Summary of Splitsee: a Splittable Self-supervised Framework For Single-channel Eeg Representation Learning, by Rikuto Kotoge et al.
SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning
by Rikuto Kotoge, Zheng Chen, Tasuku Kimura, Yasuko Matsubara, Takufumi Yanagisawa, Haruhiko Kishima, Yasushi Sakurai
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a novel framework called SplitSEE for learning temporal-frequency representations in single-channel electroencephalography (EEG) data. The approach is designed to be robust to multi-channels and scalable across various tasks, such as seizure prediction. SplitSEE incorporates a self-supervised deep clustering task that learns domain-specific representations from both time and frequency domains, which are then combined to share the same cluster assignment. The framework leverages a pre-training-to-fine-tuning approach within a splittable architecture, achieving superior performance on single-channel EEG data and showing robustness across different channels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SplitSEE is a new way to analyze brain signals using just one channel of information. It’s like taking a photo from two angles – time and frequency – and making sure they show the same thing. This helps with things like predicting seizures or understanding brain activity. The approach uses a special kind of artificial intelligence called deep learning, which can learn patterns in data on its own. This makes it good at finding what matters most in single-channel EEG signals. |
Keywords
» Artificial intelligence » Clustering » Deep learning » Fine tuning » Self supervised
