Towards error-free interaction

Human-machine interaction (HMI) relies on pattern recognition algorithms that are not perfect. To improve the performance and usability of these systems we can utilize the neural mechanisms in the human brain dealing with error awareness. This study aims at designing a practical error detection algorithm using electroencephalogram signals that can be integrated in an HMI system. Thus, real-time operation, customization, and operation convenience are important. We address these requirements in an experimental framework simulating machine errors. Our results confirm the presence of brain potentials related to processing of machine errors. These are used to implement an error detection algorithm emphasizing the differences in error processing on a per subject basis. The proposed algorithm uses the individual best bipolar combination of electrode sites and requires short calibration. The single-trial error detection performance on six subjects, characterized by the area under the ROC curve ranges from 0.75 to 0.98.