Brain Network Reorganization During Visual Search Task Revealed by a Network Analysis of Fixation-Related Potential.

Visual search is ubiquitous in daily life and has attracted substantial research interest over the past decades. Although accumulating evidence has suggested complex neurocognitive processes underlying visual search, the neural communication across the brain regions remains poorly understood. The present work aimed to fill this gap by investigating functional networks of fixation-related potential (FRP) during the visual search task. Multi-frequency electroencephalogram (EEG) networks were constructed from 70 university students (male/female = 35/35) using FRPs time-locked to target and non-target fixation onsets, which were determined by concurrent eye-tracking data. Then graph theoretical analysis (GTA) and a data-driven classification framework were employed to quantitatively reveal the divergent reorganization between target and non-target FRPs. We found distinct network architectures between target and non-target mainly in the delta and theta bands. More importantly, we achieved a classification accuracy of 92.74% for target and non-target discrimination using both global and nodal network features. In line with the results of GTA, we found that the integration corresponding to target and non-target FRPs significantly differed, while the nodal features contributing most to classification performance primarily resided in the occipital and parietal-temporal areas. Interestingly, we revealed that females exhibited significantly higher local efficiency in delta band when focusing on the search task. In summary, these results provide some of the first quantitative insights into the underlying brain interaction patterns during the visual search process.

Lane-change intention prediction using eye-tracking technology: A systematic review.

The aim of this study is to identify the best practices and future research directions for driver lane-change intention (DLCI) prediction using eye-tracking technologies based on a systematic literature review. We searched five academic literature databases and then conducted an in-depth review, structured coding, and analysis of 40 relevant articles. The literature on DLCI prediction is summarized in terms of input features, feature extraction and prediction time windows, labeling methods, and machine learning algorithms. The results show that eye tracking data features along with other data sources can be useful inputs for the prediction of DLCI. Major challenges in this line of research include determining the optimal time window for feature extraction and developing and evaluating the appropriate machine learning algorithm. Suggestions for future research and practice for DLCI prediction in intelligent vehicles are discussed.

Using eye-controlled highlighting techniques to support both serial and parallel processing in visual search.

Recent research has developed two eye-controlled highlighting techniques, namely, block highlight display (BHD) and single highlight display (SHD), that enhance information presentation based on a user's current gaze position. The present research aimed to investigate how these techniques facilitate mental processing of users' visual search in high information-density visual environments. In Experiment 1, 60 participants performed 3-, 6-, 9-, and 12-icon visual search tasks. The search times significantly increased as the number of icons increased with the SHD but not with the BHD. In Experiment 2, 40 participants performed a 49-icon visual search task. The search time was faster, and the fixation spatial density was lower with the BHD than with the SHD. These results suggested that the BHD supported parallel processing in the highlighted area and serial processing in the broader display area; thus, the BHD improved search performance compared to the SHD, which primarily supported serial processing.

The applicability of eye-controlled highlighting to the field of visual searching.

OBJECTIVE: With the increasing amount of information presented on current human-computer interfaces, eye-controlled highlighting has been proposed, as a new display technique, to optimise users' task performances. However, it is unknown to what extent the eye-controlled highlighting display facilitates visual search performance. The current study examined the facilitative effect of eye-controlled highlighting display technique on visual search with two major attributes of visual stimuli: stimulus type and the visual similarity between targets and distractors. METHOD: In Experiment 1, we used digits and Chinese words as materials to explore the generalisation of the facilitative effect of the eye-controlled highlighting. In Experiment 2, we used Chinese words to examine the effect of target-distractor similarity on the facilitation of eye-controlled highlighting display. RESULTS: The eye-controlling highlighting display improved visual search performance when words were used as searching target and when the target-distractor similarity was high. No facilitative effect was found when digits were used as searching target or target-distractor similarity was low. CONCLUSIONS: The effectiveness of the eye-controlled highlighting on a visual task was influenced by both stimulus type and target-distractor similarity. These findings provided guidelines for modern interface design with eye-based displays implemented.