EEG-Based Recognition Method of Intentional and Unintentional Hand Movement

Wang, Xiaoguang; Wang, Jiarong; Bi, Luzheng; Fei, Weijie

doi:10.1109/cac48633.2019.8996446

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…The rationale behind this central hypothesis is derived from the human factor theory of unintentional causation [41,42], which states that slip-based human error can result from an inappropriate response or activity. The ability to detect unintentional hand movement, which reached over 87% accuracy when analyzing EEG signals [33], supports this hypothesis. Thus, EEG signals differ in intended vs unintended hand movement.…”

Section: Main Hypothesissupporting

confidence: 53%

“…The ACDS system when compared to the literature shows higher acceptability, accessibility, and accuracy. Even though using EEG signals and eye movement show the ability to detect slip-based human error with an accuracy of 86.4% [33] and 84.5% [34], respectively, the applications may be limited, as they require either special equipment to be placed on one's scalp to capture the EEG signals that are susceptible to noise given the low amplitude EEG signals have, measured in the micro volt, or require a live stream capture from a camera at all times. Therefore, utilizing the sensors that a typical smartphone already has in order to detect slip-based human error may be a better accepted solution to the general public.…”

Section: Discussionmentioning

confidence: 99%

“…Because most unintentional-based human error involves a human movement, often hand movement, the authors in [33] investigated the ability to differentiate between intentional and unintentional hand movement, which can help detect unintentional clicks. The authors ran an experiment on seven individuals and collected EEG data.…”

Section: Unintentional-based Human Errormentioning

confidence: 99%

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Micro-Behavioral Accidental Click Detection System for Preventing Slip-Based Human Error

Almehmadi¹

2021

Sensors

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Accidentally clicking on a link is a type of human error known as a slip in which a user unintentionally performs an unintended task. The risk magnitude is the probability of occurrences of such error with a possible substantial effect to which even experienced individuals are susceptible. Phishing attacks take advantage of slip-based human error by attacking psychological aspects of the users that lead to unintentionally clicking on phishing links. Such actions may lead to installing tracking software, downloading malware or viruses, or stealing private, sensitive information, to list a few. Therefore, a system is needed that detects whether a click on a link is intentional or unintentional and, if unintentional, can then prevent it. This paper proposes a micro-behavioral accidental click detection system (ACDS) to prevent slip-based human error. A within-subject-based experiment was conducted with 20 participants to test the potential of the proposed system. The results reveal the statistical significance between the two cases of intentional vs. unintentional clicks using a smartphone. Random tree, random forest, and support vector machine classifiers were used, exhibiting 82.6%, 87.2%, and 91.6% accuracy in detecting unintentional clicks, respectively.

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Section: Main Hypothesissupporting

confidence: 53%

Section: Discussionmentioning

confidence: 99%

Section: Unintentional-based Human Errormentioning

confidence: 99%

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