makes it difficult to diagnose microsleep events objectively. In one study neural activity underlying MSs was investigated by simultaneously measuring eye video, response behavior, EEG, and fMRI in normally-rested individuals engaged in a sensory-motor task. Twenty participants tracked a visual stimulus with a joystick for 50 minutes in 2 dimensions (up/down/right/left) on a computer screen. Participants performed this task in an fMRI scanner such that joystick response, right eye-video, EEG (60 EEG electrodes), and fMRI data were recorded simultaneously. Most participants had frequent microsleeps (>35) in a continuous visuomotor task (tracking visual stimulus on a screen), corresponding with decreased activity in arousal-related brain regions over time (thalamus, midbrain, and the posterior cingulate cortex). Another study examined the activation patterns of 5 people who woke up from microsleeps in a simulated driving experiment. It was found that upon awakening the visual area, frontal cortex, limbic lobe were activated (in the intense activation phase) and the frontal cortex, temporal cortex, primary motor area, and insula were activated (in the post abrupt awakening phase). Therefore, the study concluded that decision-making was not activated immediately upon waking up from a MS episode, likely increasing risk of injury in intense decision-making tasks like driving or surgery. The transition from wakefulness to sleep is regulated by a variety of chemicals. Adenosine likely causes the 'feeling sleepy' side of microsleeps, while dopamine likely reduces microsleep events by promoting wakefulness. It has been shown that microsleeps correlate with spontaneous pontine-geniculate-occipital (PGO waves) waves, which suppress visual processing in the basal ganglia. When this pathway is not activated, cells in the superior colliculus (which causes release of dopamine) cannot be dis-inhibited via the basal ganglia, leading to poor processing ability and microsleep onset. Detection methods and classifications There are currently many ways to detect microsleeps; however, there is a lack of general consensus as to the best way to identify and classify microsleeps. The simplest methods to detect these events seem to be through psychological tests, speech tests, and behavioral tests (e.g. yawn test and eye-video test). More complex and expensive ways to detect microsleeps include EEG, fMRI, EOG, and PSG tied to various software platforms. When multiple tests are used in parallel, detection of microsleeps most likely will become more accurate. Despite attempts to globally classify microsleeps through these detection methods (with particular emphasis on EEG and slow eyelid closure tests), there is great variability in the types