EEG biofeedback (neurotherapy) has been demonstrated as effective in the treatment of alcoholism, as evidenced by Peniston and Kulkosky's research efforts. These neurotherapy pioneers evaluated the efficacy of alpha-theta brain wave biofeedback as a treatment for chronic alcohol abuse, citing 80% abstinence rates as measured by improvements in psychopathology, serum beta endorphin levels, and long-term alcohol abstinence. Most research with alpha-theta EEG biofeedback has addressed alcohol addiction. Cocaine is now considered to be the most common drug problem of patients entering treatment for drug abuse. To date, only one controlled study has been published that researched alpha-theta neurofeedback in the treatment of "crack" cocaine addiction. The present study was an extension of a 4-year EEG-biofeedback treatment outcome project underway at a faith-based homeless mission in Houston, Texas, with male "crack" cocaine addicts. Changes in personality, attention, and impulsivity were measured following 30 sessions of a non-individualized EEG -biofeedback protocol. Experimental subjects received a variant of the Peniston-Kulkosky alpha-theta protocol for 30 sessions while controls received all elements of the experimental protocol except the EEG biofeedback. Assessment measures included the MMPI-2 and the IVA. Although experimental subjects showed greater mean improvement on most MMPI basic scales and all IVA Attention related measures, results indicated no significant differences between control and experimental groups. The present study did not result in significant differences between control and experimental groups on attentional or personality variables in crack cocaine addicts. Implications and limitations of the study are discussed.

Although the mu rhythm is traditionally defined as a normal EEG variant, recent evidence suggests that mu may have functional significance in a variety of disorders such as autism, Parkinson's disease, and multiple sclerosis. While an increasing number of articles have focused on the blocking mechanism of mu in relation to various cognitive processes and disorders, few have examined the significance of a prominent mu rhythm in the background EEG. A few studies have examined the relationship between the mu rhythm and psychological disturbance, such as attentional and affective disorders. Increasing evidence suggests that EEG and qEEG variables may be useful in classifying psychiatric disorders, presenting a neurophysiological alternative to traditional symptom-based diagnosis and classification. Thus, the intention of the present study was to examine the relationship between neuropsychological variables, gathered from multiple assessment sources, and the presence of a prominent mu rhythm in the EEG. Results did not show a statistically significant difference between individuals with and without a prominent mu rhythm on the Test of Variables of Attention (TOVA); although individuals in the mu group showed a pattern of increased impulsivity and performance decrement over time. For adults, no significant differences were observed between groups on psychological variables measured by the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). However, for children, the mu and control groups differed on several behavioral and emotional variables on the Child Behavior Checklist (CBCL). Results are examined in the context of other research and clinical implications are discussed.

O esquecimento intencional pode cumprir uma função estratégica no sistema cognitivo, que permite aos indivíduos não pensar sobre acontecimentos indesejados do passado, tais como eventos traumáticos, dolorosos e violentos, dos quais se prefere não recordar. Enquanto esquecer involuntariamente é uma falha da lembrança, por outro lado, esquecer intencionalmente parece ser uma função estratégica da memória. A presente dissertação teve por objetivo investigar os mecanismos neurocognitivos que contribuem para o esquecimento de memórias. A dissertação se organizou em dois estudos. O primeiro estudo consiste em uma revisão sistemática de artigos empíricos publicados nos últimos dez anos sobre a supressão de memórias indesejadas. O segundo estudo é um ensaio empírico, no qual foi executado um experimento adaptado do paradigma Think/No-Think com a utilização de marcadores eletrofisiológicos de eletroencefalograma. Participaram do experimento 22 sujeitos, alocados aleatoriamente em dois grupos com estratégias distintas de esquecimento: Supressão de memória e substituição de pensamentos. Durante toda a tarefa experimental, os participantes tiveram dados de EEG continuamente gravados. Os resultados decorrentes do ensaio empírico estão de acordo com os achados da literatura, indicando que a positividade parietal em torno de 400-800ms após a apresentação do estímulo é um marcador de lembrança consciente durante a recuperação de memória. Apenas na estratégia de supressão de memória houve uma redução da positividade centro-parietal durante o esquecimento, entre 450 e 700ms após apresentação do estímulo. Além disso, uma maior deflexão no componente N2 durante a supressão é um preditor de esquecimento induzido. Os achados indicam que é possível mapear o sistema neurocognitivo subjacente à supressão de memórias. / Intentional forgetting can be characterized as a strategic function of the cognitive system that allows us not to think about unwanted memories from our past, as for example emotional events or traumatic experiences that we would prefer not to remember. While forgetting involuntarily is a failure of recollection, on the other hand, forgetting intentionally seems to be a strategic function of memory. The aim of this dissertation was to investigate the neurocognitive mechanisms that contribute to forgetting memories. The dissertation was organized in two studies. The first study consists of a systematic review of empirical articles published in the last ten years on the suppression of unwanted memories. The second study is an empirical essay, in which an experiment adapted from the Think/No-Think paradigm was performed, with the use of electrophysiological markers of electroencephalogram. Twenty-two subjects participated in the experiment, randomly assigned to two groups with distinct strategies of forgetting: Memory suppression and thought substitution. Throughout the experimental task, participants had continuously recorded EEG data. The results of the empirical essay are in agreement with the literature findings, indicating that the parietal positivity around 400-800 ms after the presentation of the stimulus is a marker of conscious memory during memory recovery. Only direct memory suppression reduced centro-parietal positivity during forgetting, between 450 and 700 ms post-stimulus. Also, a greater deflection in the N2 component during suppression is an induced forgetting predictor. The findings indicate that it is possible to map the neurocognitive system underlying memory suppression.

