Feedback control policies employed by people using intracortical brain–computer interfaces

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 two-dimensional 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.

Inferring single-trial neural population dynamics using sequential auto-encoders

“…This relationship is outlined in [34]; briefly, the Kalman filter can be viewed as a two step process -first smoothing the data, and subsequently performing a linear dimensionality reduction step that maps the smoothed, high-dimensional neural data onto kinematics. In the Kalman Filter the amount of smoothing is largely determined by the simple linear dynamical system (LDS) that models state evolution (i.e., models changes in kinematics).…”

Signal-independent noise in intracortical brain–computer interfaces causes movement time properties inconsistent with Fitts’ law

“…We reparameterized the Kalman filter to separate its dimensionality reduction step (linearly mapping the high-dimensional neural feature space to a two-dimensional control signal) from its filtering dynamics (gain, smoothing, and integrating velocity to get position) following the methods of (Willett et al 2017). This reparameterization allowed us to clearly isolate and define the cursor gain, which was important for testing Fitts’ law.…”

“…Our signal processing and neural feature extraction methods followed closely those of (Willett et al 2017; Jarosiewicz et al 2015). Every 20 millisecond time step, threshold crossing counts and power in the spike frequency band (250–5000 Hz) were computed for each channel.…”

“…To our knowledge, there is no direct evidence in the literature concerning the signal-dependency of unintended variability (“noise”) in the decoder output during control of an iBCI, though when discussing and simulating iBCI movements researchers often model the decoding noise as signal-independent (Willett et al 2017; Marathe and Taylor 2015; Rouse and Schieber 2015). …”

Review: Human Intracortical Recording and Neural Decoding for Brain–Computer Interfaces

“…For the purposes of BCI decoding in controlling assistive technologies, many researchers have focused on decoding neural activity from the motor cortex in both non-human primates (NHPs) [30]–[42] and early pilot clinical trials [11]–[15], [18]–[20], [23], [43]–[45]. …”