Classification techniques are useful for processing complex signals into labels with semantic value. For example, they can be used to interpret brain signals generated by humans corresponding to a finite set of commands for a physical device. The classifier, however, may interpret the signal as a command that is different from the intended one. This error in classification leads to poor performance in tasks where the class labels are used to learn some information or to control a physical device. We propose a computationally efficient algorithm to identify which class labels may be misclassified out of a sequence of class labels, when these labels are used in a given learning or control task. The algorithm is based on inference methods using Markov random fields. We apply the algorithm to goal-learning and tracking using brain-computer interfacing (BCI), in which signals from the brain are commonly processed using classification techniques. We demonstrate that the proposed algorithm reduces the time taken to identify the goal state in control experiments.
|Title of host publication||IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Number of pages||7|
|State||Published - Dec 13 2017|
|Event||2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada|
Duration: Sep 24 2017 → Sep 28 2017
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Conference||2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017|
|Period||9/24/17 → 9/28/17|
Bibliographical noteFunding Information:
This work was supported by grants from AFRL (#FA8650-15-C-2546), DARPA (#W911NF-16-1-0001), ARO (#W911NF-15-1-0592), and NSF (#1550212 and #1652113).
© 2017 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications