Error-related potentials (ErrPs) are neurophysiological signals associated with error processing. They are generated when wrong actions are perceived and have been reported in many contexts in the past two decades, namely when a subject perceives that he/she has committed an error and recognizes it immediately (‘response ErrP’), when a subject receives the feedback of a previous choice without knowing whether it was wrong (‘feedback ErrP’), when observing mistakes of another person or agent (‘observation ErrP’)’ or during the interaction with a brain-computer interface (BCI) when the feedback is not the expected one (‘interaction ErrP’).
The components of an ErrP appear within a time window of 500 ms and are naturally elicited in the brain without the user’s explicit intention. Thus, its automatic detection can be used in myriad ways, in real-time, and in human-machine interaction processes. In particular, interaction and observation ErrPs have been applied as a proof-of-concept in several applications, for example, for detection and correction of BCI choices to increase reliability, to adapt BCI systems over the time, or to make intelligent systems (e.g., external agents) learn. There is also a growing interest for ErrPs in clinical applications for disorders where error monitoring is impaired.
Despite the successful use of ErrPs and other error related neural signatures, there are many challenges to overcome in order to make the most of error recognition, including for instance: 1) Low accuracy of single-trial classification; 2) Need of calibration; 3) Low generalization across applications/tasks; 4) Dependent of user engagement and user’s perception of error; and 5) Difficult to detect asynchronously in continuous tasks.
These challenges have been dictating the use of automatic error recognition mainly for highly controlled (unrealistic) applications/scenarios. However, the development of new machine learning techniques associated with new control strategies/paradigms and new neuroscience findings, opens up a new set of application possibilities in clinical neuroscience.
This Research Topic aims to discuss the challenges of integrating ErrPs and other neural signatures related to errors in human-machine interaction systems and to stimulate the research and contribution of new methodological solutions as well as their application in realistic clinical and non-clinical contexts.
All manuscripts must contain a critical analysis of addressed methodology or analysis tools, highlighting key contributions and achievements in Computational Intelligence and Neuroscience and indicate the author’s perspective for future developments.
Original Research, review papers, methods, perspective, conceptual analysis, brief research report, general commentary, opinion, and technology and Code manuscripts focused on the following topics are welcome:
- New machine learning methods to improve single-trial detection, generalization and calibration minimization;
- Hybrid approaches combining ErrPs, error-related frequency modulation in EEG, and others;
- New approaches/strategies to enhance user’s engagement, error perception, and detection in continuous control scenarios;
- New applications beyond the existing ones or still very unexplored in the fields robotics, assistive tools for motor neurorehabilitation and social and behaviour disorders (e.g., ADHD and Autism).
- Systematic reviews and/or metanalyses in this field will also be considered.
Topic Editor Christoph Guger is the founder and CEO of g.tec medical engineering GmbH. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Error-related potentials (ErrPs) are neurophysiological signals associated with error processing. They are generated when wrong actions are perceived and have been reported in many contexts in the past two decades, namely when a subject perceives that he/she has committed an error and recognizes it immediately (‘response ErrP’), when a subject receives the feedback of a previous choice without knowing whether it was wrong (‘feedback ErrP’), when observing mistakes of another person or agent (‘observation ErrP’)’ or during the interaction with a brain-computer interface (BCI) when the feedback is not the expected one (‘interaction ErrP’).
The components of an ErrP appear within a time window of 500 ms and are naturally elicited in the brain without the user’s explicit intention. Thus, its automatic detection can be used in myriad ways, in real-time, and in human-machine interaction processes. In particular, interaction and observation ErrPs have been applied as a proof-of-concept in several applications, for example, for detection and correction of BCI choices to increase reliability, to adapt BCI systems over the time, or to make intelligent systems (e.g., external agents) learn. There is also a growing interest for ErrPs in clinical applications for disorders where error monitoring is impaired.
Despite the successful use of ErrPs and other error related neural signatures, there are many challenges to overcome in order to make the most of error recognition, including for instance: 1) Low accuracy of single-trial classification; 2) Need of calibration; 3) Low generalization across applications/tasks; 4) Dependent of user engagement and user’s perception of error; and 5) Difficult to detect asynchronously in continuous tasks.
These challenges have been dictating the use of automatic error recognition mainly for highly controlled (unrealistic) applications/scenarios. However, the development of new machine learning techniques associated with new control strategies/paradigms and new neuroscience findings, opens up a new set of application possibilities in clinical neuroscience.
This Research Topic aims to discuss the challenges of integrating ErrPs and other neural signatures related to errors in human-machine interaction systems and to stimulate the research and contribution of new methodological solutions as well as their application in realistic clinical and non-clinical contexts.
All manuscripts must contain a critical analysis of addressed methodology or analysis tools, highlighting key contributions and achievements in Computational Intelligence and Neuroscience and indicate the author’s perspective for future developments.
Original Research, review papers, methods, perspective, conceptual analysis, brief research report, general commentary, opinion, and technology and Code manuscripts focused on the following topics are welcome:
- New machine learning methods to improve single-trial detection, generalization and calibration minimization;
- Hybrid approaches combining ErrPs, error-related frequency modulation in EEG, and others;
- New approaches/strategies to enhance user’s engagement, error perception, and detection in continuous control scenarios;
- New applications beyond the existing ones or still very unexplored in the fields robotics, assistive tools for motor neurorehabilitation and social and behaviour disorders (e.g., ADHD and Autism).
- Systematic reviews and/or metanalyses in this field will also be considered.
Topic Editor Christoph Guger is the founder and CEO of g.tec medical engineering GmbH. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
![Average EEG responses of 12 users for (A) incorrect [P2], (B) incorrect [P3], (C) correct [P2] and (D) correct [P3].](https://www.frontiersin.org/_rtmag/_next/image?url=https%3A%2F%2Fwww.frontiersin.org%2Ffiles%2FArticles%2F788258%2Ffnhum-15-788258-HTML%2Fimage_m%2Ffnhum-15-788258-g006.jpg&w=3840&q=75)
