Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)
Uri Maoz, Shengxuan Ye, Ian Ross, Adam Mamelak, Christof Koch
The ability to predict action content from neural signals in real time before the ac- tion occurs has been long sought in the neuroscientiﬁc study of decision-making, agency and volition. On-line real-time (ORT) prediction is important for under- standing the relation between neural correlates of decision-making and conscious, voluntary action as well as for brain-machine interfaces. Here, epilepsy patients, implanted with intracranial depth microelectrodes 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 combined 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±3% 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. Our 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.