Improved decoding methods to reduce reaction time in brain - machine interface systems

2007; Lebedev et al., 2006; Mussa-Ivaldi et al., 2003, Nicolelis et al., 2009, Schwartz et al., 2006). Development of BMI systems In recent years, there has been much development in the quality of recordings extracted from neuronal ensembles with the aim of creating improved BMIs to drive neuroprosthetics. Initially, single-electrode implants in the brain showed promise for providing the source of signals to drive arti!cial devices in restoration of mobility after paralysis (Schmidt et al., 1980). Advancing on this technique, the development of the novel electrophysiological model of multi-electrode recordings, such as the Utah Intracortical Electrode Array (UIEA) emerged (Maynard, et al., 1996). "e UIEA demonstrated the ability of neuronal populations to perform control tasks and showed that the number of neurons present in a recording is signi!cant, as recordings from small populations of neurons rather than single units are more reliable for brain-computer interface application. "e introduction of this e#ective model was almost simultaneous with the development of BMIs (Schmidt, 1980). Chronic implants containing multielectrode arrays in multiple cortical areas of the rhesus monkeys brain are now able to record extracellular electrical activity of hundreds of neurons (Carmena, et al., 2003; Fitzsimmons et al., 2009; Lebedev et al., 2005; Nicolelis et al., 2003; Lebedev & Nicolelis, 2011). With these novel electrophysiological techniques executing simultaneous Introduction Sensorimotor defects resulting from neurologic injuries, diseases, or limb loss a#ect millions of people worldwide. In the United States alone, !ve million people are currently a$icted with some form of paralysis according to data from Medical News Today (Paddock, 2009). Such paralyzing disorders substantially limit independence, mobility, and communication. Despite severe motor de!cits due to damage to the spinal cord, nerves, or muscles, many patients retain fully intact cortical and subcortical motor networks that are capable of motor processing (Mattia et al., 2009). "ese areas can adapt to new controls due to innate brain plasticity (Hosp & Luft, 2003; Winstein et al., 2003). To bypass the site of neural lesion, activity from healthy motor regions such as M1 or S1 can be connected to a neural prosthetic through an interface, called a brainmachine interface (BMI) (Lebedev & Nicolelis, 2006). "us, arti!cial actuators such as an exoskeleton or arti!cial limbutilizing neurophysiological signals from undamaged components of the central nervous system allow for direct interaction between the brain and the outside world (Andersen, et al., 2004; Jackson et al., 2004; Lebedev et al., 2006). Many research groups are currently pursuing this goal with the hope that BMIs with increasingly sophisticated technologies and decoding strategies will serve to augment partial and full body mobility in paralyzed patients (Andersen, et al., 2004; Birbaumer et al., 2007; Fetz, Improved decoding methods to reduce