Proportional estimation of finger movements from high-density surface electromyography

Background: the importance to restore the hand function following an injury/disease of the nervous system led to the development of novel rehabilitation interventions. surface electromyography can be used to create a user-driven control of a rehabilitation robot,in which the subject needs to engage actively,by using spared voluntary activation to trigger the assistance of the robot. methods: the study investigated methods for the selective estimation of individual finger movements from high-density surface electromyographic signals (hd-semg) with minimal interference between movements of other fingers. regression was evaluated in online and offline control tests with nine healthy subjects (per test) using a linear discriminant analysis classifier (lda),a common spatial patterns proportional estimator (csp-pe),and a thresholding (thr) algorithm. in all tests,the subjects performed an isometric force tracking task guided by a moving visual marker indicating the contraction type (flexion/extension),desired activation level and the finger that should be moved. the outcome measures were mean square error (nmse) between the reference and generated trajectories normalized to the peak-to-peak value of the reference,the classification accuracy (ca),the mean amplitude of the false activations (mafa) and,in the offline tests only,the pearson correlation coefficient (pcorr). results: the offline tests demonstrated that,for the reduced number of electrodes (≤24),the csp-pe outperformed the lda with higher precision of proportional estimation and less crosstalk between the movement classes (e.g.,8 electrodes,median mafa ~ 0.6 vs. 1.1 %,median nmse ~ 4.3 vs. 5.5 %). the lda and the csp-pe performed similarly in the online tests (median nmse < 3.6 %,median mafa < 0.7 %),but the csp-pe provided a more stable performance across the tested conditions (less improvement between different sessions). furthermore,thr,exploiting topographical information about the single finger activity from hd-semg,provided in many cases a regression accuracy similar to that of the pattern recognition techniques,but the performance was not consistent across subjects and fingers. conclusions: the csp-pe is a method of choice for selective individual finger control with the limited number of electrodes (<24),whereas for the higher resolution of the recording,either method (cps-pa or lda) can be used with a similar performance. despite the abundance of detection points,the simple thr showed to be significantly worse compared to both pattern recognition/regression methods. nevertheless,thr is a simple method to apply (no training),and it could still give satisfactory performance in some subjects and/or simpler scenarios (e.g.,control of selected fingers). these conclusions are important for guiding future developments towards the clinical application of the methods for individual finger control in rehabilitation robotics. © 2016 the author(s).