématurément de tous les âges démontraient un délai significatif dans la latence de la réponse MMN et de la P150 par rapport aux enfants nés à terme lors de la présentation des sons verbaux. De plus, les latences plus tardives de la MMN et de la P150 étaient également corrélées à des performances langagières plus faibles lors d’une évaluation neurodéveloppementale. Toutefois, aucune différence n’a été observée entre les enfants nés à terme ou prématurément lors de la discrimination des stimuli non verbaux, suggérant des capacités préattentionnelles auditives préservées chez les enfants prématurés. Dans l’ensemble, les résultats de cette thèse indiquent que les processus préattentionnels auditifs se développent plus tôt dans l'enfance que ceux associés à la discrimination langagière. Les réseaux neuronaux impliqués dans la discrimination verbale sont encore immatures à la fin de l'enfance. De plus, ceux-ci semblent être particulièrement vulnérables aux impacts physiologiques liés à la prématurité. L’utilisation des PEAs et de la MMN en réponse aux stimuli verbaux en bas âge peut fournir des marqueurs prédictifs des difficultés langagières fréquemment observées chez l’enfant prématuré. / The aim of this thesis is to investigate early auditory attention and language development in full-term and preterm children. The last months of pregnancy are particularly important for the child’s cerebral development, and the impacts of a premature birth on his/her neurodevelopment can be substantial. Prematurely born children are at higher risk of developing a variety of neurodevelopmental disorders compared to full-terms. Even without visible brain injury, many premature children are at risk of presenting disorders such as language delays and attentional difficulties. In this thesis, we suggest an approach to investigate pre-attentional processes and early language discrimination abilities in infants using high-density electrophysiology and auditory event-related potentials (AERPs). We conducted two studies. The first one aimed at establishing a paradigm to evaluate auditory attention and language discrimination development in healthy full-term children, over different developmental stages (3 to 7 years, 8 to 13 years, adults; N = 40). To do so, we analyzed the Mismatch Negativity (MMN) component in response to speech (spoken syllables /Ba/ and /Da/) and non-speech stimuli (frequency-synthesized tones, Ba: 1578 Hz/2800 Hz; Da: 1788 Hz/2932 Hz). Distinct patterns of activation were revealed according to stimulus type and age. In all groups, non-speech stimuli elicited an MMN of larger amplitude and earlier latency than did the presentation of speech stimuli. Moreover, in response to speech stimuli, both children groups (3 to 7 years, 8 to 13 years) showed a significantly delayed MMN response compared to the adults group. In contrast, in response to non-speech stimuli, only the youngest group (3 to 7 years) showed a significantly delayed MMN compared to the adults. Age-related differences for tone discrimination therefore appear to occur earlier in children’s development than do the discriminative processes for speech sounds. In the second study, we aimed at identifying the electrophysiological markers of auditory attention and language deficits often incurred by a premature birth. We thus presented this paradigm to 74 infants born preterm (before 34 gestational weeks) or full-term (at least 37 gestational weeks), aged 3, 12 and 36 months old. Our results indicated that preterm children of all age groups showed a significantly delayed MMN and P150 responses to speech stimuli compared to full-terms. Moreover, significant correlations were found between the delayed MMN and P150 responses to speech sounds and lower language scores on a neurodevelopmental assessment. However, no significant differences were found between full-term and preterm children for the MMN in response to non-speech stimuli, suggesting preserved pre-attentional auditory discrimination abilities in these children. Altogether, the findings from this thesis indicate that the neurodevelopmental processes associated with auditory pre-attentional skills occur earlier in childhood compared to language discrimination processes. Cerebral networks involved in speech discrimination are still immature in late childhood. Furthermore, neural networks involved in speech discrimination and language development also appear to be particularly vulnerable to the impacts of prematurity. The use of AERPs and the MMN response to speech stimuli in infancy can thus provide predictive markers of language difficulties commonly seen in premature infants.

