학술논문

Evaluation of a minimally invasive endovascular neural interface for decoding motor activity
Document Type
Conference
Source
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) Neural Engineering (NER), 2019 9th International IEEE/EMBS Conference on. :750-753 Mar, 2019
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Animals
Task analysis
Support vector machines
Arrays
Training
Australia
Decoding
Language
ISSN
1948-3554
Abstract
Endovascular devices like the Stentrode™ provide a minimally invasive approach to brain-machine-interfaces that mitigates safety concerns while maintaining good signal quality. Our research aims to evaluate the feasibility of using a stent-electrode array (Stentrode) to decode movements in sheep. In this study, two sheep were trained to perform an automated forced-choice task designed to elicit left and right head movement following an external stimulus. Cortical, movement-related signals were recorded using a Stentrode placed in the superior sagittal sinus adjacent to the motor cortex. Recorded brain signal was used to train a support vector machine classifier. Our results show that the Stentrode can be used to acquire motor-related brain signals to detect movement of the sheep in a forced-choice task. These results support the validity of using the Stentrode as a minimally invasive brain-machine interface.