Fig. 4

Decoding hand and wrist movements in subjects with severe hand impairment (UEFM-HS < 3). A Left: Comparison of severe (UEFM-HS < 3) and mild (UEFM-HS ≥ 3) subject impairment average movement scores (Average movement score: unpaired t-test UEFM-HS < 3 vs. UEFM-HS ≥ 3, p = 0.02). Right: Comparison of NN decoding performance for severe and mild subject impairments (Decoding accuracy: unpaired t-test UEFM-HS < 3 vs. UEFM-HS ≥ 3, p = 0.006). B Decoding performance of NN binary classifier for UEFM-HS < 3 subjects comparing Rest and Move in which Move is made up of combining all 12 movements into a single class. Confusion matrix of subject 61,204 for the two-class problem. The observed movement score is the average of all movements observed movement scores. The two-class decoder can reliably distinguish the difference between a resting and moving state. C Decoding performance of NN model when restricting classes to Rest, Hand Close, and Hand Open. Confusion matrix of lowest performing subject (61,204) for the three-class problem. The three-class decoder is not sufficient to distinguish the movements reliably. D Decoding performance of NN model when restricting classes to Rest and the top 2 movements for each subject for a total of three classes. Confusion matrix of subject 61,204 for the three-class problem. Focusing on movements specific to subjects increases the robustness of decoder performance