The use of ultra-wideband (UWB) signals holds great promise for remote monitoring of vital-signs which has applications in the medical, for first responder and in security. Previous research has shown the feasibility of a UWB-based radar system for respiratory and heartbeat rate estimation. Some simulation and real experimental results are presented to demonstrate the capability of the respiration rate detection. However, past analysis are mostly based upon the assumption of an ideal experiment environment. The accuracy of the estimation and interference factors of this technology has not been investigated. This thesis establishes an analytical framework for the FFT-based signal processing algorithms to detect periodic bio-signals from a single target. Based on both simulation and experimental data, three basic challenges are identified: (1) Small body movement during the measurement interval results in slow variations in the consecutive received waveforms which mask the signals of interest. (2) The relatively strong respiratory signal with its harmonics greatly impact the detection of heartbeat rate. (3) The non-stationary nature of bio-signals creates challenges for spectral analysis. Having identified these problems, adaptive signal processing techniques have been developed which effectively mitigate these problems. Specifically, an ellipse-fitting algorithm is adopted to track and compensate the aperiodic large-scale body motion, and a wavelet-based filter is applied for attenuating the interference caused by respiratory harmonics to accurately estimate the heartbeat frequency. Additionally, the spectrum estimation of non-stationary signals is examined using a different transform method. Results from simulation and experiments show that substantial improvement is obtained by the use of these techniques. Further, this thesis examines the possibility of multi-target detection based on the same measurement setup. Array processing techniques with subspace-based algorithms are applied to estimate multiple respiration rates from different targets. The combination of array processing and single- target detection techniques are developed to extract the heartbeat rates. The performance is examined via simulation and experimental results and the limitation of the current measurement setup is discussed. / Master of Science

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.

O uso de sistemas denominados Brain Computer Interface, ou simplesmente BCI, para controle de dispositivos tem gerado cada vez mais trabalhos de análise de sinais de EEG, principalmente devido ao fato do desenvolvimento tecnológico dos sistemas de processamento de dados, trazendo novas perspectiva de desenvolvimento de equipamentos que auxiliem pessoas com debilidades motoras. Neste trabalho é abordado o comportamento dos classificadores LDA (Discriminante Linear de Fisher) e o classificador Naive Bayes para classificação de movimento de mão direita e mão esquerda a partir da aquisição de sinais eletroencefalográficos. Para análise destes classificadores foram utilizadas como características de entrada a energia de trechos do sinal filtrados por um passa banda com frequências dentro dos ritmos sensório-motor e também foram utilizadas componentes de energia espectral através do periodograma modificado de Welch. Como forma de pré-processamento também é apresentado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento. Foram obtidas taxas de acerto de até 70% para a base de dados geradas neste trabalho e de até 88% utilizando a base de dados do BCI Competition II, taxas de acertos compatíveis com outros trabalhos na área. / Brain Computer Interface (BCI) systems usage for controlling devices has increasingly generated research on EEG signals analysis, mainly because the technological development of data processing systems has been offering a new perspective on developing equipment to assist people with motor disability. This study aims to examine the behavior of both Fisher's Linear Discriminant (LDA) and Naive Bayes classifiers in determining both the right and left hand movement through electroencephalographic signals. To accomplish this, we considered as input feature the energy of the signal trials filtered by a band pass with sensorimotor rhythm frequencies; spectral power components from the Welch modified periodogram were also used. As a preprocessing form, the Common Spatial Pattern (CSP) filter was used to increase the discriminative activity between classes of movement. The database created from this study reached hit rates of up to 70% while the BCI Competition II reached hit rates up to 88%, which is consistent with the literature.

The aim of this master’s thesis is to learn the problem of analysis and identification of significant spectral components in speech signal. Based on learning a special literature chooses the suitable methods of spectrum estimate. Does learning the literature in specification of testing of spectral components significate. Makes a procedure for identification of chosen speech formants. Does this procedure for audio signals both of in stress and in normal state. Estimates the results, compares efficiency of chosen methods and determine threshold for chosen formant of analyzed stress signal. States the recommendations for speech spectral analysis in stress situation.