Neural Engineering

Transformative Technologies

Work Package: WP7
Programme: P18
Deliverable: 18.1 EEG decoders of mind wandering.

Deliverable due date: month 30

This document reports on the progress of work on Deliverable 18.1. All the planned tasks related to this Deliverable have been accomplished. The first results were submitted and accepted in the 6th International Brain-Computer Interface Conference 2014 (published in September 2014, accessible at Consolidated results were submitted to “Journal of Neural Engineering” peer-reviewed journal in June 2015 and published in January 2016 (accessible at These publications are a scientific part of the Deliverable 18.1 and this “Project Deliverable Report” document focuses on its relation to the NETT proposal.
In the NETT 289146 Grant Annex, we stated in the context of Deliverable 18.1 and 18.2: “P18 is related to any rehabilitation therapy such as stroke or neurofeedback systems for attention deficit disorders, where a key point of the therapy is to move on only when the patient is attending to the task. In this context the objective is the modelling and real-time detection of human cognitive information of the rehabilitation task related to the degree of attention.” During the mid-term review this generic objective was further specified as “the need to detect mind wandering from EEG correlates.”
In the aforementioned publications we study the neural correlates of mind wandering during the execution of a motor task similar to a lower limb rehabilitation intervention. We designed an ecological protocol that combines three conditions (attending, deliberate mind wandering, and spontaneous mind wandering) and subjective reporting. Differences in EEG power show that mind wandering most strongly modulates the alpha activity, but there were also changes in the theta and beta bands. The largest difference was further pinpointed to the lower alpha band, relative to individual alpha peak. Based on this correlates, a brain-computer interface (BCI) was designed to discriminate between attention and deliberate mind wandering. The BCI was tested in offline and simulated online conditions and proved to be accurate 69% of the time.
In such a way we accomplish Deliverable 18.1 and proposed an EEG based detector of mind wandering and specific calibration procedures for this type of decoders.

Filip Melinščak, Luis Montesano, Javier Minguez