Making brain–machine interfaces robust to future neural variability

Citations Over TimeTop 10% of 2016 papers

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 data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is 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 are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime.

Related Papers

• → Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction(2019)71 cited
• → Artificial Intelligence, Machine Learning, and Medicine: A Little Background Goes a Long Way Toward Understanding(2021)29 cited
• → Application of Machine Learning in Animal Disease Analysis and Prediction(2020)26 cited
• → Sentiment Analysis by Using Supervised Machine Learning and Deep Learning Approaches(2020)3 cited
• → Breakdown of Machine Learning Algorithms(2022)1 cited