Detection of error related neuronal responses recorded by electrocorticography in humans during continuous movements

PLoS One. 2013;8(2):e55235. doi: 10.1371/journal.pone.0055235. Epub 2013 Feb 1.


Background: Brain-machine interfaces (BMIs) can translate the neuronal activity underlying a user's movement intention into movements of an artificial effector. In spite of continuous improvements, errors in movement decoding are still a major problem of current BMI systems. If the difference between the decoded and intended movements becomes noticeable, it may lead to an execution error. Outcome errors, where subjects fail to reach a certain movement goal, are also present during online BMI operation. Detecting such errors can be beneficial for BMI operation: (i) errors can be corrected online after being detected and (ii) adaptive BMI decoding algorithm can be updated to make fewer errors in the future.

Methodology/principal findings: Here, we show that error events can be detected from human electrocorticography (ECoG) during a continuous task with high precision, given a temporal tolerance of 300-400 milliseconds. We quantified the error detection accuracy and showed that, using only a small subset of 2×2 ECoG electrodes, 82% of detection information for outcome error and 74% of detection information for execution error available from all ECoG electrodes could be retained.

Conclusions/significance: The error detection method presented here could be used to correct errors made during BMI operation or to adapt a BMI algorithm to make fewer errors in the future. Furthermore, our results indicate that smaller ECoG implant could be used for error detection. Reducing the size of an ECoG electrode implant used for BMI decoding and error detection could significantly reduce the medical risk of implantation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Brain-Computer Interfaces / standards*
  • Cerebral Cortex / physiology*
  • Electrodes, Implanted
  • Electroencephalography / methods*
  • Humans
  • Movement / physiology*
  • Neurons / physiology*
  • Prostheses and Implants*
  • Psychomotor Performance / physiology

Grant support

This work was supported by the German Federal Ministry of Education and Research (BMBF) grant 01GQ0420 to BCCN Freiburg, BMBF 01GQ0830 grant to BFNT Freiburg and Tübingen, BMBF GoBio grant 0313891 and the Imperial College London. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.