A 128-Channel FPGA-Based Real-Time Spike-Sorting Bidirectional Closed-Loop Neural Interface System.

A multichannel neural interface system is an important tool for various types of neuroscientific studies. For the electrical interface with a biological system, high-precision high-speed data recording and various types of stimulation capability are required. In addition, real-time signal processing is an important feature in the implementation of a real-time closed-loop system without unwanted substantial delay for feedback stimulation. Online spike sorting, the process of assigning neural spikes to an identified group of neurons or clusters, is a necessary step to make a closed-loop path in real time, but massive memory-space requirements commonly limit hardware implementations. Here, we present a 128-channel field-programmable gate array (FPGA)-based real-time closed-loop bidirectional neural interface system. The system supports 128 channels for simultaneous signal recording and eight selectable channels for stimulation. A modular 64-channel analog front-end (AFE) provides scalability and a parameterized specification of the AFE supports the recording of various electrophysiological signal types with 1.59 ± 0.76 root-mean-square noise. The stimulator supports both voltage-controlled and current-controlled arbitrarily shaped waveforms with the programmable amplitude and duration of pulse. An empirical algorithm for online real-time spike sorting is implemented in an FPGA. The spike-sorting is performed by template matching, and templates are created by an online real-time unsupervised learning process. A memory saving technique, called dynamic cache organizing, is proposed to reduce the memory requirement down to 6 kbit per channel and modular implementation improves the scalability for further extensions.

ZigBee-based Wireless Neuro-Stimulator for Improving Stroke Recovery.

Stroke is a leading cause of adult disability and the second-leading cause of death in Korea. It is also the third-leading cause of death in the United States, leading to a serious demand for new interventions to improve the quality of life in stroke survivors. To this end, direct cortical stimulation using an epidural electrode has been reported with promising results in animal and human studies, showing the potential for enhancing the recovery in chronic stroke patients. For optimal results, doctors must be able to modify the stimulation pattern as frequently as needed over a period of time for a given patient. However, severe aftereffects caused by stroke limit patients' activities, making regular doctor visits for treatment difficult. This study aims to develop a prototype of a telemedicine system to enhance stroke recovery by using a ZigBeebased wireless neuro-stimulator. The ZigBee is a stable platform for many low-power wireless applications. To allow stroke patients to remotely obtain neuro-stimulation treatments from their doctors, we connected the ZigBee to the internet. The system also allows doctors to personalize treatment based on the history of the stimulation parameters. The system developed here can also be beneficial as a common platform for a wide range of brain diseases and clinical care for which electric stimulation is used.