A 32-channel MCU-based feature extraction and classification for scalable on-node spike sorting

摘要

This paper describes a new hardware-efficient method and implementation for neural spike sorting based on selection of a channel-specific near-optimal subset of features given a larger predefined set. For each channel, realtime classification is achieved using a simple decision matrix that considers the features that provide the highest separability determined through off-line training. A 32-channel system for online feature extraction and classification has been implemented in an ARM Cortex-M0+ processor. Measured results of the hardware platform consumes 268μW per channel during spike sorting (includes detection). The proposed method provides at least x10 reduction in computational requirements compared to literature, while achieving an average classification error of less than 10% across wide range of datasets and noise levels.