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.

Alpha and theta peak frequency track on- and off-thoughts.

Our thoughts are highly dynamic in their contents. At some points, our thoughts are related to external stimuli or tasks focusing on single content (on-single thoughts), While in other moments, they are drifting away with multiple simultaneous items as contents (off-multiple thoughts). Can such thought dynamics be tracked by corresponding neurodynamics? To address this question, here we track thought dynamics during post-stimulus periods by electroencephalogram (EEG) neurodynamics of alpha and theta peak frequency which, as based on the phase angle, must be distinguished from non-phase-based alpha and theta power. We show how, on the psychological level, on-off thoughts are highly predictive of single-multiple thought contents, respectively. Using EEG, on-single and off-multiple thoughts are mediated by opposite changes in the time courses of alpha (high in on-single but low in off-multiple thoughts) and theta (low in on-single but high in off-multiple thoughts) peak frequencies. In contrast, they cannot be distinguished by frequency power. Overall, these findings provide insight into how alpha and theta peak frequency with their phase-related processes track on- and off-thoughts dynamically. In short, neurodynamics track thought dynamics.

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.