A Comparison of Delay-and-Add and Maximum Likelihood Estimation for Velocity-Selective Recording Using Multi-Electrode Cuffs

• Published in: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 4127 - 4130
• Main Authors: Andreis, Felipe Rettore, Metcalfe, Benjamin, Al Muhamadee Janjua, Taha, Meijs, Suzan, Favretto, Mateus Andre, Jensen, Winnie, Dos Santos Nielsen, Thomas Gomes Norgaard
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2022
• Subjects: Channel estimation; Electromyography; Implants; Maximum likelihood estimation; Peripheral nervous system; Q-factor; Recording
• ISSN: 2694-0604
• DOI: 10.1109/EMBC48229.2022.9870897

Extracting information from the peripheral nervous system with implantable devices remains a significant challenge that limits the advancement of closed-loop neural prostheses. Linear electrode arrays can record neural signals with both temporal and spatial selectivity, and velocity selective recording using the delay-and-add algorithm can enable classification based on fibre type. The maximum likelihood estimation method also measures velocity and is frequently used in electromyography but has never been applied to electroneurography. Therefore, this study compares the two algorithms using in-vivo recordings of electrically evoked compound action potentials from the ulnar nerve of a pig. The performance of these algorithms was assessed using the velocity quality factor (Q-factor), computational time and the influence of the number of channels. The results show that the performance of both algorithms is significantly influenced by the number of channels in the recording array, with accuracies ranging from 77% with only two channels to 98% for 11 channels. Both algorithms were comparable in accuracy and Q-factor for all channels, with the delay-and-add having a slight advantage in the Q-factor.