Modular particle filtering FPGA hardware architecture for brain machine interfaces

Abstract

As the computational complexities of neural decoding algorithms for brain machine interfaces (BMI) increase, their implementation through sequential processors becomes prohibitive for real-time applications. This work presents the field programmable gate array (FPGA) as an alternative to sequential processors for BMIs. The reprogrammable hardware architecture of the FPGA provides a near optimal platform for performing parallel computations in real-time. The scalability and reconfigurability of the FPGA accommodates diverse sets of neural ensembles and a variety of decoding algorithms. Throughput is significantly increased by decomposing computations into independent parallel hardware modules on the FPGA. This increase in throughput is demonstrated through a parallel hardware implementation of the auxiliary particle filtering signal processing algorithm.