title: Predicting occurrence of errors during a Go/No-Go task from EEG signals using Support Vector Machine.
abstract: Human error often becomes a serious problem in dairy life. Recent studies have shown that failures of attention and motor errors can be captured before they actually occur in the alpha, theta, and beta-band powers of electroencephalograms (EEGs), suggesting the possibility that errors in motor responses can be predicted. The goal of this study was to use single-trial offline classification to examine how accurately EEG signals recorded before motor responses can predict subsequent errors. Ten subjects performed a Go/No-Go task, and the accuracy of error classification by a Support Vector Machine (SVM) was investigated 1000 ms before presenting the Go/No-Go cue. The resulting mean classification accuracy was 62%, and strong increases and decreases in activities associated with errors were observed in occipital and frontal alpha-band powers. This result suggests the possibility that future errors can be predicted using EEG.
authors.forename: Shota
authors.initials: S
authors.lastname: Yamane
authors.forename: Isao
authors.initials: I
authors.lastname: Nambu
authors.forename: Yasuhiro
authors.initials: Y
authors.lastname: Wada
mesh_terms: D001519:Behavior; D001921:Brain / Q000502:physiology; D001931:Brain Mapping; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008297:Male; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D013647:Task Performance and Analysis; D055815:Young Adult