Figure 5.

Feature ensemble CSIM force and volitional state decoding accuracies as a function of window length. Offline decoding accuracies were computed using an LDA classifier implemented within a 10-dimensional CSIM representation of the neural feature data, using 10-fold cross-validation. 10-dimensional CSIM data of window lengths ranging from 100 ms to 3000 ms were passed to the LDA, as described in the Methods. Each window began at the start of the go phase and ended at the time point indicated on the x-axis. For participant T8, each panel shows session-averaged decoding performances from each participant-grasp pair. The T8 power and pincer panels were averaged over 5 and 3 sessions, respectively. Standard deviations across T8 sessions are indicated by the dotted lines. Gray line indicates the upper boundary of the 95% empirical confidence interval of the chance distribution, estimated using 10,000 random shuffles of the trial labels.