SmartEye: Developing a Novel Eye Tracking System for Quantitative Assessment of Oculomotor Abnormalities
Eye movements are a continuous and ubiquitous part of sensory perception. To properly generate highly accurate and co-ordinate ocular movements, a vast network of brain areas are engaged, from low-level visual processing to motor control of gaze orientation. This renders oculomotor system vulnerable to various neurological disorders with unique clinical patterns.
A Spatially Focused Method for High Density Electrode-Based Functional Brain Mapping Applications
Mapping the electric field of the brain with electrodes benefits from its superior temporal resolution but is prone to low spatial resolution property comparing with other modalities such as fMRI, which can directly impact the precision of clinical diagnosis. Simulations show that dense arrays with straightforwardly miniaturized electrodes in terms of size and pitch may not improve the spatial resolution but only strengthen the cross coupling between adjacent channels due to volume conduction.
Semiparametric Identification of Human Arm Dynamics for Flexible Control of a Functional Electrical Stimulation Neuroprosthesis
We present a method to identify the dynamics of a human arm controlled by an implanted functional electrical stimulation neuroprosthesis. The method uses Gaussian process regression to predict shoulder and elbow torques given the shoulder and elbow joint positions and velocities and the electrical stimulation inputs to muscles.
Position-Independent Decoding of Movement Intention for Proportional Myoelectric Interfaces
In this decade, myoelectric interfaces based on pattern recognition have gained considerable attention thanks to their naturalness enabling human intentions to be conveyed to and in control of a machine. However, the high variations of electromyogram signal patterns caused by arm position changes prohibit application to the real world.
Automatic Parametrization of Somatosensory Evoked Potentials With Chirp Modeling
In this paper, an approach using polynomial phase chirp signals to model somatosensory evoked potentials (SEPs) is proposed. SEP waveforms are assumed as impulses undergoing group velocity dispersion while propagating along a multipath neural connection. Mathematical analysis of pulse dispersion resulting in chirp signals is performed. An automatic parameterization of SEPs is proposed using chirp models.
Real-Time Detection and Monitoring of Acute Brain Injury Utilizing Evoked Electroencephalographic Potentials
Rapid detection and diagnosis of a traumatic brain injury (TBI) can significantly improve the prognosis for recovery. Helmet-mounted sensors that detect impact severity based on measurements of acceleration or pressure show promise for aiding triage and transport decisions in active, field environments such as professional sports or military combat. The detected signals, however, report on the mechanics of an impact rather than directly indicating the presence and severity of an injury.