Brain-computer interfaces (BCls) decode recorded neural signals from the brain and/or stimulate the brain with encoded neural sig-nals. BCls span both hardware and software and have a wide range of applications in restorative medicine, from restoring movement through prostheses and robotic limbs to restoring sensation and communication through spellers. BCls also have applications in di-agnostic medicine, e.g., providing clinicians with data for detecting seizures, sleep patterns, or emotions. Despite their promise, BCls have not yet been adopted for long-term, day-to-day use because of challenges related to reliability and robustness, which are needed for safe operation in all scenarios. Ensuring safe operation currently requires hours of manual data collection and recalibration, involving both patients and clinicians. However, data collection is not targeted at eliminating specific faults in a BCI. This paper presents a new methodology for char-acterizing, detecting, and localizing faults in BCls. Specifically, it proposes partial test oracles as a method for detecting faults and slice functions as a method for localizing faults to characteristic patterns in the input data or relevant tasks performed by the user. Through targeted data acquisition and retraining, the proposed methodology improves the correctness of BCls. We evaluated the proposed methodology on five BCl applications. The results show that the proposed methodology (1) precisely localizes faults and (2) can significantly reduce the frequency of faults through retraining based on targeted, fault-based data acquisition. These results sug-gest that the proposed methodology is a promising step towards repairing faulty BCls.

@inproceedings{WinstonWWWWRJ22,
  title = {Repairing brain-computer interfaces with fault-based data acquisition},
  author = {Cailin Winston and Caleb Winston and Chloe N Winston and Claris Winston and Cleah Winston and Rajesh P N Rao and René Just},
  booktitle = {ICSE},
  year = {2022},
  doi = {10.1145/3510003.3512764}
}