In noisy and complex environments, human listeners must segregate the mixture of sound sources arriving at their ears and selectively attend a single source, thereby solving a computationally difficult problem called the cocktail party problem. However, the neural mechanisms underlying these computations are still largely a mystery. Oscillatory synchronization of neuronal activity between cortical areas is thought to provide a crucial role in facilitating information transmission between spatially separated populations of neurons, enabling the formation of functional networks. In this thesis, we seek to analyze and model the functional neuronal networks underlying attention to speech stimuli and find that the Frontal Eye Fields play a central 'hub' role in the auditory spatial attention network in a cocktail party experiment. We use magnetoencephalography (MEG) to measure neural signals with high temporal precision, while sampling from the whole cortex. However, several methodological issues arise when undertaking functional connectivity analysis with MEG data. Specifically, volume conduction of electrical and magnetic fields in the brain complicates interpretation of results. We compare several approaches through simulations, and analyze the trade-offs among various measures of neural phase-locking in the presence of volume conduction. We use these insights to study functional networks in a cocktail party experiment. We then construct a linear dynamical system model of neural responses to ongoing speech. Using this model, we are able to correctly predict which of two speakers is being attended by a listener. We then apply this model to data from a task where people were attending to stories with synchronous and scrambled videos of the speakers' faces to explore how the presence of visual information modifies the underlying neuronal mechanisms of speech perception. This model allows us to probe neural processes as subjects listen to long stimuli, without the need for a trial-based experimental design. We model the neural activity with latent states, and model the neural noise spectrum and functional connectivity with multivariate autoregressive dynamics, along with impulse responses for external stimulus processing. We also develop a new regularized Expectation-Maximization (EM) algorithm to fit this model to electroencephalography (EEG) data.

This thesis explores how predictions about upcoming linguistic stimuli are generated during real-time language comprehension in younger and older adults. Previous research has shown humans' ability to use rich contextual information to compute linguistic prediction during real-time language comprehension. Research in the modulating factors of prediction has shown, first, that predictions are informed by our experience with language and second, that these predictions are modulated by cognitive factors such as working memory and processing speed. However, little is known about how these factors interact in aging in which verbal intelligence remains stable or even increases, whereas processing speed, working memory, and inhibitory control decline with age. Experience-driven models of language learning argue that learning occurs across the life span instead of terminating once representations are learned well enough to approximate a stable state. In relation to aging, these models predict that older adults are likely to possess stronger learned associations, such that the predictions they generate during on-line processing may be stronger. At the same time, however, processing speed, working memory, and inhibitory control decline as a function of age, and age-related declines in these processes may reduce the degree to which older adults can predict. Here, I explored the interplay between language and cognitive factors in the generation of predictions and hypothesized that older adults will show stronger predictability effects than younger adults likely because of their language experience. In this thesis, I provide evidence from reading eye-movements, event-related potentials (ERPs), and EEG phase synchronization, for the role of language experience and cognitive decline in prediction in younger and older English speakers. I demonstrated that the eye-movement record is influenced by linguistic factors, which produce greater predictability effects as linguistic experience advances, and cognitive factors, which produce smaller predictability effects as they decline. Similarly, the N400, an ERP response that is modulated by a word's predictability, was also moderated by cognitive factors. Most importantly, older adults were able to use context efficiently to facilitate upcoming words in the ERP study, contrary to younger adults. Further, I provide initial evidence that coherence analysis may be used as a measure of cognitive effort to illustrate the facilitation that prediction confers to language comprehenders. The results indicate that for a comprehensive account of predictive processing research needs to take into account the role of experience acquired through lifetime and the declines that aging brings.

Syftet med uppsatsen är att ge en uppdaterad översikt av området BCI (Brain Computer Interface) och undersöka vad som hänt sedan begreppet introducerades i forskningssammanhang; vilka praktiska resultat forskningen lett till och vilka tillämpningar som tillkommit. Metoden som företrädesvis används är litteraturstudie som tecknar bakgrund och enkät. Därefter följer en diskussion där utmaningar för framtiden, potential och tillämpningar i BCI-tekniken behandlas utifrån ett neuropsykologiskt perspektiv. Kommer BCI-tekniken att implementeras på samma sätt som radio, TV och telekommunikationer i samhället och vilka etiska och tekniska problem finns idag. För att skildra allmänhetens uppfattning om BCI genomfördes en webbaserad enkätundersökning (survey) i form av pilotstudie (n=32) som syftar till att ge en indikation på attityder och hur allmänhetens opinion med avseende på tillämpningar i samtiden och jämförelser med avseende på teknisk bakgrund.

