Summary of Uncertainty Quantification For Cross-subject Motor Imagery Classification, by Prithviraj Manivannan et al.
Uncertainty Quantification for cross-subject Motor Imagery classification
by Prithviraj Manivannan, Ivo Pascal de Jong, Matias Valdenegro-Toro, Andreea Ioana Sburlea
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Uncertainty Quantification aims to determine when a Machine Learning model’s prediction is likely to be wrong. In Computer Vision research, methods for determining epistemic uncertainty (model uncertainty) have been explored. These methods theoretically allow for predicting misclassifications due to inter-subject variability. This paper applied various Uncertainty Quantification methods to predict misclassifications for a Motor Imagery Brain Computer Interface using Deep Ensembles, which performed best in terms of classification performance and cross-subject Uncertainty Quantification performance. Notably, standard CNNs with Softmax output performed better than some advanced methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure Machine Learning models are reliable. Sometimes, these models can make mistakes, but we want to know when that’s going to happen. The researchers used different techniques to figure out how likely it was for a model to be wrong. They tested these techniques on a special kind of computer interface that helps people with paralysis control devices. The best technique they found was called Deep Ensembles. It did a great job at predicting when the model might make a mistake. |
Keywords
* Artificial intelligence * Classification * Machine learning * Softmax
