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.

介绍了一种名为 DETRtime 的新框架，该框架利用 EEG 数据而无需额外记录眼球跟踪模式，实现了时间序列的分割，实现了多种眼球跟踪实验范式的最新最优性能，同时在 EEG 睡眠阶段的分割任务中得到了良好的泛化表现。