Fig. 5. Channel importance for grasp classification.

Saliency maps for the model used online, a model using HG features from all channels except from channel 112, and a model using HG features only from channels covering cortical hand-knob are shown in (a), (c) and (e), respectively. Channels overlayed with larger and more opaque circles represent greater importance for grasp classification. White and transparent circles represent channels which were not used for model training. Mean confusion matrices from repeated 10-fold CV using models trained on HG features from all channels, all channels except for channel 112, and channels covering only the cortical hand-knob are shown in (b), (d), and (f), respectively. g Box and whisker plot showing the offline classification accuracies from 10 cross-validated testing folds using models with the above-mentioned channel subsets. Specifically, for one model configuration, each dot represents the average accuracy of the same validation fold across 20 repetitions of 10-fold CV (see Channel contributions and offline classification comparisons). Offline classification accuracies from CV-models trained on features from all channels were statistically higher than CV-models trained on features from channels only over cortical hand-knob (* P = 0.015, two-sided Wilcoxon Rank-Sum test with 3-way Holm-Bonferroni correction).