Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 64 for “"EEG Signals"”.
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Motor imagery classification using sparse representation of EEG signals
… brain is able to generate responses to the signals it receives, and transmit messages to the body. Some neural disorders can impair the communication between the brain and the body preventing the transmission of these messages. Brain Computer Interfaces (BCIs) are devices that hold immense …
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An analysis of EEG signals present during target search
… This technique involves classifying a users EEG (Electroencephalography) signals as they view images presented on a screen. The premise is that images (targets) that arouse a subjects attention generate distinct brain responses, and these brain responses can then be used to label the images. …
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Topic Modeling for Inferring Brain States from Electroencephalography (EEG) Signals
<p>Inferring brain states from EEG signals allows for the management of sleep disorders and brain diseases by providing an insight into the electrophysiological state of the brain. We explore the use of topic modeling – which are popular text processing algorithms – to infer brain states from EEG …
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Improving Golf Putt Performance with Statistical Learning of EEG Signals
… of golfers based on their electroencephalogram (EEG) data. The method can be used as a core building block of a brain-computer interface, which is designed to provide guidance to golf players based on their EEG patterns. The proposed method includes three steps. First, multi-channel 1-second EEG …
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Event detection in EEG signals for brain computer interface using expectation-maximation algorithm
… disorders. BCI works by recording brain signals (example; electroencephalography (EEG)) and translating them into machine-understandable language. Most of the current BCI systems identify features of the brain signals and classify them according to a predefined criterion set by the …
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Prediction of the Outcome in Cardiac Arrest Patients Undergoing Hypothermia Using EEG Wavelet Entropy
… of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients’ outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band …
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EEG subspace analysis and classification using principal angles for brain-computer interfaces
… of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to …
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Stacked generalization for early diagnosis of Alzheimer's disease
… and other signal processing methods to analyze EEG signals in an attempt to find a noninvasive biomarker for AD. In this study, multiresolution wavelet analysis was performed on event related potentials (ERPs) of electroencephalogram (EEG) signals. Extracted feature sets were then used to train …
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A Physiological Signal Processing System for Optimal Engagement and Attention Detection.
… system which uses fundamental physiological signals such as the Electrocardiograph (ECG), to analyze and predict the presence or lack of cognitive attention in individuals during task execution. The primary focus of this study is to identify the correlation between fluctuating level of …
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Efficiency evaluation of external environments control using bio-signals
There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing …
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Comparison and Development of Algorithms for Motor Imagery Classification in EEG- based Brain-Computer Interfaces
… imagery can be detected and classified from EEG signals. The motivation of the present work was to compare several algorithms for motor imagery classification in EEG signals as well as to test several novel algorithms. The algorithms tested included the popular method of common spatial …
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Early Detection of Neurodegenerative Diseases from Bio-Signals: A Machine Learning Approach
… I aimed at analysing the gait signals as well as EEG signals, separately, as both of these signals severely get affected by any neurological disease.The first part of this research work focuses on the discrimination analysis of gait signals of different neurodegenerative …
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Classification of ADHD Using Heterogeneity Classes and Attention Network Task Timing
… to further develop a potential method of using EEG signals to accurately discriminate between ADHD and non-ADHD children using features that capture spectral and perhaps temporal information from evoked EEG signals. KNN has been shown in prior research to be an effective tool in discriminating …
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EEG Feature Extraction and Pattern Recognition Based on Chaotic Systems
… studies have reported the chaotic nature of EEG (Electroencephalogram) signals and various feature extraction techniques for pattern recognition. This work is an effort to investigate the complex underlying dynamics of chaotic systems and to develop a machine learning based pattern …
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A preliminary design of an integrated non-invasive brain recording and stimulation device
… a device to amplify and digitize high frequency EEG signals up to 1 KHz. The other portion is to create a device to apply controlled and arbitrary current stimulation. This project has the potential to enhance human memory formation, an essential ability for people in everyday life. Along the …
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Toward Robust and Generalizable Spatiotemporal Modeling for Tasks beyond Forecasting and Classification
… irregularities in both traffic forecasting and EEG signal classification tasks. textbf{(2) Domain Adaptation:} We design and evaluate domain adaptation strategies that enable robust cross-patient EEG classification, addressing inter-subject variability and enhancing generalization across diverse …
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Events prediction in electroencephalographic signals
… operates by transforming electrophysiological signals, known as Electroencephalogram (EEG) signals, from the user into device commands under an operating protocol. The protocol initialises and defines the nature of the communication (i.e., discrete or continuous). It also determines the …
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Complexity Analysis of Physiological Time Series with Applications to Neonatal Sleep Electroencephalogram Signals
… to neonatal sleep electroencephalography (EEG) signals. Complexity analysis is applied to two clinical data sets of neonatal sleep Electroencephalography(EEG) time series, to uncover the evolution of signal dynamics and its relationship to neurodevelopment and maturation. A review of the …
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