Summary of Graph Adapter Of Eeg Foundation Models For Parameter Efficient Fine Tuning, by Toyotaro Suzumura et al.
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning
by Toyotaro Suzumura, Hiroki Kanezashi, Shotaro Akahori
First submitted to arxiv on: 25 Nov 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 paper presents a novel approach called EEG-GraphAdapter (EGA) to diagnose neurological disorders from electroencephalography (EEG) data. Building upon foundation models like Transformers, which capture temporal dynamics, EGA incorporates Graph Neural Networks (GNNs) for representing spatial relationships among EEG sensors. To overcome the computational limitations of fine-tuning large-scale models, EGA uses a parameter-efficient fine-tuning (PEFT) strategy that freezes the pre-trained temporal backbone model and only adapts the GNN-based module. This results in reduced computational overhead and data requirements while achieving improved performance on two healthcare-related tasks: Major Depressive Disorder (MDD) and Abnormality Detection (TUAB). The EGA approach demonstrates a significant boost of up to 16.1% in F1-score compared to the baseline BENDR model, showcasing its potential for scalable and accurate EEG-based predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about using special computer models to help doctors diagnose brain disorders from brain wave data. The models are like super smart helpers that can learn patterns in the data. The challenge is that these models need a lot of training, but there isn’t always enough data available. To solve this problem, the researchers created a new way to fine-tune these models called EEG-GraphAdapter (EGA). EGA is faster and uses less data than before, which means it can help doctors make more accurate diagnoses without needing as many brain wave recordings. The results show that EGA is really good at detecting certain disorders, like depression, and has the potential to be a game-changer for diagnosing brain disorders. |
Keywords
* Artificial intelligence * F1 score * Fine tuning * Gnn * Parameter efficient