Event-related potentials play a significant role in error processing and attentional processes. Specifically, event-related negativity (ERN), feedback-related negativity (FRN), and the P300 are related to performance monitoring. The current study examined these components in relation to subjective probability, or confidence, regarding response accuracy on a complicated learning task. Results indicated that confidence ratings were not associated with any changes in ERN, FRN, or P300 amplitude. P300 amplitude did not vary according to participants’ subjective probabilities. ERN amplitude and FRN amplitude did not change throughout the task as participants learned. Future studies should consider the relationship between ERN and FRN using a learning task that is less difficult than the one employed in this study.

Driver sleepiness contributes to a large amount of all road traffic crashes. Developing an objective measurement of driver sleepiness in order to prevent eventual traffic accidents is desirable. The aim of this master thesis was to investigate if deep learning can be used to provide a driver sleepiness classification from brain activity signals obtained by electroencephalography (EEG). The intention was to study the classification performance when using different representations of the input data and to examine how various deep neural network architectures and class weighting during training affect the classification.  The data was collected from 12 experiments, where 269 participants (1187 driving sessions) were driving either on real roads or in a moving-base driving simulator, while electrophysiological data was recorded. Several deep neural network architectures were developed, depending on the representation of the input data.  Regardless of which data representation that was used as input to the network, the datawas divided into three datasets: Training 60%, validation 20% and test 20%. The data from each participant, with associated driving sessions, were randomly assigned to the different datasets according to the given percentage, which resulted in a subject-independent sleepiness detection. The output was in the form of continuous regression further rounded to the closest integer and divided into five classes according to Karolinska Sleepiness Scale (KSS = 1-5, 6, 7, 8, 9). The best performance was obtained with a convolutional neural network (CNN) combined with Long Short-Term Memory (LSTM) architecture, with time series data as input. This gave an accuracy of 41.44%, a mean absolute error of 0.94 and a macro F1-score of 0.37. Overall, the models with time series data showed better classification results compared to those with time-frequency data. Class weighting, giving all classes inverse proportional weight to their appearance, compensated slightly for class imbalance, but all networks had in general difficulties with generalizing to new data.

Movement disorders are a group of syndromes that often arise due to neurological abnormalities. Approximately 40 million Americans are affected by some form of movement disorder, significantly impacting patients’ quality of life and their ability to live independently. Deep brain stimulation (DBS) is one treatment that has shown promising results in the past couple decades, however, the currently used open-loop system has several drawbacks. By implementing a closed-loop or adaptive DBS (aDBS) system, the need for expensive parameter reprogramming sessions would be reduced, side-effects may be relieved, and habituation could be avoided. Several biomarkers, for example signals or activity derived from electroencephalogram (EEG), could potentially be used as a feedback source for aDBS. Here, we attempted to characterize cortical EEG potentials in healthy subjects performing six tasks that are difficult for those with movement disorders. Using a 32-channel EEG cap with an amplifier sampling at 500 Hz, we performed our protocol on 11 college-aged volunteers lacking any known movement disorder. For each task, we analyzed task-related power (TRP) changes, spectrograms, and topographical maps. In a finger movement exercise, we found task-related depression (TRD) in the delta band at the F4 electrode, as well as TRD at the C3 electrode in the alpha band during a pencil-pickup task, and TRD at the F3 electrode in the beta band during voluntary swallowing. While delta-ERD in the finger movement exercise was likely due to ocular artifact, the other significant results were in line with what relevant literature would predict. The findings from the work, in conjunction with a future study involving movement disorder patients, can provide insight into the use of EEG as a feedback source for aDBS. Keywords: EEG, electroencephalography, neurostimulation, deep brain stimulation, movement disorders, closed-loop DBS, adaptive DBS, aDBS

This master’s thesis deals with diagnostics of prodromal stage of Lewy body disease using microstate analysis. First part of the thesis includes theoretical background which is needed for understanding discussed topics and presented results. This part consists of description of the disease, diagnostic options, electroencephalography, pre-processing of the EEG record and the microstate analysis process. Theoretical background is followed by a practical part of the thesis. In the beginning, there is a chapter about a dataset, used EEG device, and own solution of the pre-processing. Microstate analysis is discussed next, its output parameters were compared between groups with statistical methods. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections. Comparison of the subjects in prodromal stage of Lewy body disease and healthy controls brought significant differences in three parameters of microstates, in rate of unlabelled time frames and also for some counts of transitions between each map or unlabelled sections.