@inproceedings{
cubillos2024exploring,
title={Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces},
author={Luis Hernan Cubillos and Guy Revach and Matthew Mender and Joseph T Costello and Hisham Temmar and Aren Hite and Diksha Anoop Kumar Zutshi and Dylan Michael Wallace and Xiaoyong Ni and Madison M. Kelberman and Matt Willsey and Ruud Van Sloun and Nir Shlezinger and Parag Ganapati Patil and Anne Draelos and Cynthia Chestek},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=UDi51I8K1p}
}
People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the 'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying “trust” that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data, $n=13$ days) and online (real-time predictions, $n=5$ days) with a simple KF and two recent deep-learning algorithms: tcFNN (non-ReFIT version) and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite this trade-off, our analysis successfully integrates traditional controls and modern deep-learning approaches to motivate high-performing yet still explainable BMI designs.
Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces
Luis Hernan Cubillos Guy Revach Matthew Mender Joseph T Costello Hisham Temmar Aren Hite Diksha Anoop Kumar Zutshi Dylan Michael Wallace Xiaoyong Ni Madison M. Kelberman Matt Willsey Ruud Van Sloun Nir Shlezinger Parag Ganapati Patil Anne Draelos Cynthia Chestek