Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface

TL;DR

This paper introduces GaPP, a novel method for real-time tracking of neural adaptation in brain-machine interfaces, improving prediction accuracy of neural tuning changes during rapid brain control adaptation.

Contribution

The paper presents a globally adaptive point process method (GaPP) that effectively tracks fast neural tuning changes in brain control, outperforming existing slow-change tracking methods.

Findings

GaPP accurately predicts neural modulation states during brain control.

It identifies active neurons and their tuning property changes.

The method improves kinematic reconstruction speed and accuracy.

Abstract

Brain-machine interfaces (BMIs) help the disabled restore body functions by translating neural activity into digital commands to control external devices. Neural adaptation, where the brain signals change in response to external stimuli or movements, plays an important role in BMIs. When subjects purely use neural activity to brain-control a prosthesis, some neurons will actively explore a new tuning property to accomplish the movement task. The prediction of this neural tuning property can help subjects adapt more efficiently to brain control and maintain good decoding performance. Existing prediction methods track the slow change of the tuning property in the manual control, which is not suitable for the fast neural adaptation in brain control. In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework. We implement the method on real data from rats performing a two-lever discrimination task under manual control and brain control. The results show our method successfully predicts the neural modulation state and identifies the neurons that become active in brain control. Compared to existing methods, ours tracks the fast changes of the hyper preferred direction from manual control to brain control more accurately and efficiently and reconstructs the kinematics better and faster.