EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction

Kastrati, Ard; Płomecka, Martyna Beata; Pascual, Damián; Wolf, Lukas J.; Gillioz, Victor; Wattenhofer, Roger; Langer, Nicolas

doi:10.48550/arxiv.2111.05100

Cited by 3 publications

(7 citation statements)

References 33 publications

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“…EEGEyeNet Dataset: For all experiments, we used EEGEyeNet [16] dataset and benchmark that includes synchronized Electroencephalography (EEG) and Eye-tracking recordings, obtained during an experiment where participants were instructed to follow a series of successively appearing dots on a 24-inch monitor placed at a distance of 68cm. A stable head position of the participant was ensured via a chin rest.…”

Section: Materials and Experimental Settingsmentioning

confidence: 99%

“…EEGEyeNet Dataset: For our experiments, we utilized the EEGEyeNet dataset [16], which includes synchronized EEG and Eye-tracking data. The EEG signals were collected using a high-density, 128-channel EEG Geodesic Hydrocel system sampled at a frequency of 500 Hz.…”

Section: Materials and Experimental Settingsmentioning

confidence: 99%

“…Recently, Electroencephalogram (EEG) has been explored as an alternative method to estimate eye movements by recording electrical activity from the brain non-invasively with high temporal resolution [16]. The growing body of literature has shown that Deep Learning architectures could be significantly effective for many EEG-based tasks [4,26].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we introduce a new deep learning framework for analyzing EEG signals applying attention mechanisms. For the method evaluation, we used the EEGEyeNet dataset and benchmark [16], which includes concurrent EEG and infrared eye-tracking recordings, with eye tracking data serving as a ground truth. Our method incorporates attention modules to assign weights to individual electrodes based on their importance, allowing the network to prioritize relevant information in the signal.…”

Section: Introductionmentioning

confidence: 99%

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An Interpretable Attention-based Method for Gaze Estimation Using Electroencephalography

Weng¹,

Płomecka²,

Kaufmann³

et al. 2023

Preprint

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Eye movements can reveal valuable insights into various aspects of human mental processes, physical well-being, and actions. Recently, several datasets have been made available that simultaneously record EEG activity and eye movements. This has triggered the development of various methods to predict gaze direction based on brain activity. However, most of these methods lack interpretability, which limits their technology acceptance. In this paper, we leverage a large data set of simultaneously measured Electroencephalography (EEG) and Eye track- ing, proposing an interpretable model for gaze estimation from EEG data. More specifically, we present a novel attention-based deep learning framework for EEG signal analysis, which allows the network to focus on the most relevant information in the signal and discard problematic channels. Additionally, we provide a comprehensive evaluation of the presented framework, demonstrating its superiority over current methods in terms of accuracy and robustness. Finally, the study presents visualizations that explain the results of the analysis and highlights the potential of attention mechanism for improving the efficiency and effectiveness of EEG data analysis in a variety of applications.

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Section: Materials and Experimental Settingsmentioning

confidence: 99%

Section: Materials and Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Interpretable Attention-based Method for Gaze Estimation Using Electroencephalography

Weng¹,

Płomecka²,

Kaufmann³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Researchers from Computer Science, Neuroscience, and Medical fields have applied EEG-based Brain-Computer Interaction (BCI) techniques in many different ways [2,15,19,22,24,26,34], such as diagnosis of abnormal states, evaluating the effect of the treatments, seizure detection, motor imagery tasks [4,5,6,17,23,27], and developing BCI-based games [14]. Previous studies have demonstrated the great potential of machine learning, deep learning, and transfer learning algorithms [1,3,7,8,12,16,18,20,21,25,28,29,37,38,39,40,41,42] in such clinical and non-clinical data analysis.…”

Section: Introductionmentioning

confidence: 99%

Time Majority Voting, a PC-based EEG Classifier for Non-expert Users

Dou¹,

Zhang²,

Qu³

2022

Preprint

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Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision and natural language processing, the data amount of these trials is still rather tiny. Developing a PC-based machine learning technique to increase the participation of non-expert end-users could help solve this data collection issue. We created a novel algorithm for machine learning called Time Majority Voting (TMV). In our experiment, TMV performed better than cutting-edge algorithms. It can operate efficiently on personal computers for classification tasks involving the BCI. These interpretable data also assisted end-users and researchers in comprehending EEG tests better.

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OperatorEYEVP: Operator Dataset for Fatigue Detection Based on Eye Movements, Heart Rate Data, and Video Information

Kovalenko

Mamonov

Kuznetsov

et al. 2023

Sensors

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Detection of fatigue is extremely important in the development of different kinds of preventive systems (such as driver monitoring or operator monitoring for accident prevention). The presence of fatigue for this task should be determined with physiological and objective behavioral indicators. To develop an effective model of fatigue detection, it is important to record a dataset with people in a state of fatigue as well as in a normal state. We carried out data collection using an eye tracker, a video camera, a stage camera, and a heart rate monitor to record a different kind of signal to analyze them. In our proposed dataset, 10 participants took part in the experiment and recorded data 3 times a day for 8 days. They performed different types of activity (choice reaction time, reading, correction test Landolt rings, playing Tetris), imitating everyday tasks. Our dataset is useful for studying fatigue and finding indicators of its manifestation. We have analyzed datasets that have public access to find the best for this task. Each of them contains data of eye movements and other types of data. We evaluated each of them to determine their suitability for fatigue studies, but none of them fully fit the fatigue detection task. We evaluated the recorded dataset by calculating the correspondences between eye-tracking data and CRT (choice reaction time) that show the presence of fatigue.

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EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction

Cited by 3 publications

References 33 publications

An Interpretable Attention-based Method for Gaze Estimation Using Electroencephalography

An Interpretable Attention-based Method for Gaze Estimation Using Electroencephalography

Time Majority Voting, a PC-based EEG Classifier for Non-expert Users

OperatorEYEVP: Operator Dataset for Fatigue Detection Based on Eye Movements, Heart Rate Data, and Video Information

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