L'objectif de ces travaux de recherche est l'étude et le développement d'un système ICM embarqué en utilisant la méthodologie de conception conjointe afin de satisfaire ses contraintes spécifiques. Il en a découlé la constitution d'un système ICM complet intégrant un système d'acquisition OpenBCI et un système de traitement à base de FPGA. Ce système pourrait être utilisé dans des contextes variés : médicale (pour les diagnostiques précoces des pathologies), technologique (informatique ubiquitaire), industriel (communication avec des robots), ludique (contrôler un joystick dans les jeux vidéo), etc. Dans notre contexte d’étude, la plateforme ICM proposée a été réalisée pour assister les personnes à mobilité réduite à commander les équipements domestiques. Nous nous sommes intéressés en particulier à l'étude et à l'implémentation des modules de filtrage adaptatif et dynamique, sous forme d'un coprocesseur codé en HDL afin de réduire son temps d'exécution car c'est le bloc le plus critique de la chaine ICM. Quant aux algorithmes d'extraction des caractéristiques et de classification, ils sont exécutés par le processeur Nios-II sous son système d'exploitation en ANSI-C. Le temps de traitement d'un trial par notre système ICM réalisé est de l'ordre de 0.4 s/trial et sa consommation ne dépasse guère 0.7 W. / The main purpose of this thesis is to study and develop an embedded brain computer interface (BCI) system using HW/SW methodology in order to satisfy the system specifications. A complete BCI system integrated in an acquisition system (OpenBCI) and a hardware platform based on the FPGA were achieved. The proposed system can be used in a variety of contexts: medical (for early diagnosis of pathologies, assisting people with severe disabilities to control home devices system through thought), technological (ubiquitous computing), industrial (communication with Robots), games (control a joystick in video games), etc. In our study, the proposed ICM platform was designed to control home devices through the thought of people with severe disabilities. A particular attention has been given to the study and implementation of the filtering module, adaptive and dynamic filtering, in the form of a coprocessor coded in HDL in order to reduce its execution time as it is the critical block in the returned ICM algorithms. For the feature extraction and classification algorithms, they are executed in the Nios-II processor using ANSI-C language. The prototype operates at 200 MHz and performs a real time classification with an execution delay of 0.4 second per trial. The power consumption of the proposed system is about 0.7 W.

No description available.

In this dissertation the effects of variations in stimulus properties and CTOA, in auditory attention tasks were explored using recently developed approaches to EEG analysis including LIMO. The last experiment was structured using information theory, designing an effective experiment. Four studies were carried out using a number parity decision task, that employed different combinations of cueing Tone (T), Novel (N) and the Goal (G) stimuli. In the first EEG study, contrary to previous findings (Polich 2002, 2007) in control participants, no correlation between the time of a novel condition to the next novel condition and P300 amplitude was found. Therefore single trial across-subject averaging of participants’ data revealed significant correlations (r > .3) of stimulus properties (such as probability, frequency, amplitude and duration) on P300, and even r > .5 was found when N was an environmental sound in schizophrenic patients. In the second EEG study, simultaneously with fMRI recordings, the participants that showed significant behavioural distraction evoked brain activations and differences in both hemispheres (similar to Corbetta, 2002, 2008) while the participants, as a whole, produced significant activations mainly in left cortical and subcortical regions. A context analysis was run in distracted participants contrasting the trials immediately prior to the G trials, resulting in different prefrontal activations, which was consistent with studies of prefrontal control of visual attention (Koechlin 2003, 2007). In the third EEG study, the distractor noise type was manipulated (white vs environmental sounds) as well as presence or absence of scanner background noise in a blocked design. Results showed consistent P300, MMN and RON due to environmental noise. In addition, using time constants found in MEG results (Lu, Williamson & Kaufman, 1992) and adding the CTOA to the analysis, an information theory framework was calculated. After the simulation of the information of the experiment, a saddle indentation in the curve of the information measure based on the states of the incoming signal at around 300 ms CTOA was found. This saddle indentation was evident in more than 60 novel trials. In the fourth study, the CTOA and stimulus properties were manipulated in a parametric experiment. Based on the three studies, reducing complexity if the task (first study), using more than 60 stimuli in the novel conditions (third study). The CTOA randomly varying between 250 ms or 500 ms. Thirty-eight ANCOVA with 2 categorical and 1 continuous regressors were conducted and determined which time and channels elicited reliably signatures (p <.05) in the whole participants at short CTOA. Results revealed differences for the waveforms of current condition by depending on which condition appeared previously as well in terms of frequency and duration in scalp frontal electrodes (such as the second study). These results were interpreted as a consequence of switching between modes of attention and alerting states which resulted in the activation of frontal areas. Moreover, contextual analyses showed that systematic manipulation of stimulus properties allowed the visualization of the relationships between CTOA, executive function and orienting of attention.

