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Cortical neuroprostheses that employ repeated electrical stimulation of cortical areas with fixed stimulus parameters, are faced with the problem of large trial-by-trial variability of evoked potentials. This variability is caused by the ongoing cortical signal processing, but it is an unwanted phenomenon if one aims at imprinting neural activity as precisely as possible. Here, we use local field potentials measured by one microelectrode, located at a distance of 200 microns from the stimulation site, to drive the electrically evoked potential toward a desired target potential by real-time adaptation of the stimulus intensity. The functional relationship between ongoing cortical activity, evoked potential, and stimulus intensity was estimated by standard machine learning techniques (support vector regression with problem-specific kernel function) from a set of stimulation trials with randomly varied stimulus intensities. The smallest deviation from the target potential was achieved for low stimulus intensities. Further, the observed precision effect proved time sensitive, since it was abolished by introducing a delay between data acquisition and stimulation. These results indicate that local field potentials contain sufficient information about ongoing local signal processing to stabilize electrically evoked potentials. We anticipate that adaptive low intensity microstimulation will play an important role in future cortical prosthetic devices that aim at restoring lost sensory functions.