A 'Total Unique Variation Analysis' for Brain-Machine Interfaces.

When designing a fully implantable brain-machine interface (BMI), the primary aim is to detect as much neural information as possible with as few channels as possible. In this paper, we present a total unique variance analysis (TUVA) for evaluating the signal unique to each channel that cannot be predicted by linear combination of signals on other channels. TUVA is a statistical method for determining the total unique variance in multidimensional data, ordering channels from most to least informative, to aid in the design of maximally-efficacious BMIs.

Cortical Auditory Evoked Potentials Recorded Directly Through the Cochlear Implant in Cochlear Implant Recipients: a Feasibility Study.

Objectives: The use of objective measures in cochlear implant (CI) mapping, has greatly contributed to the refinement of the setting of audible and comfortable stimulation levels, which serve as the basis of the mapping process, especially in cases of infants and young children. In addition, objective measures can also confirm the integrity of the CI system. Current CI objective measures mainly reflect neural activity from the auditory nerve and brainstem site.

Multi-frequency Electrocochleography Measurements can be Used to Monitor and Optimize Electrode Placement During Cochlear Implant Surgery.

Objective: To report the use of multi-frequency intra-cochlear electrocochleography (ECOG) in monitoring and optimizing electrode placement during cochlear implant surgery. An acoustic pure tone complex comprising of 250, 500, 1000, and 2000 Hz was used to elicit ECOG, or more specifically cochlear microphonics (CMs), responses from various locations in the cochlea. The most apical cochlear implant electrode was used as the recording electrode.

Near Perfect Neural Critic from Motor Cortical Activity Toward an Autonomously Updating Brain Machine Interface.

We are developing an autonomously updating brain machine interface (BMI) utilizing reinforcement learning principles. One aspect of this system is a neural critic that determines reward expectations from neural activity. This critic is then used to update a BMI decoder toward an improved performance from the user's perspective.

Toward an autonomous brain machine interface: integrating sensorimotor reward modulation and reinforcement learning.

For decades, neurophysiologists have worked on elucidating the function of the cortical sensorimotor control system from the standpoint of kinematics or dynamics. Recently, computational neuroscientists have developed models that can emulate changes seen in the primary motor cortex during learning. However, these simulations rely on the existence of a reward-like signal in the primary sensorimotor cortex.