@inproceedings{b7b4f9d1c5a74aceb2cc091bf598923c,
title = "Evaluation of a minimally invasive endovascular neural interface for decoding motor activity",
abstract = "Endovascular devices like the Stentrode{\texttrademark} 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.",
author = "Forsyth, \{Ian A.\} and Megan Dunston and Gabriel Lombardi and Rind, \{Gil S.\} and Stephen Ronayne and Wong, \{Yan T.\} and May, \{Clive N.\} and Grayden, \{David B.\} and Thomas Oxley and Nicholas Opie and John, \{Sam E.\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 ; Conference date: 20-03-2019 Through 23-03-2019",
year = "2019",
month = may,
day = "16",
doi = "10.1109/NER.2019.8717000",
language = "English",
series = "International IEEE/EMBS Conference on Neural Engineering, NER",
publisher = "IEEE Computer Society",
pages = "750--753",
booktitle = "9th International IEEE EMBS Conference on Neural Engineering, NER 2019",
address = "United States",
}