Thesis (M.S.)--University of Wisconsin--Madison, 1966. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1970. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Event-related potentials (ERPs) were evoked by visually presented words in a number of experimental paradigms. The question of which linguistic factors, if any, underlie differences between visual word ERPs was addressed. These studies identified 3 factors as predictors of ERF variance. Studies of ERPs in language processing tasks are selectively reviewed, and methodological problems associated with ERPs evoked by non-identical stimuli are discussed. The importance of an understanding of the linguistic factors which underlie ERP differences is outlined, and a methodology for approaching this issue is set out. The statistical procedure necessary to address the question is developed and described in Chapter Two. This procedure was a quantitative modelling strategy, based on multidimensional scaling and PROCRUSTES rotation. Five quantitative modelling studies were undertaken. These experiments involved two experimental tasks, a passive exposure task in which the subjects attended but did not respond to the stimuli (experiment 1) and a category membership decision task (experiments 2 to 5). Words drawn from two semantic categories were employed. ERPs were evoked by individual members of the category of colour names (experiments 1 to 3) and by members of the category of furniture terms (experiments 4 and 5). The results of these studies suggested that word length was the important factor in the early part of the post-stimulus epoch and that this factor was followed by semantic similarity. A late positivity was present in the decision task ERPs whose modulation was related to word frequency. These results were validated by two conventionally analysed experiments which examined the relation between word length and repetition and that between word frequency and repetition. It is concluded that three factors underlie ERP variance in the experimental paradigms employed. These factors are word length (physical extent was not dissociated from length in letters), word frequency and semantic similarity. These results may inform issues of experimental control in future studies of ERPs and language processing, may suggest some reassessment of existing studies in which control was not effected for these factors and may have provided a method of wider utility in cognitive neuroscience. The results suggest that systematic information can be derived about the linguistic characteristics of individual words from single word ERPs.

The effects of lithium on auditory evoked potential and electroencephalogram spectral edges are examined. The nature of the relationship between spectral edge and serum lithium concentration is investigated, with the goal of establishing a unique and functional relationship, thus enabling the spectral edge to be used as an index, or surrogate measurement, of serum lithium concentration.

No description available.

Investigators have shown that monitoring the electrical activity of the brain can indicate CNS status and that it can enhance the assessment of anesthetic depth when used with other clinical signs. It is important to understand the variables that are produced by EEG monitors and used to assess CNS status and to understand similarities and differences between stages of intra-operative sleep. This investigation used well studied stages of normal sleep for comparison with different stages of intra-operativesleep. EEG data from 6 intra-operative from 6 intra-operative and 6 normal sleep subjects were collected on FM recorder and processed with 2 clinical EEG monitors. The results failed to show any similarities in EEG variables between stages of normal sleep and intra-operative sleep. Comparison of the two monitors in assessing similar EEG waveforms showed that they have different sensitivities to frequency and amplitude and they produce different results with differences in their ability to separate information.

The fractal dimension of the phase space representation of the EEG of 8 clinical patients undergoing general anesthesia is determined. An attempt is made to correlate the trends in EEG dimensional complexity with the depth of anesthesia. While the EEG showed marked changes in dimensional complexity when passing through anesthetic stages, a uniform depth-dimension trend consistent with the eight patients was not observed. The only significant trend observed was a distinct change in dimension for individual EEG's with anesthesia. The dimensional complexities measured were very high and non-convergent, so that the presence of a fractal attractor in the EEG was not evident in this analysis. The observed trends were the close correlation of both brain hemispheres simultaneously exhibiting the same degree of dimensional complexity, and the close correlation of change in EEG spectral edge frequency with change in dimension with anesthesia.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in ful llment of the requirements for the degree of Master of Science. October 31, 2016. / Brain Computer Interface (BCI) technology is a promising new channel for communication between humans and computers, and consequently other humans. This technology has the potential to form the basis for a paradigm shift in communication for people with disabilities or neuro-degenerative ailments. The objective of this work is to create an asynchronous BCI that is based on a commercial-grade electroencephalography (EEG) sensor. The BCI is intended to allow a user of possibly low income means to issue control signals to a computer by using modulated cortical activation patterns as a control signal. The user achieves this modulation by performing a mental task such as imagining waving the left arm until the computer performs the action intended by the user. In our work, we make use of the Emotiv EPOC headset to perform the EEG measurements. We validate our models by assessing their performance when the experimental data is collected using clinical-grade EEG technology. We make use of a publicly available data-set in the validation phase. We apply signal processing concepts to extract the power spectrum of each electrode from the EEG time-series data. In particular, we make use of the fast Fourier transform (FFT). Specific bands in the power spectra are used to construct a vector that represents an abstract state the brain is in at that particular moment. The selected bands are motivated by insights from neuroscience. The state vector is used in conjunction with a model that performs classification. The exact purpose of the model is to associate the input data with an abstract classification result which can then used to select the appropriate set of instructions to be executed by the computer. In our work, we make use of probabilistic graphical models to perform this association. The performance of two probabilistic graphical models is evaluated in this work. As a preliminary step, we perform classification on pre-segmented data and we assess the performance of the hidden conditional random fields (HCRF) model. The pre-segmented data has a trial structure such that each data le contains the power spectra measurements associated with only one mental task. The objective of the assessment is to determine how well the HCRF models the spatio-spectral and temporal relationships in the EEG data when mental tasks are performed in the aforementioned manner. In other words, the HCRF is to model the internal dynamics of the data corresponding to the mental task. The performance of the HCRF is assessed over three and four classes. We find that the HCRF can model the internal structure of the data corresponding to different mental tasks. As the final step, we perform classification on continuous data that is not segmented and assess the performance of the latent dynamic conditional random fields (LDCRF). The LDCRF is used to perform sequence segmentation and labeling at each time-step so as to allow the program to determine which action should be taken at that moment. The sequence segmentation and labeling is the primary capability that we require in order to facilitate an asynchronous BCI protocol. The continuous data has a trial structure such that each data le contains the power spectra measurements associated with three different mental tasks. The mental tasks are randomly selected at 15 second intervals. The objective of the assessment is to determine how well the LDCRF models the spatio-spectral and temporal relationships in the EEG data, both within each mental task and in the transitions between mental tasks. The performance of the LDCRF is assessed over three classes for both the publicly available data and the data we obtained using the Emotiv EPOC headset. We find that the LDCRF produces a true positive classification rate of 82.31% averaged over three subjects, on the validation data which is in the publicly available data. On the data collected using the Emotiv EPOC, we find that the LDCRF produces a true positive classification rate of 42.55% averaged over two subjects. In the two assessments involving the LDCRF, the random classification strategy would produce a true positive classification rate of 33.34%. It is thus clear that our classification strategy provides above random performance on the two groups of data-sets. We conclude that our results indicate that creating low-cost EEG based BCI technology holds potential for future development. However, as discussed in the final chapter, further work on both the software and low-cost hardware aspects is required in order to improve the performance of the technology as it relates to the low-cost context. / LG2017

