Electrode clustering and bandpass analysis of eeg data for gaze estimation

In this study, we validate the findings of previously published papers, showing the feasibility of an Electroencephalography (EEG) based gaze estimation. Moreover, we extend previous research by demonstrating that with only a slight drop in model performance, we can significantly reduce the number of electrodes, indicating that a high-density, expensive EEG cap is not necessary for the purposes of EEG-based eye tracking. Using data-driven approaches, we establish which electrode clusters impact gaze estimation and how the different types of EEG data preprocessing affect the models’ performance. Finally, we also inspect which recorded frequencies are most important for the defined tasks

Aging effects and test-retest reliability of inhibitory control for saccadic eye movements

Neuropsychological studies indicate that healthy aging is associated with a decline of inhibitory control of attentional and behavioral systems. A widely accepted measure of inhibitory control is the antisaccade task that requires both the inhibition of a reflexive saccadic response toward a visual target and the initiation of a voluntary eye movement in the opposite direction. To better understand the nature of age-related differences in inhibitory control, we evaluated antisaccade task performance in 78 younger (20-35 years) and 78 older (60-80 years) participants. In order to provide reliable estimates of inhibitory control for individual subjects, we investigated test-retest reliability of the reaction time, error rate, saccadic gain, and peak saccadic velocity and further estimated latent, not directly observable processed contributing to changes in the antisaccade task execution. The intraclass correlation coefficients (ICCs) for an older group of participants emerged as good to excellent for most of our antisaccade task measures. Furthermore, using Bayesian multivariate models, we inspected age-related differences in the performances of healthy younger and older participants. The older group demonstrated higher error rates, longer reaction times, significantly more inhibition failures, and late prosaccades as compared with young adults. The consequently lower ability of older adults to voluntarily inhibit saccadic responses has been interpreted as an indicator of age-related inhibitory control decline. Additionally, we performed a Bayesian model comparison of used computational models and concluded that the Stochastic Early Reaction, Inhibition and Late Action (SERIA) model explains our data better than PRO-Stop-Antisaccade (PROSA) that does not incorporate a late decision process

A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications

The collection of eye gaze information provides a window into many critical aspects of human cognition, health and behaviour. Additionally, many neuroscientific studies complement the behavioural information gained from eye tracking with the high temporal resolution and neurophysiological markers provided by electroencephalography (EEG). One of the essential eye-tracking software processing steps is the segmentation of the continuous data stream into events relevant to eye-tracking applications, such as saccades, fixations, and blinks. Here, we introduce DETRtime, a novel framework for time-series segmentation that creates ocular event detectors that do not require additionally recorded eye-tracking modality and rely solely on EEG data. Our end-to-end deep learning-based framework brings recent advances in Computer Vision to the forefront of the times series segmentation of EEG data. DETRtime achieves state-of-the-art performance in ocular event detection across diverse eye-tracking experiment paradigms. In addition to that, we provide evidence that our model generalizes well in the task of EEG sleep stage segmentation