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Predicting Action Content On-Line and in Real Time before Action Onset - an Intracranial Human Study

Maoz, Uri and Ye, Shengxuan and Ross, Ian B. and Mamelak, Adam N. and Koch, Christof (2013) Predicting Action Content On-Line and in Real Time before Action Onset - an Intracranial Human Study. In: Advances in Neural Information Processing Systems 25 : 26th Annual Conference on Neural Information Processing Systems 2012. Curran Associates , Red Hook, NY, pp. 872-880. ISBN 162748003X http://resolver.caltech.edu/CaltechAUTHORS:20130816-103411425

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Abstract

The ability to predict action content from neural signals in real time before the action occurs has been long sought in the neuroscientific study of decision-making, agency and volition. On-line real-time (ORT) prediction is important for understanding the relation between neural correlates of decision-making and conscious, voluntary action as well as for brain-machine interfaces. Here, epilepsy patients, implantded with intracranial depth microelectodes or subdural grid electrodes for clinical purposes, participated in a "matching-pennies" game against an opponent. In each trial, subjects were given a 5 s countdown, after which they had to raise their left or right hand immediately as the "go" signal appeared on a computer screen. They won a fixed amount of money if they raised a different hand than their opponent and lost that amount otherwise. The question we here studied was the extent to which neural precursors of the subjects' decisions can be detected in intracranial local field potentials (LFP) prior to the onset of the action. We found that combinded low-frequency (0.1-5 Hz) LFP signals from 10 electrodes were predictive of the intended left-/right-hand movements before the onset of the go signal. Our ORT system predicted which hand the patient would raise 0.5 s before the go signal with 68% accuracy in two patients. Based on these results, we constructed an ORT system that tracked up to 30 electrodes simultaneously, and tested it on retrospective data from 7 patients. On average, we could predict the correct hand choice in 83% of the trials, which rose to 92% if we let the system drop 3/10 of the trials on which it was less confident. Out system demonstrates-for the first time-the feasibility of accurately predicting a binary action on single trials in real time for patients with intracranial recordings, well before the action occurs.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://books.nips.cc/papers/files/nips25/NIPS2012_0403.pdfOrganizationConference paper
Alternate Title:Poster: A System for Predicting Action Content On-Line and in Real Time before Action Onset in Humans – an Intracranial Study
Additional Information:We thank Ueli Rutishauser, Regan Blythe Towel, Liad Mudrik and Ralph Adolphs for meaningful discussions. This research was supported by the Ralph Schlaeger Charitable Foundation, Florida State University’s “Big Questions in Free Will” initiative and the G. Harold & Leila Y. Mathers Charitable Foundation. Poster M82: A System for Predicting Action Content On-Line and in Real Time before Action Onset in Humans – an Intracranial Study. December 03, 2012. Part of the Poster Session and Reception.
Group:Koch Laboratory, KLAB
Funders:
Funding AgencyGrant Number
Ralph Schlaeger Charitable FoundationUNSPECIFIED
Florida State University’s “Big Questions in Free Will” initiativeUNSPECIFIED
G. Harold and Leila Y. Mathers Charitable FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20130816-103411425
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20130816-103411425
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:40703
Collection:CaltechAUTHORS
Deposited By: KLAB Import
Deposited On:04 Mar 2013 22:29
Last Modified:13 Sep 2013 22:33

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