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

Lukas Wolf, Ard Kastrati, Martyna B Plomecka, Jie-Ming Li, Dustin Klebe, Alexander Veicht, Roger Wattenhofer, Nicolas Langer
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23912-23932, 2022.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-wolf22a, title = {A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications}, author = {Wolf, Lukas and Kastrati, Ard and Plomecka, Martyna B and Li, Jie-Ming and Klebe, Dustin and Veicht, Alexander and Wattenhofer, Roger and Langer, Nicolas}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {23912--23932}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/wolf22a/wolf22a.pdf}, url = {https://proceedings.mlr.press/v162/wolf22a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications %A Lukas Wolf %A Ard Kastrati %A Martyna B Plomecka %A Jie-Ming Li %A Dustin Klebe %A Alexander Veicht %A Roger Wattenhofer %A Nicolas Langer %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-wolf22a %I PMLR %P 23912--23932 %U https://proceedings.mlr.press/v162/wolf22a.html %V 162 %X 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.
APA
Wolf, L., Kastrati, A., Plomecka, M.B., Li, J., Klebe, D., Veicht, A., Wattenhofer, R. & Langer, N.. (2022). A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:23912-23932 Available from https://proceedings.mlr.press/v162/wolf22a.html.

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