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The ongoing electrical activity of the brain is known as the electroencephalogram (EEG). Evoked potentials (EPs) are voltage deviations in the EEG elicited in association with stimuli. EPs provide clinical information by allowing an insight into neurological processes. The amplitude of EPs is typically several times less than the background EEG. The background EEG has the effect of obscuring the EPs and therefore appropriate signal processing is required for their recovery. The EEG waveforms recorded from electrodes placed on the scalp contains the ongoing background EEG, EPs from various brain sources as well as signal components with sources external to the brain. An example of externally generated signal which is picked up by the electrodes on the scalp is the electrooculogram (EOG). This signal is generated by the eyes when eye movements or blinks are performed. Saccade-related EEG waveforms were recorded from 7 normal subjects. A signal source separation technique, namely the independent component analysis (ICA) algorithm of Bell and Sejnowski (hereafter refereed to as BS_ICA), was employed to analyse the recorded waveforms. The effectiveness of the BS_ICA algorithm as well as that of the ICA algorithm of Cardoso, was investigated for removing ocular artefact (OA) from the EEG. It was quantitavely demonstrated that both ICA algorithms were more effective than the conventional correlation-based techniques for removing the OA from the EEG.A novel iterative synchronised averaging method for EPs was devised. The method optimally synchronised the waveforms from successive trials with respect to the event of interest prior to averaging and thus preserved the features of the signals components that were time-locked to the event. The recorded EEG waveforms were analysed using BS_ICA and saccade-related components (frontal and occipital pre-saccadic potentials, and the lambda wave) were extracted and their scalp topographies were obtained. This initial study highlighted some limitations of the conventional ICA approach of Bell and Sejnowski for analysing saccade-related EEG waveforms. Novel techniques were devised in order to improve the performance of the ICA algorithm of Bell and Sejnowski for extracting the lambda wave EP component. One approach involved designing a template-model that represented the temporal characteristics of a lambda wave. Its incorporation into the BS_ICA algorithm improved the signal source separation ability of the algorithm for extracting the lambda wave from the EEG waveforms. The second approach increased the effective length of the recorded EEG traces prior to their processing by the BS_ICA algorithm. This involved abutting EEG traces from an appropriate number of successive trials (a trial was a set of waveforms recorded from 64 electrode locations in a experiment involving a saccade performance). It was quantitatively demonstrated that the process of abutting EEG waveforms was a valuable pre-processing operation for the ICA algorithm of Bell and Sejnowski when extracting the lambda wave. A Fuzzy logic method was implemented to identify BS_ICA-extracted single-trial saccade-related lambda waves. The method provided an effective means to automate the identification of the lambda waves extracted by BS_ICA. The approach correctly identified the single-trial lambda waves with an Accuracy of 97.4%.

The brain requires a continuous supply of oxygen and even a short period ofreduced oxygen supply risks severe and lifelong consequences for theaffected individual. The delivery is a vulnerable period for a baby who mayexperience for example hypoxia (lack of oxygen) that can damage the brain.Babies who experience problems are placed in an intensive care unit wheretheir vital signs are monitored, but there is no reliable way to monitor thebrain directly. Monitoring the brain would provide valuable informationabout the processes going on in it and could influence the treatment and helpto improve the quality of neonatal care. The scope of this project is todevelop methods that eventually can be put together to form a monitoringsystem for the brain that can function as decision-support for the physician incharge of treating the patient.The specific technical problem that is the topic of this thesis is detection ofburst and suppression in the electroencephalogram (EEG) signal. The thesisstarts with a brief description of the brain, with a focus on where the EEGoriginates, what types of activity can be found in this signal and what theymean. The data that have been available for the project are described,followed by the signal processing methods that have been used for preprocessing,and the feature functions that can be used for extracting certaintypes of characteristics from the data are defined. The next section describesclassification methodology and how it can be used for making decisionsbased on combinations of several features extracted from a signal. Theclassification methods Fisher’s Linear Discriminant, Neural Networks andSupport Vector Machines are described and are finally compared with respectto their ability to discriminate between burst and suppression. An experimentwith different combinations of features in the classification has also beencarried out. The results show similar results for the three methods but it canbe seen that the SVM is the best method with respect to handling multiplefeatures.

