# Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift

arXiv: https://arxiv.org/abs/2208.00465 · HTML: https://mlsanity.com/paper/2208.00465/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising](https://mlsanity.com/paper/2009.11662/) (2020)
- [EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction](https://mlsanity.com/paper/2111.05100/) (2021)
- [Mental State Recognition via Wearable EEG](https://mlsanity.com/paper/1602.00985/) (2016)
- [Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading](https://mlsanity.com/paper/2108.06763/) (2021)
- [Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma](https://mlsanity.com/paper/2005.04069/) (2020)
- [Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma](https://mlsanity.com/paper/2005.04073/) (2020)
- [Multi-Target Deep Learning for Algal Detection and Classification](https://mlsanity.com/paper/2005.03232/) (2020)
- [Machine Learning for removing EEG artifacts: Setting the benchmark](https://mlsanity.com/paper/1903.07825/) (2019)

## Built on by (most cited first, 3 of 3)

- [Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifiers](https://mlsanity.com/paper/2209.03761/) (2022)
- [EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures](https://mlsanity.com/paper/2408.03449/) (2024)
- [Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data](https://mlsanity.com/paper/2408.03478/) (2024)

## Abstract (arXiv)

The main challenges of using electroencephalogram (EEG) signals to make eye-tracking (ET) predictions are the differences in distributional patterns between benchmark data and real-world data and the noise resulting from the unintended interference of brain signals from multiple sources. Increasing the robustness of machine learning models in predicting eye-tracking position from EEG data is therefore integral for both research and consumer use. In medical research, the usage of more complicated data collection methods to test for simpler tasks has been explored to address this very issue. In this study, we propose a fine-grain data approach for EEG-ET data collection in order to create more robust benchmarking. We train machine learning models utilizing both coarse-grain and fine-grain data and compare their accuracies when tested on data of similar/different distributional patterns in order to determine how susceptible EEG-ET benchmarks are to differences in distributional data. We apply a covariate distributional shift to test for this susceptibility. Results showed that models trained on fine-grain, vector-based data were less susceptible to distributional shifts than models trained on coarse-grain, binary-classified data.