O presente estudo, através de metodologia sistematizada envolvendo testes eletrodiagnósticos e avaliações dinâmicas da marcha, objetivou analisar e interpretar parâmetros biomecânicos e eletrofisiológicos relacionadas à neuropatia diabética durante a marcha. Analisou e interpretou-se parâmetros temporais, de distribuição da pressão plantar e força reação do solo, adquiridos através de um equipamento específico para tal fim para três grupos experimentais. Analisou-se parâmetros da eletrofisiologia indicadores de respostas somato-sensoriais: cronaxia sensitiva e tolerância à dor em regiões anatomicamente determinadas da superfície plantar. Com tais parâmetros, buscou-se definir os padrões da marcha de indivíduos neuropatas ou não, para intervir de forma mais complexa na descrição e interpretação da doença. Investigou-se relações de dependência entre as variáveis dinâmicas e as eletrofisiológicas como forma de entender outros fatores intervenientes na etiologia das lesões decorrentes, bem como nas alterações compensatórias dinâmicas na marcha desses pacientes, alterações essas que são bilaterais e necessárias para a adaptação devido ao déficit sensorial decorrente da neuropatia. Apesar de não ter sido observada correlação entre estas variáveis, vale ressaltar que esse relacionamento torna-se cada vez mais importante para a compreensão da doença e interpretação de suas diferentes respostas adaptativas na avaliação do comportamento da marcha humana / The purpose of this study is to investigate electrophysiological parameters related to diabetic neuropathy and biomechanical parameters during gait, by systemized methodology, including electrodiagnostic tests and dynamic gait evaluation. We have analysed temporal aspects during stance phase, plantar pressure distribution, and ground reaction forces, acquired by an specific equipment in three experimental groups. We also have studied electrophysiological parameters related to somatic sensorial responses, like sensitive cronaxie and pain tolerance threshold, in some specific anatomical areas of plantar surface. Within these parameters, we searched for defined gait patterns in neuropathic diabetic patients, as well as in diabetic patients without neuropathy, to intervene in a more complex description and interpretation of the disease. We also looked for some relations of dependency between dynamic and electrophysiological variables as a contribution to the better undestanding of the disease ethiology and dynamic compensatory mechanisms in pathological gait. These compensatory mechanisms are usually bilateral, because it is a way of adapting to the sensorial deficit due to the neuropathy. Although there are no observed correlation between these variables, it is good to emphasize that such relationship becomes gradually more important to the understanding of the disease and the interpretation of its different adaptative reactions in human locomotion

Driver sleepiness is a cause for crashes and it is estimated that 3.9 to 33 % of all crashes might be related to sleepiness at the wheel. It is desirable to get an objective measurement of driver sleepiness for reduced sensitivity to subjective variations. Using deep learning for classification of driver sleepiness could be a step toward this objective. In this master thesis, deep learning was used for investigating classification of electrophysiological data, electroencephalogram (EEG) and electrooculogram (EOG), from drivers into levels of sleepiness. The EOG reflects eye position and EEG reflects brain activity. Initially, the intention was to include electrocardiogram (ECG), which reflects heart activity, in the research but this data were later excluded. Both raw time series data and data transformed into time-frequency domain representations were fed into the developed neural networks and for comparison manually extracted features were used in a shallow neural network architecture. Investigation of using EOG and EEG data separately as input was performed as well as a combination as input. The data were labeled using the Karolinska Sleepiness Scale, and the scale was divided into two labels "fatigue" and "alert" for binary classification or in five labels for comparison of classification and regression. The effect of example length was investigated using 150 seconds, 60 seconds and 30 seconds data. Different variations of the main network architecture were used depending on the data representation and the best result was given when using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network with time distributed 150 seconds EOG data as input. The accuracy was in this case 80.4 % and the majority of both alert and fatigue epochs were classified correctly with 85.7 % and 66.7 % respectively. Using the optimal threshold from the created receiver operating characteristics (ROC) curve resulted in a more balanced classifier with 76.3 % correctly classified alert examples and 79.2 % correctly classified fatigue examples. The results from the EEG data, both in terms of accuracy and distribution of correctly classified examples, were shown to be less promising compared to EOG data. Combining EOG and EEG signals was shown to slightly increase the proportion of correctly classified fatigue examples. However, more promising results were obtained when balancing the classifier for solely EOG signals. The overall result from this project shows that there are patterns in the data connected to sleepiness that the neural network can find which makes further work on applying deep learning to the area of driver sleepiness interesting.

