AbstractsPsychology

Lapses in responsiveness (‘lapses’), especially behavioural microsleeps (‘microsleeps’) and attention lapses, involve complete disruption of performance from 0.5 to 15 s and can result in injury or death, especially in the transport sector (e.g., pilots, air-traffic controllers, truck and car drivers, etc.). The existence of a real-time monitoring system for detecting lapses could reduce accidents and save lives. The Christchurch Neurotechnology Research Programme (www.neurotech.org.nz) is a leader in lapse research in terms of characterization and EEG-based detection of microsleeps. Despite its achievements, there is still some way to go toward developing a system capable of accurately detecting, let alone predicting, microsleeps in the real world. Functional magnetic resonance imaging (fMRI) has shown increases and decreases in blood oxygen level dependent (BOLD) activity in certain regions of the brain prior to and during microsleeps. During microsleeps, the BOLD signal (and, hence, neural activity) decreases in the thalamus and posterior cingulate cortex but increases in several cortical regions, including the inferior frontal cortex, posterior parietal cortex, and occipital cortex. Furthermore, the extent of decreases in neural activity in the thalamus increases with the duration of microsleeps. Therefore, identifying regions with increased or decreased activity based on EEG functional imaging would provide more information for current EEG-based microsleep detectors. A limitation of current EEG-based detectors of microsleeps is that analysis is performed on sensor-space EEG data which does not, and cannot, take advantage of the dipolar pattern of brain source signals. Hence, they cannot estimate signals from specific regions in the brain related to microsleeps, such as the thalamus. Synchronous activation and alignment of neurons produces electrical currents and changes in potentials on the scalp which can be recorded by EEG sensors, and these sources can be modelled via head-modelling techniques as equivalent current dipole sources. The electrical activity of these sources is not distributed uniformly on the scalp and has a dipolar pattern. Therefore, techniques such as spatial filters, which assume a dipolar constraint, can be used to reconstruct EEG source-space signals, i.e., reconstruct the time-series of virtual voxels. In this way, it is possible to reconstruct/enhance the signal of different brain regions and/or identify which brain regions have activations/deactivations associated with microsleeps. In this project, the aim was to perform EEG-based functional imaging of the brain so as to enhance brain activity and identify brain locations which have changes in activity during microsleeps. A literature review identified potential signal processing techniques, including spatial filters and head-modelling techniques, which might have application in the reconstruction of brain electrical activity in 3D source-space rather than EEG sensor-space. Once the appropriate techniques from minimum-variance and…