Using a Neural Network Classifier to predict movement outcome from LFP signals

Simeng Zhang, Patrick K. Fahey, Mathew L. Rynes, Stephen J. Kerrigan, James Ashe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The dorsal lateral prefrontal cortex (DLPFC) is thought to be an integrative brain area for intelligent behavior in primates. It is responsible for motor planning, decision making, and has been shown to be involved with working memory. We studied the role of the DLPFC in controlling a complex instructed behavior, in which a non-human primate produced sequential arm movements punctuated by sequences of self-timed temporal intervals. We examined local field potentials (LFPs) recorded during an instruction period before the movement onset and used a Neural Network Classifier to predict whether the subject would perform the upcoming behavior correctly. The classifier was able to predict the outcomes of movement using LFPs during 3 scenarios: spatial error versus correct trial, temporal error versus correct trial, and spatial versus temporal error. The successful classification of the outcomes indicates that DLPFC LFPs can be used as a signal for cognitive neural prosthetics (CNPs).

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages981-984
Number of pages4
DOIs
StatePublished - 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

Fingerprint Dive into the research topics of 'Using a Neural Network Classifier to predict movement outcome from LFP signals'. Together they form a unique fingerprint.

Cite this