Efficient Compression Techniques and Hardware Implementation for High‐Channel‐Count Brain Neurochemical Recording
Fast-scan cyclic voltammetry (FSCV) at a carbon-fiber microelectrode (CFM) is a powerful monitoring modality for real-time probing of endogenous neurotransmitters in the brain of behaving animals due to its exquisite spatiotemporal and chemical resolution. Great strides have been made in developing implantable FSCV-based microsystems for high-fidelity wireless sensing of brain neurochemistry, with a particular focus on dopamine signaling.
However, one major shortcoming of the existing wireless microsystems is the low number of FSCV-recording channels. This limitation arises from high data rates that will have to be supported by the wireless link for transmission, if the number of FSCV-recording channels is significantly increased. Another limitation is lack of signal processing in order to differentiate neurochemicals from interferents, in the current recording microsystems. Processing of brain neurochemistry signals is an essential step, specifically, in activity-dependent neuromodulation microsystems, which combine neural recording, signal processing, and microstimulation in a single device for closed-loop interfacing with the nervous system.
The main focus of this research work is to design and develop algorithms and hardware dedicated to integrated microsystems that perform high-site-density recording of brain neurochemistry with high fidelity. In particular, first, we propose compressive FSCV (C-FSCV) and compressed principal component regression (C-PCR) algorithms for FSCV data rate reduction and hardware-efficient differentiation of neurochemicals from interferents, respectively. Next, we develop a digital signal processing (DSP) unit, called Compressive processor, which implemented C-FSCV and C-PCR for real-time functionality. Finally, we develop a neurochemical recording microsystem which employed analog background-current subtraction technique in addition to Compressive processor for recording of neurochemicals using FSCV at a CFM.
