UZH-Logo

Real time decoding of hand grasping signals from macaque premotor and parietal cortex


Townsend, B R; Subasi, E; Scherberger, H (2008). Real time decoding of hand grasping signals from macaque premotor and parietal cortex. In: 13th International FES Society Conference, Freiburg, Germany, 21 September 2008 - 25 September 2008, 203-205.

Abstract

A brain machine interface (BMI) for visually guided grasping would provide significant benefits for paralyzed patients, given the crucial role these movements play in our everyday life. We have developed a BMI to decode grasp shape in real-time in macaque monkeys. Neural activity was evaluated using chronically implanted elec-trodes in the anterior intraparietal cortex (AIP) and ventral premotor cortex (F5), areas that are known to be in-volved in the transformation of visual signals into hand grasping instructions. In a first study, we decoded two grasp types (power and precision grip) and three grasp orientations (target oriented vertically or tilted left or right) from the neural activity during movement planning with an accuracy of about 70%. These results are proof-of-concept for a BMI for visually guided grasping that could be extended for larger number of grip types and grip orientations, as needed for prosthetic applications in humans.

A brain machine interface (BMI) for visually guided grasping would provide significant benefits for paralyzed patients, given the crucial role these movements play in our everyday life. We have developed a BMI to decode grasp shape in real-time in macaque monkeys. Neural activity was evaluated using chronically implanted elec-trodes in the anterior intraparietal cortex (AIP) and ventral premotor cortex (F5), areas that are known to be in-volved in the transformation of visual signals into hand grasping instructions. In a first study, we decoded two grasp types (power and precision grip) and three grasp orientations (target oriented vertically or tilted left or right) from the neural activity during movement planning with an accuracy of about 70%. These results are proof-of-concept for a BMI for visually guided grasping that could be extended for larger number of grip types and grip orientations, as needed for prosthetic applications in humans.

Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:25 September 2008
Deposited On:11 Mar 2009 08:35
Last Modified:05 Apr 2016 13:10
Related URLs:http://www.ifess2008.de/conferences_en/ifess-2008
Other Identification Number:ini:20321

Download

Full text not available from this repository.

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations