Table 1.

Summary of EMG and force decoders. In order to assess the effectiveness of the adaptive EMG decoder, we compared it to an “optimal force” decoder based on regression between neurons and actual continuous force data. In order to identify the impact of limiting the decoder training to short data segments and using a gradient descent instead of a regression, we also tested “optimal target” and “adaptive force” decoders.

Decoder name	Training method	Supervisory signal	Data segments used	Rationale for testing
Optimal force	Regression	Actual force	Entire dataset (continuous)	Provides an upper bound performance level for force prediction or control.
Adaptive EMG	Gradient descent	Inferred EMG patterns	Target hold periods	Clinically applicable decoder. Provides muscle activation signals based only on motor intent without measuring EMG.
Optimal target	Regression	Target force	Target hold periods	Comparison to Adaptive EMG decoder revealed little difference between regression and adaptation, or inferred EMG rather than measured force.
Adaptive force	Gradient descent	Actual force	Target hold periods	Given the minimal effect of adaptation seen above, comparison to Optimal Force decoder revealed the large effect of using limited force training data.