Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms

Abstract

Pattern recognition is a useful tool for deciphering movement intent from myoelectric signals. Recognition paradigms must adapt with the user in order to be clinically viable over time. Most existing paradigms are static, although two forms of adaptation have received limited attention. Supervised adaptation can achieve high accuracy since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without knowledge of the intended class, thus achieving adaptation that is not cumbersome to the user, but at the cost of reduced accuracy. This study reports a novel adaptive experiment on eight subjects that allowed repeated measures post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 26% compared to the nonadapting classifier. Most unsupervised adaptation paradigms provided smaller reductions in error, due to frequent uncertainty of the correct class. One method that selected high-confidence samples showed the most practical implementation, although the other methods warrant future investigation. Supervised adaptation should be considered for incorporation into any clinically viable pattern recognition controller, and unsupervised adaptation should receive renewed interest in order to provide transparent adaptation.

Fig. 1

Electrode placement. A) 12 electrodes were placed in a circumferential array around the proximal third of the forearm for able-bodied subjects. B) Placement of the 12 electrodes was chosen for TMR subjects based on a high-density electrode array optimization.

Fig. 2

Sample Classification decisions. From top to bottom: real-time test (circles/dots), confidence of decisions (line), and samples tagged in post-hoc analysis for inclusion in the adaptive data set (numbers). Out of 11 classes, only class 3 was prompted during this snapshot (shown as circles) and four classes were predicted during the real-time test (shown with dots). Post-hoc adaptation paradigms selected different samples to add to the adaptive data set, based on criteria specific to each adaptation paradigm. All adaptation paradigms tagged samples from this snapshot except UB₂. Sometimes unsupervised adaptation strategies suggested the inclusion of a sample but incorrectly changed the class: these incorrectly tagged samples (class ≠ 3) are highlighted in dark gray.

Fig. 3

Examples of classifier error over time. Supervised and unsupervised classifier ranges are shaded, and two illustrative adaptive classifiers are highlighted in comparison to the non-adapting classifier: Supervised Low-confidence (SL) and Unsupervised High-confidence (UH). AB3's non-adapting classifier was stable over time, and her adaptive classifiers reduced this error as time progressed. TMR2's non-adapting classifier produced more error over time, but adaptive classifiers maintained a low level of error. Supervised adaptive classifiers typically had lower error than unsupervised adaptive classifiers for both of these subjects. Minimum error is the error produced by training and testing on the same real-time data. Minimum error was low for both subjects, suggesting that their patterns were consistent (minimal overlap in feature space) and that the adaptive classifiers could have further reduced error.