Thesis M Med (Psych)--University of Limpopo, 2010. / INTRODUCTION: The yield of EEG amongst psychiatric patients has been reported to be low and the value of EEG in the practice of psychiatry is questionable.EEG is used as part of a diagnostic work up for patients with psychiatric disorders .Often the reason given for its use is to exclude epilepsy as a cause of psychiatric symptoms. Epilepsy is primarily a clinical diagnosis, but the EEG may provide strong support by the findings of inter – ictal Epileptogenic discharge METHOD: All the adult EEGs requested at Dr George Mukhari psychiatric hospital, over a 36 month period,were reviewed to describe the outcome of the requested EEG reports. The study is a simple retrospective analysis of 111 consecutive EEG requested to the department of Neurology at DGMH from psychiatric unit at DGMH. Subjects were both inpatients and outpatients. All the EEG was reported by a qualified Neurologist. Data were extracted from the EEG request form and the patients’ clinical files, which reported on the clinical reason for the EEG test, nature of psychiatric diagnosis of patients, the psychiatric treatment received prior to the EEG test and the nature of the EEG results RESULTS: There were 111 EEG reports analysed, and 69 EEG reports for males and 42 EEG reports for females. The reason for EEG request was dominated mainly by exclusion of epilepsy. Majority of the patients were diagnosed with a psychotic disorder , followed second by a mood disorder , all of which was attributed to GMC (epilepsy).About 62.73% of patients were on a combination of treatment of antipsychotic drug and anticonvulsants, whilst 34.55% were on antipsychotic monotherapy prior to the EEG test. Further analysis of the requested EEG form was carried out in whom the test was to determine whether or not the patients were suffering from epilepsy. EEG abnormalities were identified amongst 24% of the patients. About 11,7% of patients presented with non specific EEG results. Out of a total number of 111 patients whom an EEG test was requested and epilepsy was highly suspected from clinical presentation, only 14 patients (12.6%),presented with epileptiform discharge on their EEG results. However majority of the patients (76%) demonstrated normal EEG pattern, which doesn’t exclude a diagnosis of epilepsy. CONCLUSION: The yield of EEG in psychiatry is low. To diagnose epilepsy as a cause of psychiatric presentation,clinicians should continue to rely on the clinical history of attacks and not the EEG. In the practice of psychiatry it is not recommended to routinely order an EEG to exclude a diagnosis of epilepsy, more so to confirm a psychiatric diagnosis. The presence of a psychiatric symptoms in patients who presents with epilepsy, is rarely associated with meaningful EEG changes

A new framework is described for managing uncertainty and for dealing with artefact corruption to introduce objectivity in the interpretation of the electroencephalogram (EEG). Conventionally, EEG interpretation is time consuming and subjective, and is known to show significant inter- and intra-personnel variation. A need thus exists to automate the interpretation of the EEG to provide a more consistent and efficient assessment. However, automated analysis of EEGs by computers is complicated by two major factors. The difficulty of adequately capturing in machine form, the skills and subjective expertise of the experienced electroencephalbgrapher, and the lack of a reliable means of dealing with the range of EEG artefacts (signal contamination). In this thesis, a new framework is described which introduces objectivity in two important outcomes of clinical evaluation of the EEG, namely, the clinical factual report and the clinical 'conclusion', by capturing the subjective expertise of the electroencephalographer and dealing with the problem of artefact corruption. The framework is separated into two stages .to assist piecewise optimisation and to cater for different requirements. The first stage, 'quantitative analysis', relies on novel digital signal processing algorithms and cluster analysis techniques to reduce data and identify and describe background activities in the EEG. To deal with artefact corruption, an artefact removal strategy, based on new reUable techniques for artefact identification is used to ensure that artefact-free activities only are used in the analysis. The outcome is a quantitative analysis, which efficiently describes the background activity in the record, and can support future clinical investigations in neurophysiology. In clinical practice, many of the EEG features are described by the clinicians in natural language terms, such as very high, extremely irregular, somewhat abnormal etc. The second stage of the framework, 'qualitative analysis', captures the subjectivity and linguistic uncertainty expressed.by the clinical experts, using novel, intelligent models, based on fuzzy logic, to provide an analysis closely comparable to the clinical interpretation made in practice. The outcome of this stage is an EEG report with qualitative descriptions to complement the quantitative analysis. The system was evaluated using EEG records from 1 patient with Alzheimer's disease and 2 age-matched normal controls for the factual report, and 3 patients with Alzheimer's disease and 7 age-matched nonnal controls for the 'conclusion'. Good agreement was found between factual reports produced by the system and factual reports produced by qualified clinicians. Further, the 'conclusion' produced by the system achieved 100% discrimination between the two subject groups. After a thorough evaluation, the system should significantly aid the process of EEG interpretation and diagnosis.

