Signal processing for non-invasive brain biomarkers of sensorimotor performance and brain monitoring 461 X Signal processing for non-invasive brain biomarkers of sensorimotor performance and brain monitoring Rodolphe J. Gentili, Hyuk Oh, Trent J. Bradberry, Bradley D. Hatfield and José L. Contreras-Vidal University of Maryland-College Park USA 1. Introduction Many endogenous and exogenous factors can affect the physiological, mental and behavioral states in humans. In order to identify such states, monitoring tools need to use biological indicators, or biomarkers, able to identify biological events and predict outcomes. These biomarkers can be divided into two categories. The first category contains what we could call the “structural” biomarkers that are extracted from physiological structures and mainly defined at the genetic and/or molecular level (e.g., Berg, 2008; Dengler et al., 2007; Eleuteri et al., 2009; Isaac, 2008; Moura et al., 2008; Wei, 2009). For instance, the formation or consumption of certain molecules provide biomarkers to identify patients with moderate to severe forms of cardiac heart failure (Eleuteri et al., 2009; Isaac, 2008) while changes in cortisol level allow detection of an increased stress response (Armstrong & Hatfield, 2006). Similarly, other active molecules (e.g., C-reactive protein) are used as biomarkers of valvular heart disease (Moura et al., 2008) while cardiac troponins and N-type natriuretic peptides can be used in post-transplant patient surveillance (Dengler et al., 2007). Other examples of structural biomarkers aim to identify abnormalities in neural connectivity in the brain. For instance, the presence of certain molecules in venous blood or a damaged white matter provides potential predictors of risk of cerebral palsy (Dammann & Leviton, 2004, 2006; Kaukola et al., 2004). Also, genomic and proteomic biomarkers are able to define the risk of an individual to develop a neurodegenerative disease such as Parkinson’s disease (Gasser, 2009), Alzheimer's disease (Berg, 2008; Wei, 2009) or amyotrophic lateral (Tuner et al., 2009) and multiple sclerosis (Wei, 2009). The second category includes what we could call “functional” biomarkers that are further related to continuous measurements of body function throughout time in order to track physiological, mental and behavioral states (e.g., Georgopoulos et al., 2007; Hejjel & Gál, 2001; Hofstra et al., 2008). For instance, electro-cardiograms, heartbeat, and body temperature are possible functional biomarkers to determine stress level (Hejjel & Gál, 2001). Body temperature can be used to detect the phase of circadian rhythms (Hofstra et al., 24