Summary of Knowledge-guided Eeg Representation Learning, by Aditya Kommineni et al.
Knowledge-guided EEG Representation Learning
by Aditya Kommineni, Kleanthis Avramidis, Richard Leahy, Shrikanth Narayanan
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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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 The proposed self-supervised model for EEG biosignals leverages large-scale unlabelled data to learn robust representations, which can improve inference tasks. The work adapts established objectives from multimedia domains to EEG analysis, proposing a state space-based deep learning architecture and a novel knowledge-guided pre-training objective that accounts for the idiosyncrasies of the EEG signal. The results show improved embedding representation learning and downstream performance compared to prior works on exemplary tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to use large amounts of unlabelled data to learn about brain signals (EEG). This can help make better predictions and improve our understanding of brain activity. They used a special kind of computer model that’s good at handling the unique characteristics of EEG data. The results are promising, showing improved performance on certain tasks compared to previous methods. |
Keywords
* Artificial intelligence * Deep learning * Embedding * Inference * Representation learning * Self supervised
