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Related Experiment Video

Updated: Apr 18, 2026

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

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Predicting occurrence of errors during a Go/No-Go task from EEG signals using Support Vector Machine.

Shota Yamane, Isao Nambu, Yasuhiro Wada

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary
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    Predicting human errors in dairy operations is possible. Electroencephalograms (EEG) can detect neural patterns preceding motor errors, offering a novel approach to error prediction.

    Area of Science:

    • Neuroscience
    • Human-Computer Interaction
    • Cognitive Science

    Background:

    • Human error is a significant issue in various fields, including dairy operations.
    • Electroencephalography (EEG) studies suggest neural activity patterns (alpha, theta, beta bands) can precede attention and motor errors.
    • Predicting these errors could improve safety and efficiency.

    Purpose of the Study:

    • To investigate the accuracy of predicting subsequent motor errors using single-trial electroencephalogram (EEG) signals recorded before a response.
    • To examine the predictive capability of EEG data in a Go/No-Go task.

    Main Methods:

    • Ten subjects participated in a Go/No-Go task.
    • Support Vector Machine (SVM) classification was used to analyze EEG signals.

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    Last Updated: Apr 18, 2026

    Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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  • Classification accuracy was assessed for EEG data recorded 1000 ms before the Go/No-Go cue.

    • Main Results:

    • The mean classification accuracy for predicting errors was 62%.
    • Significant changes in frontal and occipital alpha-band power were associated with impending errors.
    • These findings indicate distinct neural signatures preceding motor errors.

    Conclusions:

    • EEG signals recorded prior to motor responses can predict future errors with moderate accuracy.
    • Alpha-band power in specific brain regions shows potential as a biomarker for error prediction.
    • This research opens avenues for developing real-time error detection systems.