# Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifiers

arXiv: https://arxiv.org/abs/2209.03761 · HTML: https://mlsanity.com/paper/2209.03761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading](https://mlsanity.com/paper/1905.06312/) (2019)
- [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)
- [A Simplistic Machine Learning Approach to Contact Tracing](https://mlsanity.com/paper/2012.05940/) (2020)
- [Machine Learning for removing EEG artifacts: Setting the benchmark](https://mlsanity.com/paper/1903.07825/) (2019)
- [Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift](https://mlsanity.com/paper/2208.00465/) (2022)
- [Residual Channel Attention Network for Brain Glioma Segmentation](https://mlsanity.com/paper/2205.10758/) (2022)

## Built on by (most cited first, 1 of 1)

- [Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data](https://mlsanity.com/paper/2408.03478/) (2024)

## Abstract (arXiv)

The differences in distributional patterns between benchmark data and real-world data have been one of the main challenges of using electroencephalogram (EEG) signals for eye-tracking (ET) classification. Therefore, increasing the robustness of machine learning models in predicting eye-tracking positions from EEG data is integral for both research and consumer use. Previously, we compared the performance of classifiers trained solely on finer-grain data to those trained solely on coarse-grain. Results indicated that despite the overall improvement in robustness, the performance of the fine-grain trained models decreased, compared to coarse-grain trained models, when the testing and training set contained the same distributional patterns citevectorbased. This paper aims to address this case by training models using datasets of mixed data complexity to determine the ideal distribution of fine- and coarse-grain data. We train machine learning models utilizing a mixed dataset composed of both fine- and coarse-grain data and then compare the accuracies to models trained using solely fine- or coarse-grain data. For our purposes, finer-grain data refers to data collected using more complex methods whereas coarser-grain data refers to data collected using more simple methods. We apply covariate distributional shifts to test for the susceptibility of each training set. Our results indicated that the optimal training dataset for EEG-ET classification is not composed of solely fine- or coarse-grain data, but rather a mix of the two, leaning towards finer-grain.