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Related Experiment Video

Updated: Apr 15, 2026

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Characterization of mind wandering using fNIRS.

Gautier Durantin¹, Frederic Dehais², Arnaud Delorme³

¹Département Conception des Véhicules Aérospatiaux, Institut Supérieur de l'Aéronautique et de l'Espace Toulouse, France ; Centre de Recherche Cerveau et Cognition, Universite de Toulouse UPS, Toulouse, France ; CNRS, CerCo Toulouse, France.

Frontiers in Systems Neuroscience

|April 11, 2015

Summary

Functional near infrared spectroscopy (fNIRS) can detect mind wandering (MW) episodes by identifying brain activity in the medial prefrontal cortex. This non-invasive technique shows promise for educational and professional applications requiring sustained attention monitoring.

Keywords:

default mode network fNIRS linear discriminant analysis mind wandering

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Area of Science:

Cognitive Neuroscience
Neuroimaging Techniques

Background:

Assessing task attention is crucial for educational and professional settings.
Mind wandering (MW) refers to attentional drifts away from a primary task.
Existing neuroimaging methods for MW detection can be invasive or costly.

Purpose of the Study:

To evaluate the efficacy of functional near infrared spectroscopy (fNIRS) in detecting episodes of mind wandering.
To explore fNIRS as a non-invasive tool for monitoring attentional states.

Main Methods:

Utilized the Sustained Attention to Response Task (SART) to elicit mind wandering.
Collected 16-channel fNIRS data over the frontal cortices during task performance.
Analyzed fNIRS signals to identify neural correlates of MW.

Main Results:

Observed significant brain activation in the medial prefrontal cortex (mPFC) during MW episodes.
The mPFC is a key region within the default mode network (DMN).
fNIRS data allowed for the classification of MW episodes above chance level.

Conclusions:

fNIRS successfully detected default mode network activations associated with mind wandering.
This study demonstrates the potential of fNIRS as a viable, non-invasive method for MW detection.
Findings support the use of fNIRS in applications requiring attention monitoring.