Spatio-spectral patterns based on Stein kernel for EEG signal classification
Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood-onset neurological disorder that can persist in adolescence and adult life, reducing concentration, memory, and productivity. The main drawback with mental health abnormalities of this type is the traditional diagnostic technique. Since...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | Colombia |
| Institución: | Universidad Tecnológica de Pereira |
| Repositorio: | Repositorio Institucional UTP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.utp.edu.co:11059/13163 |
| Acceso en línea: | https://hdl.handle.net/11059/13163 |
| Access Level: | acceso abierto |
| Palabra clave: | Neurofisiología Electroencefalografía Procesamiento de imágenes |
| Sumario: | Attention-Deficit/Hyperactivity Disorder (ADHD) is a childhood-onset neurological disorder that can persist in adolescence and adult life, reducing concentration, memory, and productivity. The main drawback with mental health abnormalities of this type is the traditional diagnostic technique. Since this is based exclusively on a symptomatological description without considering any biological data, leading to high overdiagnosis rates. To address the above problem, clinical researchers are attempting to extract ADHD biomarkers from recorded electroencephalographic (EEG) signals. Among the most common biomarkers are Theta/Beta Ratio and P300, of which recent studies have shown a lack of significance on the differences between ADHD and control subjects. Besides, another great challenge in EEG processing is given by the sensitivity of the signals, since they can be easily affected by background noise, muscle artifacts, head movements and flickering that greatly impair their quality, which limits its introduction into real world applications. This work proposes an EEG signal representation methodology for identifying subject-wise discrepancies of inhibitory responses, decoding the data structure, and supporting diagnosis of mental disorders. For this, first we develop a feature extraction approach based on the common spatial patterns (CSP) from EEG signals to support the ADHD diagnosis as show in Chapter... |
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