The purpose of this study was to compare event-related potential (ERP) effects of speech processing and effects in sentence reading elicited by sentences containing gapping structures, or a "missing" verb. N400 and P600 waveforms were collected in 20 adults between 18 and 30 years of age. Two experiments were conducted with each participant. In the two experiments ERP recordings were collected as sentences, some containing gapping structures, were presented to the subjects. In one experiment sentences were presented through headphones in sentences spoken at normal rate and with normal intonation. In the second experiment sentences with the same gapping structures were presented on a computer screen word by word at a rate of four words per second. Results suggest that all gapping structures are processed at approximately the same time. Amplitude and topography differences were seen between stimuli types and modalities.

Many questions remain unanswered regarding the intricacies of the human brain, especially with regard to the complexities of language processing. One essential component of human sentence processing is the ability to detect, decipher, and recover from errors in the interpretation of both verbal and written language. This process of repair of ungrammatical sentences and revision or reinterpretation of ambiguous sentences has been studied extensively in recent years. A variety of tools have been developed, including the use of event-related potentials (ERPs) in order to assess how language is processed and developed, and to help better identify the nature of these processes. The purpose of this study was to compare event-related potential effects of speech processing of spoken and written sentences containing both incorrect and correct semantic and syntactic information. Specifically, sentences containing correct and incorrect gapping structures, each with a "missing" verb, were presented along with other grammatical and ungrammatical sentences in order to elicit and measure the P300, N400, and P600 amplitudes and latencies. The aim was to determine some of the commonalities and differences in these electrophysiological responses via the auditory and visual modalities. Two experiments were conducted with each participant, one in the auditory modality, and one within the visual using two sets of stimuli. Amplitude and topography differences were noted within and between modalities for each of the components (P300, N400, and P600), as well as between stimulus types. Significant findings suggest that in the adolescent population, incorrect gapping structures are generally processed as semantic errors, as evidenced by the N400 response, followed by the P600 response in both the auditory and visual modalities. The exact nature of the P600 component within gapping structures remains unclear. Of particular interest was the involvement of the occipital area of the brain for the processing of gapping structures. Minimal differences were noted overall between adolescents and the adult populations.

Infants 6-8 months old are able to identify nearly every speech sound contrast on which they have been tested, regardless of whether that contrast represents an across-category (two different phonemes) phonetic change in their native language or not. A child's ability to identify non-native consonant contrasts greatly diminishes by 11-12 months of age. The present study examined speech sound discrimination in three groups of listeners. Adult second language (L2) listeners of Spanish were compared with native English listeners with no knowledge of Spanish and native Mexican listeners to determine if they would show the ability to discriminate phonetic distinctions that are present in Spanish and not English. Behavioral and Electrophysiological measures were obtained in response to a continuum of bilabial consonant-vowel (CV) stimuli that differed in voice onset time (VOT) from -50 to 30 ms VOT (/ba/ to /pa/). The -50 ms VOT stimulus was paired with each of the others to form stimulus pairs with varying degrees of acoustic difference. Behavioral data showed that L2 listeners of Spanish perceived a transition from /ba/ to /pa/ later than native Spanish listeners but earlier than English only listeners. Electrophysiological data using the mismatch negativity revealed that the both L2 Spanish and native Spanish listeners perceived a distinction between stimuli in the stimulus pairs 20 ms earlier than English only listeners. These results support the theory that underlying neural-sensory representations of speech may be altered by linguistic experiences, and that the loss of non-native speech sound discrimination abilities in infancy and the regaining of these abilities in adulthood is not due merely to attentional bias.

