A passive BCI for monitoring the intentionality of the gaze-based moving object selection

Zhao, Darisy G.; Vasilyev, Anatoly N.; Kozyrskiy, Bogdan L.; Melnichuk, Eugeny V.; Isachenko, Andrey V.; Velichkovsky, Boris M.; Shishkin, Sergei L.
Journal of Neural Engineering, 8 January 2021, IOP Publishing

Objective. The use of an electroencephalogram (EEG) anticipation-related component, the expectancy wave (E-wave), in brain-machine interaction was proposed more than 50 years ago. This possibility was not explored for decades, but recently it was shown that voluntary attempts to select items using eye fixations, but not spontaneous eye fixations, are accompanied by the E-wave. Thus, the use of the E-wave detection was proposed for the enhancement of gaze interaction technology, which has a strong need for a mean to decide if a gaze behaviour is voluntary or not. Here, we attempted at estimating whether this approach can be used in the context of moving object selection through smooth pursuit eye movements. Approach. 18 participants selected, one by one, items which moved on a computer screen, by gazing at them. In separate runs, the participants performed tasks not related to voluntary selection but also provoking smooth pursuit. A low-cost consumer-grade eye tracker was used for item selection. Main results. A component resembling the E-wave was found in the averaged EEG segments time-locked to voluntary selection events of every participant. Linear discriminant analysis with shrinkage regularization (sLDA) classified the intentional and spontaneous smooth pursuit eye movements, using single-trial 300 ms long EEG segments, significantly above chance in eight participants. When the classifier output was averaged over ten subsequent data segments, median group ROC AUC of 0.75 was achieved. Significance. The results suggest the possible usefulness of the E-wave detection in the gazebased selection of moving items, e.g., in video games. This technique might be more effective when trial data can be averaged, thus it could be considered for use in passive interfaces, for example, in estimating the degree of the user’s involvement during gaze-based interaction.

Data Science
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© IOS Press. Personal use of this material is permitted. The definitive version of this paper was published in Journal of Neural Engineering, 8 January 2021, IOP Publishing and is available at : https://doi.org/10.1088/1741-2552/abda09
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