Summary of Effect Of Kernel Size on Cnn-vision-transformer-based Gaze Prediction Using Electroencephalography Data, by Chuhui Qiu et al.
Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data
by Chuhui Qiu, Bugao Liang, Matthew L Key
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based algorithm for predicting gaze from Electroencephalography (EEG) data is proposed, which can serve as an alternative to traditional video-based eye-tracking. The novel approach improves the root mean-squared-error of EEG-based gaze prediction to 53.06 millimeters, while reducing training time by nearly a third compared to existing state-of-the-art methods. The algorithm’s source code is publicly available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates an algorithm that can predict where people are looking just from brain activity recorded with EEG sensors. This is different from how it’s usually done using cameras. The new method does better than other similar approaches and takes less time to train. You can find the code for this project online. |
Keywords
» Artificial intelligence » Machine learning » Tracking
