Machine Learning Evaluation of Passive Wireless Neurosensing Recorder for Biopotentials Recognition Conference

Gutierrez-Hernandez, M, Moncion, C, Bojja-Venkatakrishnan, S et al. (2022). Machine Learning Evaluation of Passive Wireless Neurosensing Recorder for Biopotentials Recognition . 1072-1073. 10.1109/AP-S/USNC-URSI47032.2022.9887107

cited authors

  • Gutierrez-Hernandez, M; Moncion, C; Bojja-Venkatakrishnan, S; Volakis, JL

abstract

  • Neuropotentials monitoring can help individuals to significantly enhance their physical and mental well-being. We present an evaluation of a multichannel, passive and fully implantable wireless neurosensing system (WiNS). WiNS employs radiofrequency and optical communications to address the need for non-battery-operated systems. In this study, we will present a new automated technique to identify signal segments eliminating the difficulty of manual classification of evoked biopotentials. In addition, machine learning algorithms are adopted to evaluate signal quality from WiNS and compare it with a commercially available wired system. Somatosensory evoked potential data measured from wired and our wireless systems shows < 6% deviation in machine learning testing accuracy, indicating successful detection of biopotential signal as low as ╬╝Vpp. These results support the concept that real-time machine interface for wireless and passive acquisition of biopotentials is indeed feasible translating to several uses for future clinical research.

publication date

  • January 1, 2022

International Standard Book Number (ISBN) 13

start page

  • 1072

end page

  • 1073