Abstract
Objective. Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: (1) error magnitude, (2) distribution of different frequency components in the decoding errors, (3) processing delays, and (4) command gain. Approach. To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains. Main results. Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present. Significance. This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported here also provide an efficient way to compare a diverse range of decoding options offline.
Highlights
Advances in assistive technology provide disabled individuals the opportunity to control a wide variety of devices using movement commands decoded directly from neural signals or from retained motor activity measured via electromyograms
Seven human subjects performed a target acquisition task on a computer screen using a closed-loop BMI simulator where different types of noise, delays, and gains were added to the cursor motion to simulate the combined effect of imperfect decoding and the response properties of various BMI systems
The BMI simulator was used here because, unlike actual BMI systems, the closed-loop simulator allowed us to systematically vary four key command/device features over a range of values in all subjects
Summary
Advances in assistive technology provide disabled individuals the opportunity to control a wide variety of devices using movement commands decoded directly from neural signals or from retained motor activity measured via electromyograms. Translating neuromotor signals into one’s intended movement is an imperfect process that inevitably results in some errors in the decoded commands. The spatiotemporal characteristics of the command errors will depend on the quality and type of neuromotor signals used and the choice of signal processing and decoding methods applied. The assistive device being controlled may contribute additional errors and delays to the intended movement creating further discrepancies between the user’s intent and the resulting device motion. While the human visuomotor system is well equipped to correct for the type of errors that typically occur during visually-guided reaching, our natural error correction response may be suboptimal or even counterproductive for these human-machine systems with unnatural spatiotemporal error characteristics. A better understanding of how these command error features can positively or negatively impact one’s ability to correct for the resulting movement errors will facilitate optimization of command systems for closed-loop control
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