Making brain-machine interfaces robust to future neural variability

TL;DR

This paper presents a new neural network decoder for brain-machine interfaces that is trained on diverse data to improve robustness against neural variability, potentially reducing the need for frequent retraining.

Contribution

The authors introduce a multiplicative recurrent neural network decoder trained on varied and synthetic data, enhancing robustness to neural recording changes in BMIs.

Findings

Decoder outperforms Kalman filter under neural variability.

Training on diverse data improves robustness.

Effective in non-human primate preclinical models.

Abstract

A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to- kinematic mappings and became more robust with larger training datasets. When tested with a non-human primate preclinical BMI model, this decoder was robust under conditions that disabled a state-of-the-art Kalman filter based decoder. These results validate a new BMI strategy in which accumulated data history is effectively harnessed, and may facilitate reliable daily BMI use by reducing decoder retraining downtime.