This study examined the electrophysiological activity of two children with Specific Language Impairment (SLI) and two aged-matched peers while listening to syntactically correct sentences versus syntactically incorrect sentences. The study specifically analyzed the N400 and P600 components. The N400 component is a negative wave occurring approximately 400 ms post-stimulus and is elicited by semantically incorrect stimuli. The P600 component is a positivity that occurs approximately 600 ms post-stimulus and reflects processing of syntactically incorrect stimuli. The participants in the study included a 7-year-old child and a 9-year-old child with SLI and two age-matched peers with typically developing language. Each participant listened to a series of syntactically correct and incorrect stimuli. The results of this study indicate that children with SLI present with an N400 while listening to both syntactically correct and incorrect stimuli. This suggests that these participants have greater lexical and semantic processing demands while listening to sentence stimuli. The study also suggests that 7-year-olds have greater syntactic processing demands as seen by the presence of the P600 in both the correct and incorrect conditions. This was likely due to the complexity of the sentence stimuli which included irregular past tense and irregular plural forms. Further research is still needed in order to better understand the role of the N400 and P600 in children with SLI, which will provide useful information regarding the neurological basis for language impairments.

A commentary on Four conceptual fallacies in mapping the time course of recognition by VanRullen, R. (2011). Front. Psychol. 2:365. doi: 10.3389/fpsyg.2011.00365 Does filtering preclude us from studying ERP time-courses? by Rousselet, G. A. (2012). Front. Psychol. 3:131. doi: 10.3389/fpsyg.2012.00131

O teste de potencial auditivo de tronco encefálico (PEATE) permite captar e registrar as atividades elétricas do sistema auditivo, desde a cóclea até o tronco encefálico. No Braisl ainda é pouco utilizado, e no Sul do país ainda não é realizado em animas.O objetivo deste trabalho foi identificar os valores absolutos de latência, interpicos, amplitudes, comparar as polaridades de compressão e rarefação e conhecer o limiar auditivo em onze cães hígidos da raça Beagle. Os cães passaram por exame físico e neurológico e após foram sedados e submetidos a otoscopia e ao exame de Potencial Auditivo de Tronco Encefálico (PEATE). Foram utilizados eletrodos de superfície e fones de inserção, o estimulo utilizado foi o clique com estimulação bilateral. O teste foi iniciado com a intensidade de 90 dB SPL com polaridade condensada. Realizava-se na sequência mais uma pesquisa em 90 dB SPL com polaridade rarefeita. Após, foi feito o exame com diminuição da intensidade de 10 dB SPL em 10 dB SPL, sendo todas as pesquisas feitas com polaridade condensada, para a pesquisa dos limiares auditivos. A interpretação do PEATE, foi realizada pela avaliação das ondas I, III e IV, pela identificação das latências absolutas e interpicos, pesquisa do limiar auditivo e comparação entre polaridades. Na latência da onda I com polaridade rarefeita em 90 dB SPL, os valores médios de latência absoluta da onda I foram significativamente maiores na orelha esquerda (p=0,037*). Dados relativos à latência absoluta da onda V evidenciou que na orelha direita a latência foi significativamente menor na intensidade de 70 dB SPL, e não houve presença de onda V na orelha esquerda em 40 dB SPL. Não houveram diferenças significativas nas latências dos interpicos entre as orelhas direita e esquerda, com polaridade condensada e rarefeita. Os valores das medianas das amplitudes I, III e V com 90 dB SPL em compressão e rarefação, não diferiram estatisticamente. Concluiu-se que o nível de resposta mínimo se encontrou entre 40 dB SPL e 80 dB SPL. / The Brainstem Auditory Evoked Potential (BAEP) capture and record the electrical activity of the auditory system, from the cochlea to the brainstem. The Brazil is still little used, and in the South of the country is not yet done in animals. The objective of this study was to identify the absolute values of latency, interpeaks, amplitudes, compare the polarities of compression and rarefaction and to know the auditory threshold in eleven healthy beagle dogs. The dogs underwent physical and neurological examination and were sedated and submitted to otoscopy and Brainstem Auditory Evoked Potential (BAEP). Surface electrodes and insertion headphones were used, the stimulus used was the click with bilateral stimulation. The test was started with 90 dB SPL intensity with condensed polarity. Further research was conducted on 90 dB SPL with rarefied polarity. After that, the test was performed with sequential reductions of 10 dB SPL intensity, and all the research for the auditory thresholds was done with condensed polarity. The interpretation of BAEP was performed by the evaluation of waves I, III and IV, by the identification of the absolute and interpeak latencies, auditory threshold research and comparison between polarities. In wave I latency with a rarefied polarity of 90 dB SPL, the mean absolute values of wave I latency were significantly higher in the left ear (p = 0.037*). Data on absolute V wave latency showed that, in the right ear, the latency was significantly lower in the intensity of 70 dB SPL, and there was no presence of V wave in the left ear in 40 dB SPL. There were no statistically significant differences in the interpeak latencies between the right and left ears, with condensed and rarefied polarity. The values of the medians of amplitudes I, III and V with 90 dB SPL in compression and rarefaction did not differ statistically. The minimum response level was found between 40 dB SPL and 80 dB SPL.