Neural entrainment is a phenomenon whereby neural oscillations adjust their frequency to synchronize with the periodic vibration of external stimuli. Research suggests that neural entrainment may help explain the relationship between music education and more optimal cognitive performance later in development. This dissertation tested whether sensory entrainment caused short-term cognitive and motor performance benefits in a young adult sample, and whether entrainment or performance were impacted by stimulus parameters like modality or rhythm or individual differences in attentional ability and music training. Participants (N= 47) were asked to report the extent and type (e.g. instrumental, vocal) of music experience and severity of ADHD symptoms, and then were exposed to repetitive 1.25-Hz or arrhythmic visual or auditory stimuli with interlaced Flanker test items, while EEG was recorded. At some points in the experiment participants were additionally tasked with tapping along to the 1.25-Hz beat through both beat stimuli and gaps. Some entrainment and performance effects were congruent with findings from prior literature, while many other hypotheses regarding entrainment effects were not supported. In terms of individual differences, neither music training nor ADHD symptoms impacted entrainment, but ADHD did impact the effects of entrainment stimuli on Flanker reaction time, with higher ADHD symptoms predicting worse performance during periods of rhythmic stimulation. Lastly and surprisingly, while neither entrainment, music training, nor ADHD symptoms impacted beat-keeping performance in general, ADHD symptoms predicted better beat-keeping during stimulus gap periods. Results in general paint a complicated picture of acute entrainment effects and individual differences. / Doctor of Philosophy / Neural entrainment is a phenomenon whereby neural oscillations adjust their frequency to synchronize with the periodic vibration of external stimuli. Research suggests that neural entrainment may help explain the relationship between music education and more optimal cognitive performance later in development. This dissertation tested whether sensory entrainment caused short-term cognitive and motor performance benefits in a young adult sample, and whether entrainment or performance were impacted by stimulus parameters like modality or rhythm or individual differences in attentional ability and music training. Participants (N= 47) were asked to report the extent and type (e.g. instrumental, vocal) of music experience and severity of ADHD symptoms, and then were exposed to repetitive 1.25-Hz or arrhythmic visual or auditory stimuli with interlaced Flanker test items, while EEG was recorded. At some points in the experiment participants were additionally tasked with tapping along to the 1.25-Hz beat through both beat stimuli and gaps. Some entrainment and performance effects were congruent with findings from prior literature, while many other hypotheses regarding entrainment effects were not supported. In terms of individual differences, neither music training nor ADHD symptoms impacted entrainment, but ADHD did impact the effects of entrainment stimuli on Flanker reaction time, with higher ADHD symptoms predicting worse performance during periods of rhythmic stimulation. Lastly and surprisingly, while neither entrainment, music training, nor ADHD symptoms impacted beat-keeping performance in general, ADHD symptoms predicted better beat-keeping during stimulus gap periods. Results in general paint a complicated picture of acute entrainment effects and individual differences.

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There are hundreds of thousands of people who could benefit from a Brain-Computer Interface. However, not all are willing to undergo surgery, so an EEG is the prime candidate for use as a BCI. The features of Event-Related Desynchronization and Synchronization could be used for a switch and have been in the past. A new method of feature selection was proposed to optimize classification of active motor movement vs a non-active idle state. The previous method had pre-selected which frequency and electrode to use as electrode C3 at the 20Hz bin. The new method used SPSS statistical software to determine the most significant frequency and electrode combination. This improved method found increased accuracy in classifying cases as either active or idle states. Future directions could be using multiple features for classification and BCI control, or exploiting the difference between ERD and ERS, though for either of these a more advanced algorithm would be required.

Imagética motora é um processo mental que produz modulações na amplitude dos sinas de eletroencefalogramas em progresso. Os padrões presentes nestas modulações podem ser usados para classificar este processo mental, mas a identificação destes padrões não é uma tarefa trivial, porque eles estão presentes em bandas de frequências que são específicas para cada pessoa. Neste trabalho, apresenta-se um novo método para selecionar as bandas de frequência específicas para cada pessoa baseado na arquitetura do método Filter Bank Common Spatial Pattern. Para selecionar as bandas de frequência mais relevantes para cada pessoa, o método proposto aplica uma busca exaustiva para encontrar o melhor subconjunto de bandas de frequência contendo os padrões mais discriminativos dentro de um espaço de busca restrito a um tamanho fixo para este subconjunto. Esse tamanho é determinado usando validação cruzada e o método Sequential Forward Floating Selection. O método proposto foi avaliado usando a base de dados pública 2b da BCI Competition IV, mostrando melhores resultados do que todos os métodos também avaliados nessa base de dados. / Motor imagery is a mental process that when performed, produces modulations in the amplitude of ongoing electroencephalogram signals. These modulations happen following a series of patterns that can be used to classify this mental process, but the detection of those patterns is not a trivial task, because they occur in frequency bands that are specific for each person. In this work, we present a method to select these subject-specific frequency bands based on the arquitecture of the Filter Bank Common Spatial Pattern approach. To select the most relevant frequency bands for each person, our method uses an exhaustive search to find the best subset of frequency bands containing the most discriminative patterns, but with one restriction, the search space is restricted to find a subset with a fixed number of frequency bands. The number is determined using cross-validation and the Sequential Forward Floating Selection method. We demonstrate that, using the data set 2b of the BCI Competition IV, our method is more accurate than current methods evaluated on the same data set.

