Generative Decoding of Intracortical Neuronal Signals for Online Control of Robotic Arm to Intercept Moving Objects. (June 2020)

• Record Type: Journal Article
• Authors: Li, Chenyang; Zhang, Yiheng; Wang, Tianwei; Xu, Xinxiu; Wang, Qifan; Xu, Bradley; Cui, He
• Is Part Of: Journal of physics. Volume 1576(2020)
• Publication Date: 2020-06
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1576/1/012057
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Brain-machine interfaces (BMIs) are being developed for translating neural signals to control an external device such as a robotic arm. The prevalent decoding strategy of current BMIs is the continuously conversion of neural spike trains to motor variables via a discriminative algorithm, but this typically results in un-natural and slow robotic motion. Recently, we implemented a BMI under feedforward control for interception of moving targets, though the decoding accuracy was insufficient for accurate online control. To improve the BMI performance, a generative algorithm called Latent Factor Analysis via Dynamical Systems (LFADS) [1], was applied to data pre-processing for de-noising. The results indicate potential advantages of feedforward control with generative decoding in improving BMI design.