Abstract
In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and poor performers, depending on how well they performed the task. Microstate features were derived from EEG recordings during resting and task states. In the good performers, there was a decrease in type C and an increase in type D features during the task compared to the resting state. Mean duration and occurrence decreased and increased, respectively. In the poor performers, occurrence of type D feature, mean duration and occurrence showed greater changes. We investigated whether microstate features were suitable for task performance classification and eleven features including four archetypes were selected by recursive feature elimination (RFE). The model that implemented them showed the highest classification performance for differentiating between groups. Our pilot findings showed that the highest mean Area Under Curve (AUC) was 0.831. This study is the first to apply EEG microstate features to specific cognitive tasks in healthy subjects, suggesting that EEG microstate features can reflect task achievement.
Highlights
That can be accurately measured with functional magnetic resonance imaging (fMRI) is limited[2]
We investigated the differences between known microstate features of good performers and poor performers during a mental arithmetic task
We demonstrate the usefulness of EEG microstate features in evaluating cognitive performance
Summary
That can be accurately measured with fMRI is limited[2]. a method to supplement the weaknesses of the information provided by fMRI may be helpful, and it is needed to study higher-order functions using high temporal resolution. A method of setting four prototypical microstates (types A, B, C, and D) in k-means clustering proposed by Lehmann et al has been applied in many studies that have led to a better understanding of neurobiological bases[12,24] This method has made it possible to show differences in microstates across groups and cognitive states by using the same archetype microstate features (i.e.; duration, occurrence, and coverage). The results of such analyses may have high interpretability because the functional significance of the four archetype microstates have been reported in several studies[25,26,27]. Four prototypical microstates are features that reveal their function and interpretation, making them suitable for developing models that can be applied to many diseases and tasks
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