Sleep is a basic need as important as physical fitness and good nutrition. Without enough sleep, we will create a sleep debt and experience sleepiness. Sleepiness can be defined as the inability to stay awake, a condition that has become a health problem in our 24-hour-7-day-a-week society. Estimates suggest that up to one-third of the population suffers from excessive sleepiness. Among other interactions, sleepiness affects our performance, increasing the risk of being involved in accidents. A considerable portion of work related accidents and injuries are related to sleepiness resulting in large costs for the individuals and society. Professional drivers are one example of workers who are at risk of sleepiness related accidents. Up to 40% of heavy truck accidents could be related to sleepiness. A better knowledge about reliable indicators and predictors of sleepiness is important in preventing sleepiness related accidents. This thesis investigates both objective and subjective indicators of sleepiness, how these relate to each other, and how their pattern changes over time. The indicators investigated were electroencephalography, heart rate variability, simple reaction time, head movement, and subjective ratings of sleepiness (Study I-IV). In Study V, a questionnaire study was conducted with professional drivers in northern Sweden. This study mainly deals with predictors of sleepiness. When subjects were sleep deprived both objective and subjective ratings indicated a rapid increase in sleepiness during the first hour of the test followed by a levelling off. This change in pattern was evident for all the indicators except heart rate and heart rate variability. On the other hand, HRV was correlated with the increase of EEG parameters during the post-test sleep period. The changes in pattern of the indicators included in the thesis are analysed in the perspective of temporal patterns and relationships. Of the tested indicators, a subjective rating of sleepiness with CR-10 was considered to be the most reliable indicator of sleepiness. Of the investigated predictors of sleepiness, prior sleep habits were found to be strongly associated to sleepiness and the sleepiness related symptoms while driving. The influences of driving conditions and individual characteristics on sleepiness while driving were lower. A multidisciplinary approach when investigating and implementing indicators and predictors of sleepiness is important. In addition to their actual relations to the development of sleepiness, factors such as technical and practical limitations, work, and individual and situational needs must be taken into account.

This dissertation explores the role of brainstem structures in the development and expression of generalized tonic-clonic seizures. The functional consequences of brainstem seizures are investigated using the kindling paradigm in order to understand the behavioral and cognitive effects of generalized seizures. <BR><BR> I begin by investigating the general characteristics of brainstem kindling. The first experiment demonstrates that certain brainstem sites are indeed susceptible to kindling and begins to delineate the features that distinguish brainstem seizures from those evoked at other brain regions. Further investigation of the EEG signal features using wavelet analysis reveals that changes in the spectral properties of the electrographic activity during kindling include significant changes to high-frequency activity and organized low-frequency activity. I also identify transitions that include frequency sweeps and abrupt seizure terminations. The changing spectral features are shown to be critically associated with the evolution of the kindled seizures and may have important functional consequences. The surprising responsiveness of some brainstem structures to kindling forces us to reconsider the overall role of these structures in epileptogenesis as well as in the healthy dynamical functioning of the brain. <BR><BR> In order to study the functional consequences, a series of experiments examines the changes in behavior, cognition and affect that follow these brainstem seizures. Although the results show no effects on spatial learning or memory, there are significant and complex effects on anxiety- and depression-like behavior that appear to be related to motivation. In order to further study the cognitive effects, a second set of behavioral experiments considers how context (i.e., the environment) interacts with the behavioral changes. The results indicate that changes in affect may only be apparent when choice between seizure-related and seizure-free contexts is given, suggesting that the environment and choice can play key roles in the behavioral consequences of seizures. This thesis also includes an appendix that applies synchrotron imaging to investigate the anatomical consequences of electrode implantation in kindling and shows that significantly increased iron depositions occur even with purportedly biocompatible electrodes widely used in research and clinical settings. <BR><BR> Examination of the role of brainstem structures in generalized seizures in this dissertation offers new perspectives and insights to epileptogenesis and the behavioral effects of epilepsy. The changes in EEG features, behavior, affect and motivation observed after brainstem seizures and kindling may have important clinical implications. For example, the results suggest a need to reexamine the concept of psychogenic seizures, a potential connection to Sudden Unexplained Death in Epilepsy (SUDEP), and the contribution of environmental factors. It is hoped that these findings will help elucidate the complex issues involved in understanding and improving the quality of life for people with epilepsy.