Application of system identification methods for decoding imagined single-joint movements in an individual with high tetraplegia

Abstract

This study investigated the decoding of imagined arm movements from M1 in an individual with high level tetraplegia. The participant was instructed to imagine herself performing a series of single-joint arm movements, aided by the visual cue of an animate character performing these movements. System identification was used offline to predict the trajectories of the imagined movements and compare these predictions to the trajectories of the actual movements. We report rates of 25 - 50% for predicting completely imagined arm movements in the absence of a priori movements to aid in decoder building.

Figure 1

Color plot of electrode array where a group of four squares is an electrode (top left unit is unsorted) and individual squares are potentially sorted units with variance explained of wrist flexion position shown by cell color. Results are from one trial 966 days post-implant.

Figure 2

(Top) Recursive feature selection curves (mean ± SD) show the VAF% of the predicted position of the wrist during flexion / extension. 17 SUAs produced the maximal VAF% of 42%. (Bottom) Recursive feature selection was performed on each neural signal type, as well as on the combination of SUA and LFPstft signal type. Combining SUA and LFPstft in the same filter statistically significantly increased the predictive strength of the neural decoder (p < 0.05). Results are from one trial 966 days post-implant.

Figure 3

Results of recursive feature selection for all eight observed degrees of freedom. Combining SUA and LFPstft signal types produced neural filters with greater predictive strength than each signal individually. In cases where another individual signal type was the most informative (e.g. LFPrms in shoulder flexion / extension), the combined signal decoders performed as well or better than the other signal type. Results are from one trial 966 days post-implant.