著者
熊野宏昭
所属
Waseda Univiersity
題材
計測法行動
被検体
ヒト
データ収集方法
実験モデル
専門分野
神経科学神経心理学
評価指標
認知課題自発脳波
キーワード
support vector machine regressionneuro-feedbackmind-wanderingmachine learningelectroencephalogram
概要

Kawashima I, Kumano H. Front Hum Neurosci. 2017 Jul 12;11:365. doi: 10.3389/fnhum.2017.00365. eCollection 2017.

Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

登録日
2018年10月11日 16:21
登録者
熊野宏昭