NeuralCLIP: A Modular FPGA-Based Neural Interface for Closed-Loop Operation

Abstract

The need for a miniaturized device that can perform closed-loop operation is imminent with the growing interest in brain-controlled devices and in stimulation to treat neural disorders. This work presents the Neural Closed-Loop Implantable Platform (NeuralCLIP), a modular FPGA-based device that can record neural signals, process them locally to detect an event and trigger neural stimulation based on the detection. Specifically, the NeuralCLIP is designed to record and process different neural signals in the frequency range between 20 Hz and 1 kHz. It is a flexible platform that can be reconfigured to optimize parameters like channel count and operation frequency based on the processing requirements. The signal-agnostic feature is demonstrated by testing the device with calibration signals from standard bio-signal emulators. The application focus for this device is a brain-computer-spinal interface (BCSI) which is demonstrated based on local field potential (LFP) signals recorded from a rat motor cortex. This work demonstrates recording and on-device processing of LFP signals to decode action intent and determine stimulation timing. The FPGA implementation of the device also targets development of low power algorithms for closed-loop operation.