An EEG experiment was design to test the influence of level of proficiency on processing of emotion content between languages in a sample of Spanish-English bilinguals divided by proficiency levels between two groups of bilinguals, one group of balanced (n=23) and another of unbalanced bilinguals (n=26). The participants rated words in three categories (negative, neutral, and positive) in terms of emotional valence in English and Spanish while EEG was recorded. Event-related potentials were calculated for two components related to emotion processing: the early posterior negativity (EPN) and the late positive complex LPC. 2 (Bilingual group) x 2 (language) x 3 (valence) x 3 (electrode) analyses were conducted on each component, separately for latency and amplitude. The results for the EPN latency indicated a marginally significant valence effect, with emotion words presenting shorter latencies than neutral words across conditions indicating a processing advantage of emotion content in both languages. The EPN amplitude also reflected the effect of valence, with larger amplitudes both emotion categories than for neutral words. The overall EPN amplitude was larger in Spanish than in English for both bilingual groups across valence categories. The LPC latency was longer in English than in Spanish for both bilingual groups, possibly related to Spanish being identified as native language in most of the sample. The LPC amplitude was larger for negative than for emotion than for neutral words for both groups in English and in Spanish for the Balanced group. The Unbalanced group, however, presented larger for positive than for neutral, and for neutral than for negative words. These results suggest that the Balanced and Unbalanced groups process emotion content similarly in English, but differently in Spanish. The Valence effects were consistent across languages for the Balanced group, but not for the Unbalanced group which might reflect an attenuation of the valence effect for negative words in Spanish for this group, and could indicate weaker emotional reactivity to negative words in the less proficient language. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

The phenomenon of the increase in alpha EEG activity associated with eye closure has been shown to be successful for implementing environmental control for disabled persons. Studies in this thesis investigate strategies which improve the reliability, robustness, and ease of use of alpha EEG control systems. Primarily, research covers the effectiveness of alpha EEG detection algorithms (with regard to detection time and susceptibility to artifact) and the construction and use of EEG sensing electrodes. Many new techniques for the detection of the increase of alpha EEG associated with eye closure are researched, developed, implemented and evaluated. All detection techniques are compared to a conventional method using a novel performance parameterisation criterion. In conjunction with the application of the same EEG data sets to all techniques, the use of the performance criteria enables a fair and quantitative comparison to be made between alpha detection methodologies. Detection techniques employed include enhanced versions of conventional methods, localisation of apparent alpha sources in the brain, and preprocessing methods (such as spatial filtering, adaptive filtering and independent component analysis). The best performance of alpha EEG detection was given by the source power alpha localisation technique, which showed statistically significant and practically important improvements in performance over conventional techniques. Additionally, this localisation technique is convenient and fast to implement. In situations in which electrodes are intended for unsupervised use with environmental control systems, the evaluation of alternative electrode types to the conventional wet electrodes is required, as the use of wet electrodes has several drawbacks. The performance of wet, dry and insulating electrodes is compared in this research. One aspect of the quantitative comparison of electrodes types is the measurement of contact impedance. To enable the fast and accurate measurement of impedance spectra, a new impedance spectroscopy system was developed as part of this thesis. In addition to comparison of impedance criteria, electrodes were evaluated in the presence movement-based, and electric field induced, artifacts. The electrode comparisons were carried out in a direct and quantitative manner in a controlled test environment for the first time. Results indicate that, in contrast to earlier reports, both dry and insulating electrode perform well with respect to artifact and offer a viable alternative to wet electrodes for long-term monitoring of biosignals from the surface of the skin. More improvements are required before such electrodes are suitable for EEG usage.