Figure 4. Channel importance for grasp classification.

Saliency maps for the model used in real-time, 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. Electrodes overlayed with larger circles represent greater importance for grasp classification. White and transparent circles represent electrodes 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. For all confusion matrices, the percent value in each element of the matrix represents how many times the validation features across all repetitions of all validation folds were predicted correctly or incorrectly. The mean classification accuracy was computed from averaging the values on the diagonal of the confusion matrix. (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 Methods: Channel contributions). Offline classification accuracies from CV-models trained on all features from all channels were statistically higher than CV-models trained on features from channels only over cortical hand-knob (*P = 0.015, Wilcoxon Rank-Sum test with 3-way Bonferroni-Holm correction). Offline classification accuracies from CV-models trained on features from all channels except for channel 112 were not statistically different from those trained on features from all channels or features only from channels only over cortical hand-knob.