The ability to perceive speech is related to the listener's ability to differentiate among frequencies (i.e. spectral resolution). Cochlear implant users exhibit variable speech perception and spectral resolution abilities, which can be attributed at least in part to electrode interactions at the periphery (i.e. spatial resolution). However, electrophysiological measures of peripheral spatial resolution have not been found to correlate with speech perception. The purpose of this study was to systematically evaluate auditory processing from the periphery to the cortex using both simple and spectrally complex stimuli in order to better understanding the underlying processes affecting spatial and spectral resolution and speech perception. Eleven adult cochlear implant users participated in this study. Peripheral spatial resolution was assessed using the electrically evoked compound action potential (ECAP) to measure channel interaction functions for thirteen probe electrodes. We evaluated central processing using the auditory change complex (ACC), a cortical response, elicited with both spatial (electrode pairs) and spectral (rippled noise) stimulus changes. Speech perception included a vowel-discrimination task and the BKB-SIN test of keyword recognition in noise. We varied the likelihood of electrode interactions within each participant by creating three experimental programs, or MAPs, using a subset of seven electrodes and varying the spacing between activated electrodes. Linear mixed model analysis was used to account for repeated measures within an individual, allowing for a within-subject interpretation. We also performed regression analysis to evaluate the relationships across participants. Both peripheral and central processing abilities contributed to the variability in performance observed across CI users. The spectral ACC was the strongest predictor of speech perception abilities across participants. When spatial resolution was varied within a person, all electrophysiological measures were significantly correlated with each other and with speech perception. However, the ECAP measures were the best single predictor of speech perception for the within-subject analysis, followed by the spectral ACC. Our results indicate that electrophysiological measures of spatial and spectral resolution can provide valuable information about perception. All three of the electrophysiological measures used in this study, including the ECAP channel interaction functions, demonstrated potential for clinical utility.

<p>In modern medicine, the measurement of electrophysiological signals play a key role in health monitoring and diagnostics. Electrical activity originating from our nerve and muscle cells conveys real-time information about our current health state. The two most common and actively used techniques for measuring such signals are electrocardiography (ECG) and electroencephalography (EEG).</p><p>These signals are very weak, reaching from a few millivolts down to tens of microvolts in amplitude, and have the majority of the power located at very low frequencies, from below 1 Hz up to 40 Hz. These characteristics sets very tough requirements on the electrical circuit designs used to measure them. Usually, measurement is performed by attaching electrodes with direct contact to the skin using an adhesive, conductive gel to fixate them. This method requires a clinical environment and is time consuming, tedious and may cause the patient discomfort.</p><p>This thesis investigates another method for such measurements; by using a non-contact, capacitively coupled sensor, many of these shortcomings can be overcome. While this method relieves some problems, it also introduces several design difficulties such as: circuit noise, extremely high input impedance and interference. A capacitively coupled sensor was created using the bottom layer of a printed circuit board (PCB) as a capacitor plate and placing it against the signal source, that acts as the opposite capacitor plate. The PCB solder mask layer and any air in between the two acts as the insulator to create a full capacitor. The signal picked up by this sensor was then amplified by 60 dB with a high input impedance amplifier circuit and further conditioned through filtering.</p><p>Two measurements were made of the same circuit, but with different input impedances; one with 10 MΩ and one with 10 GΩ input impedance. Additional filtering was designed to combat interference from the main power lines at 50 Hz and 150 Hz that was discovered during initial measurements. The circuits were characterized with their transfer functions, and the ability to amplify a very low-level, low frequency input signal. The results of these measurements show that high input impedance is of critical importance for the functionality of the sensor and that an input impedance of 10 GΩ is sufficient to produce a signal-to-noise ratio (SNR) of 9.7 dB after digital filtering with an input signal of 25 μV at 10 Hz.</p>