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

Abstract

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 behavior 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. Eighteen 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 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 gaze-based 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.

Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking

“…The eye movement signals have also been capitalized on to realize hands-free intuitive interfaces for selecting the moving targets [7], [8], [9] or still targets [15]. In particular, our study concentrates on selecting still targets, and the development of such gazed-based interface suffers from Midas Touch problem [10], i.e., not every location we fixate is related to the target.…”

“…For this reason, the dual-task paradigm has been widely adopted [29], [30], [31] to simulate the highly cognitive-demanding operation scenarios in real world, where the performance measurements of the primary task reflect the operator's manipulation ability under high cognitive load, and those of a secondary task provide objective information about the operator's cognitive load. Most previous studies have investigated the BCI-based and gaze-based control performance in the singletask paradigm [7], [8], [9], [32], [33], [34], [35], whereas few efforts have been made in a more demanding dual-task paradigm where the operator's both hands are involved until very recently.…”

“…Harnessing the gaze to infer the operator's intended onscreen commands promises to be a natural control interface for human-MRS interactions [7], [8], [9], [10]. Nevertheless, accuracy of gaze-based input is found to largely depend on the target size [11].…”

On a Moving Target Selection Model in Virtual Reality Based on Decision Trees

“…However, most of the current researches focus on static targets selection [13][14][15][16]. In recent years, for the problem of moving target selection, some scholars have enhanced pointing by extending the alternative targets or setting up more easily achievable agents for each target [17].…”

Improving the performance of a gaze independent P300-BCI by using the expectancy wave

“…In order to improve the efficiency of BCI using covert attention, this paper attempts to introduce expectancy wave (E-wave) into the traditional P300-BCI as additional identification information. The E-wave is related to anticipation of an upcoming stimulus [23], typically as a part of the contingent negative variation (CNV). There have been many reports on the use of this component in BCI.…”