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Creating A Closed Loop Neuroelectric Interface via Cortical Electrodes in Rats to Investigate Optimal Learning Techniques for Human EEG Neuroelectric Devices

Introduction The gold standard for cortical neuroprosthetics is a non-invasive interface that will allow humans to control devices using the activity of their own brain. Specifically, the NeuroEngineering lab at NASA Ames Research Center is investigating optimal algorithms and training paradigms for allowing human subjects to modulate the electroencephalogram (EEG) of their brains as a signal for controlling devices and piloting aircraft. The NeuroEngineering lab at NASA Ames has made much progress in the decoding of the EEG signal, but there are many other types of signals in the brain that can also be used as control signals. The range of usable signals in the brain, in increasing order of volume of brain tissue being sampled, includes recordings of single cell electric discharges, summed population activity of thousands of neurons via local field potentials (LFPs), and electrical activity recorded from either the surface of the brain via electrocorticograms (ECoGs) or the surface of the scalp via electroencephalograms (EEGs). Many investigators are researching each of these signals as a control signal, both experimentally in animal subjects and clinically in human patients (Trejo et. However, two select issues remain to be addressed. First, the relationships among all the available brain signals are not well established. Second, when using any of the available brain signals as a control signal, a question arises as to whether signals showing intrinsic modulation should be used as control signals or whether a signal feature should be arbitrarily chosen as the control signal and then autoshaped (trained). My proposed experiments will investigate both these themes by using an innovative preparation relying on much of our lab's current research. Two groups of rats will be taught motor cortex neural control tasks using two fundamentally different algorithms, hereby called the " a priori " algorithm and the " seek and find " algorithm. The " a priori " algorithm will arbitrarily designate a feature of a cortical signal and autoshape (train) the feature to modulate in response to a cue. The " seek and find " algorithm will use an adaptive weighting scheme to find which features in a cortical signal contain the most robust response to a cue. While the rats are being trained to modulate their extracellular neuronal activity, the LFPs and ECoGs will be simultaneously recorded from the same location on the extracellular recording array. Such data will be highly relevant to human neuroprosthetic devices, as the …