Rejection of Systemic and Operator Errors in a Real-Time Myoelectric Control Task

In myoelectric pattern-recognition control, the rejection of movement decisions based on confidence - the likelihood of a correct classification - has been shown to improve system usability, however it is not known to what extent this is due directly to error mitigation, and to what extent this is due to users having opportunities to change the way they contract. To understand this, 24 subjects participated in a real-time pattern recognition control task with rejection at seven different confidence thresholds, and without rejection. Errors were classified into systemic errors (i.e., those produced by the classifier) and operator errors (i.e., those produced by user behavior). It was found that the error permitted by the rejection controller was reduced by about half at high rejection thresholds, with both systemic and operator errors significantly affected, while the errors produced by the user remained essentially constant throughout. Conversely, correct decisions were filtered out by the rejection controller at significantly greater rates at high rejection thresholds, which may be excessive enough to ultimately impair usability. While some subjects reported being experienced in myoelectric control, no significant differences were observed due to experience level.