A preocupação crescente com o desenvolvimento de hábitos saudáveis e uma alimentação adequada vêm promovendo o avanço nas ciências dos alimentos, como também nas relações entre estes e o homem. Nas últimas décadas, a qualidade da análise sensorial tem sido estudada não apenas com base na interação entre o homem e o alimento, mas através da compreensão dos fatores subjetivos e emocionais que influenciam os consumidores. O interesse pelos aspectos emocionais que influenciam essas escolhas amplia os estudos para o terreno das ciências psicológicas, que procuram entender as bases neurocognitivas e analíticas do funcionamento cerebral como motivadores desses processos no homem. O trabalho em neurociência cognitiva tem vislumbrado novos paradigmas, com o desenvolvimento de novas técnicas de observação do cérebro, visando conhecer sua estrutura e função, além de permitir a associação de um comportamento clinico ou experimentalmente observado, não só a um correlato mental presumido, mas também a marcadores específicos da atividade mental observada. Neste sentido, a atividade elétrica cerebral adquirida usando-se o eletroencefalograma (EEG) vem sendo, recentemente, muito usada para monitoramento de eventos cerebrais. Portanto, este trabalho tem como objetivo propor um modelo de análise sensorial que permita avaliar quantitativamente a ação do estímulo gustativo no contexto cognitivo, utilizando o EEG. O experimento foi realizado no laboratório de Física Aplica e Computacional (LAFAC), na Universidade Estadual de São Paulo (USP), Campus Pirassununga, no período de maio/2011 a maio/2012, com 23 voluntários (13 do sexo feminino e 10 do sexo masculino), com idade entre 19 e 24 anos. Foram coletados os sinais de EEG destes voluntários no momento em que experimentavam 27 amostras do sabor doce, sendo nove delas a 0% (água), nove com 0,15% de sacarose (limiar) e nove com 5,0% (concentração máxima). Desta amostragem, foi selecionado um grupo de 7 voluntários (5 do sexo feminino e 2 do sexo masculino) para análise dos dados. Neste estudo constatou-se que quatro voluntários do sexo feminino e um do sexo masculino foram capazes de identificar, através de dados obtidos com o EEG, as diferentes dosagens de sacarose. Os resultados permitem concluir que o modelo de análise sensorial proposta, com a utilização de EEG, para avaliar os estímulos gustativos no contexto cognitivo foi satisfatória e permitiu com a utilização de processamento de sinais digitais e AGR (Análise Adaptativa de Gabor) quantificar com eficiência a percepção dos voluntários as diferentes dosagens apresentadas no experimento. Sugere-se que as diferentes percepções dos voluntários no geral, encontradas no EEG, representam as suas singularidades quanto aos significados atribuídos ao sabor e suas correlações. / The growing concern with the development of healthy habits and a balanced diet allows the progress in Food Science as well as in the relationship between human beings and food. In the last decades the quality of the sensorial analysis has been studied not based only on the interaction between human beings and food but also through the comprehension of subjective and emotional factors that influence customers. The interest in emotional aspects which influence such choices extends the studies to the field of Psychological Science that attempts to understand neurocognitive and analytical bases of brain functioning as motivating elements of those human processes. The work in Cognitive Neuroscience has glimpsed new paradigms due to the development of new techniques about brain observation with the purpose of getting to know its structure and function other than allowing the association of behavior observed experimentally or clinical behavior not considering only a presumed mental correlative but also specific markers of mental activity observed in advance. In that aspect the brain electrical activity acquired through electroencephalogram has been recently used to monitor brain events. Therefore, the aim of this work is to propose a sensorial analysis pattern which allows to evaluate quantitatively the action of taste stimuli within a cognitive context using electroencephalogram. The experiment was carried out in the Laboratory of Applied and Computational Physics (LAFAC) in the University of the State of São Paulo (USP), Pirassununga Campus, from May 2011 to May 2012 with 23 volunteers (13 women and 10 men) aging between 19 and 21 years old. The volunteers\' electroencephalogram signals were collected when they tasted 27 sweet flavored samples, being 9 of them with 0% of sucrose (water), 9 with 0.15% of sucrose (threshold), and 9 with 5% of sucrose (maximum concentration). Seven volunteers were selected from this experiment (5 women and 2 men) whose data was analyzed. In this study was found that four women and one man were able to identify, through the EEG data obtained with the different concentrations of sucrose. The results obtained allowed to conclude that the proposed sensorial analysis pattern using electroencephalogram to evaluate taste stimuli within the cognitive context was satisfactory and allowed, along with the use of digital signals processing and Gabor Adaptive Analysis (AGR), to analyze and to quantify efficiently the volunteers\' perception of different doses presented in the experiment. It is suggested that the different perceptions of volunteers in general, encountered in EEG, representing singularities as to the meanings attributed to taste and their correlations.