Detecting Error-Related Negativity for Interaction Design

C.T Vi, S Subramanian

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

46 Citations (Scopus)
392 Downloads (Pure)


This paper examines the ability to detect a characteristic brain potential called the Error-Related Negativity (ERN) using off-the-shelf headsets and explores its applicability to HCI. ERN is triggered when a user either makes a mistake or the application behaves differently from their expectation. We first show that ERN can be seen on signals captured by EEG headsets like Emotiv™ when doing a typical multiple choice reaction time (RT) task – Flanker task. We then present a single-trial online ERN algorithm that works by pre-computing the coefficient matrix of a logistic regression classifier using some data from a multiple choice reaction time task and uses it to classify incoming signals of that task on a single trial of data. We apply it to an interactive selection task that involved users selecting an object under time pressure. Furthermore the study was conducted in a typical office environment with ambient noise. Our results show that online single trial ERN detection is possible using off-the-shelf headsets during tasks that are typical of interactive applications. We then design a Superflick experiment with an integrated module mimicking an ERN detector to evaluate the accuracy of detecting ERN in the context of assisting users in interactive tasks. Based on these results we discuss and present several HCI scenarios for use of ERN.
Translated title of the contributionDetecting Error-Related Negativity for Interaction Design
Original languageEnglish
Title of host publicationin proceedings of 30th International Conference on Human Factors in Computing Systems (CHI 2012)
Number of pages10
Publication statusPublished - May 2012

Bibliographical note

Conference Organiser: ACM


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  • Detecting Error-Related Negativity for Interaction Design

    Vi, C. T. & Subramanian, S., 2012, p. 493-502. 10 p.

    Research output: Contribution to conferenceConference Paperpeer-review

    46 Citations (Scopus)

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