| Abstract [eng] |
Consciousness is a multifaceted construct that spans a continuum from wakefulness—characterized by responsiveness to external stimuli and cognitive engagement—to altered states, such as fatigue, sleep, and anesthesia, where individuals may experience decreased cognitive function or difficulty being easily aroused. Given its wide range of real-life applications and its origin in brain, a significant amount of research has focused on studying consciousness using brain imaging techniques, particularly electroencephalography (EEG). However, most studies have relied on conventional multichannel EEG systems, which, while effective, are expensive and impractical for daily use by individuals. This thesis aims to develop EEG-based algorithms for monitoring consciousness levels in real-world applications, focusing on low-cost portable EEG devices that offer a more accessible solution for widespread use outside of laboratories and controlled environments. The central research question investigates how the limitation of spatial coverage in single channel EEG recordings can be compensated to capture changes in consciousness levels. The working hypothesis suggests that decomposing a single EEG signal into its sub-bands and applying nonlinear analysis can overcome these limitations by capturing distinct neural dynamics associated with different consciousness levels. By focusing on low-cost portable EEG devices, this work advances the field of EEG-based consciousness monitoring, making it more applicable for everyday use and accessible to a broader population. |
