Degree Granting Department
Stephen E. Saddow
Action Potential, Brain Stimulation, Embedded Neural Interface, Low Noise Recording System, Neural Recording
This work presents a high-voltage, high-precision bi-directional multi-channel system capable of stimulating neural activity through bi-phasic pulses of amplitude up to ∓50 V while recording very low-voltage responses as low as tens of microvolts. Most of the systems reported from the scientific community possess at least one of the following common limitations: low stimulation voltages, low gain capabilities, or insufficient bandwidth to acquire a wide range of different neural activities.
While systems can be found that present remarkable capabilities in one or more specific areas, a versatile system that performs over all these aspects is missing. Moreover, as many novel materials, like silicon carbide, are emerging as biocompatible interfaces, and more specifically as neuronal interfaces, it becomes mandatory to have a system operating across a wide range of voltages and frequencies for both physiological and electrical compatibility testing. The system designed and proven during this doctoral research effort features a ∓50 V bi-phasic pulse generator, 62 to 100 dB of software selectable amplification, and a wide 18 Hz to 12 kHz bandwidth.
In addition to design and realization we report about biological testing consisting in the acquisition of neural signals from tissue cultures using an MEA where faithful signal recording was achieved with superior fidelity to a commercial system used to sample signals from the same culture. The only system parameter that was less robust than the commercial system was the noise level, which due to our higher bandwidth was somewhat expected. More importantly our custom electronics outperformed in terms of lower delay and lower cost of realization. All of these results plus suggested future works are listed for the reader's convenience.
Scholar Commons Citation
Abbati, Luca, "Development of a Bi-Directional Electronics Platform for Advanced Neural Applications" (2012). USF Tampa Graduate Theses and Dissertations.