Abstract

Sleep deprivation is an important and common problem with many consequences for mental and physical health. A sleep deprivation study was conducted to better understand the effects of insufficient sleep on cognitive functions and recovery. Studying specific spontaneous sleep waves occurring during the recovery sleep after sleep deprivation allows us to better understand the brain mechanism during sleep and its importance. The focus of my Master project is the detection of these sleep waves on scalp Electro-EncephaloGraphy (EEG) data, especially spindles, using different automatic detection algorithms and comparing them to a visual detection. Automated detection of these typical EEG discharges is challenging because many artifacts occur when EEG data are recorded within the Magnetic Resonance Imaging (MRI) scanner, so in presence of a large magnetic field. EEG data should be carefully processed from MRI related artifacts before considering detection of sleep specific discharges. In the second section of the thesis, we investigated within the whole brain the Blood Oxygen Level Dependent (BOLD) responses, measured with functional MRI, to identify brain areas involved in the generation of sleep spindles detected from scalp EEG. Preliminary results showed that automated detection was not accurate enough, because of residual artifact and other consequences of preprocessing. Moreover, visual detection is limited by the complexity of the EEG data. Therefore, there are expected improvements in post-review of the automated detection or investigating the optimal parameters for automated methods. The first BOLD response maps showed similarity between automated and visual detection and also with a study conducted by (Manuel Schabus et al. 2007). To conclude, further investigation on automated methods must be performed to find the best compromise between visual detection, automated detection, and post-review of automated detection.