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A System for Predicting Action Content On-Line and in Real Time before Action Onset in Humans – an Intracranial Study
Uri M Maoz · Shengxuan Ye · Ian Ross · Adam Mamelak · Christof Koch

Mon Dec 03 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

The ability to predict action content from neural signals in real time before action onset 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. Here, epilepsy patients, implanted with intracranial depth microelectrodes or subdural grid electrodes for clinical purposes, participated in a “matching-pennies” game against either the experimenter or a computer. In each trial, subjects were given a 5s 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 working hypothesis of this experiment was that neural precursors of the subjects’ decisions precede action onset and potentially also the awareness of the decision to move, and that these signals could be detected in intracranial local field potentials (LFP). We found that low-frequency LFP signals from a combination of 10 channels, especially bilateral anterior cingulate cortex and supplementary motor area, 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 0.5s before the go signal with 68±3% accuracy in two patients. Based on these results, we constructed an ORT system that tracked up to 30 channels simultaneously, and tested it on retrospective data from 6 patients. On average, we could predict the correct hand choice in 80% of the trials, which rose to 90% correct if we let the system drop about 1/3 of the trials on which it was less confident. Our system demonstrates – for the first time – the feasibility of accurately predicting a binary action in real time for patients with intracranial recordings, well before the action occurs.

Author Information

Uri M Maoz (Caltech)
Shengxuan Ye (Caltech)
Ian Ross (Huntington Memorial Hospital)
Adam Mamelak (Cedars Sinai Medical Center)
Christof Koch (Allen Institute for Brain Science)

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