Evaluating the impact of channel count and feature set on online pattern recognition control of a virtual arm.

Pattern recognition is a commercially available strategy for transradial prosthesis control. Control performance depends on how many channels of electromyographic (EMG) signal are collected, as well as on the set of features extracted from the signal. Prior work has established common selections for channel count and feature set, importantly suggesting that there are diminishing returns in performance beyond eight EMG channels. However, these results are largely based on offline analyses of classifier error rate, rather than online control performance. This study aimed to evaluate the impact of EMG channel count and extracted feature set on online control of a virtual arm in a randomized, double-blind fashion. The primary metric of interest was Median Target Achievement Control Test Completion Time (TAC-CT), which is a measure of the time it takes a participant to guide a virtual avatar's wrist and hand into a target position. Thus, lower scores indicate better control. It was found that channel count had a significant impact on Median TAC-CT (p<0.001), but feature set did not (p=0.056). Across all feature sets, the 8-channel condition resulted in a clinically important reduction in Median TAC-CT over the 4-channel condition (-3.36s). The 16-channel condition also resulted in a clinically important reduction in Median TAC-CT over the 8-channel condition (-1.50s). Across channel count conditions, the effects of expanding the feature set were mixed. Overall, this work suggests that offline error analysis is, on its own, insufficient to assess online prosthesis control and that the addition of more than eight EMG channels may result in improved control. The authors aim to continue this line of research to further explore these phenomena.Clinical Relevance- This preliminary study demonstrates an improvement in online transradial pattern recognition control when using 16 EMG channels (versus eight EMG channels).