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Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials

Fig 6

Accuracy from simulated standard calibration.

Simulated calibration represented as accuracies computed by increasing the number of trials of the training dataset (x-axis) and testing the classifier with a fixed test set of 200 trials. In order to have a confidence measure (shadowed areas), this procedure was repeated 10 times while shuffling the data. The horizontal solid line represents the accuracy plateau (also shown above the line), whereas the dashed vertical line shows the number of trials needed to reach the plateau.

Fig 6