Feedback control policies employed by people using intracortical brain–computer interfaces. (30th November 2016)

• Record Type: Journal Article
• Authors: Willett, Francis R; Pandarinath, Chethan; Jarosiewicz, Beata; Murphy, Brian A; Memberg, William D; Blabe, Christine H; Saab, Jad; Walter, Benjamin L; Sweet, Jennifer A; Miller, Jonathan P; Henderson, Jaimie M; Shenoy, Krishna V; Simeral, John D; Hochberg, Leigh R; Kirsch, Robert F; Ajiboye, A Bolu
• Is Part Of: Journal of neural engineering. Volume 14:Number 1(2017:Feb.)
• Publication Date: 2016-11-30
• Subjects: brain–computer interface -- motor cortex -- motor control; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2560/14/1/016001
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . When using an intracortical BCI (iBCI), users modulate their neural population activity to move an effector towards a target, stop accurately, and correct for movement errors. We call the rules that govern this modulation a 'feedback control policy'. A better understanding of these policies may inform the design of higher-performing neural decoders. Approach . We studied how three participants in the BrainGate2 pilot clinical trial used an iBCI to control a cursor in a 2D target acquisition task. Participants used a velocity decoder with exponential smoothing dynamics. Through offline analyses, we characterized the users' feedback control policies by modeling their neural activity as a function of cursor state and target position. We also tested whether users could adapt their policy to different decoder dynamics by varying the gain (speed scaling) and temporal smoothing parameters of the iBCI. Main results . We demonstrate that control policy assumptions made in previous studies do not fully describe the policies of our participants. To account for these discrepancies, we propose a new model that captures (1) how the user's neural population activity gradually declines as the cursor approaches the target from afar, then decreases more sharply as the cursor comes into contact with the target, (2) how the user makes constant feedback corrections even when the cursor is on top of the target, and (3) how the user actively accounts for the cursor's current velocity to avoid overshooting the target. Further, we show that users can adapt their control policy to decoder dynamics by attenuating neural modulation when the cursor gain is high and by damping the cursor velocity more strongly when the smoothing dynamics are high. Significance . Our control policy model may help to build better decoders, understand how neural activity varies during active iBCI control, and produce better simulations of closed-loop iBCI movements.