Summary of Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications, by Joao Pereira et al.
Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications
by Joao Pereira, Michael Alummoottil, Dimitrios Halatsis, Dario Farina
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Machine learning offers promising methods for processing signals recorded with wearable devices such as surface electromyography (sEMG) and electroencephalography (EEG). Despite high within-session performance, intersession performance is hindered by electrode shift. Existing solutions often require large datasets and/or lack robustness and interpretability. This paper proposes the Spatial Adaptation Layer (SAL), which learns a parametrized affine transformation at the input between two recording sessions. SAL can be applied to any biosignal array model and outperforms standard fine-tuning on regular arrays, achieving competitive performance with orders of magnitude less physically interpretable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wearable devices like sEMG and EEG can record important signals. But when you move or switch sessions, the signals get mixed up because of how the electrodes are placed. This makes it hard to use these signals for things like gesture recognition. Some solutions need a lot of data or don’t work well. This paper wants to fix that by creating something called the Spatial Adaptation Layer (SAL). It can learn how to adjust the signal based on where you moved, and it works better than other methods with fewer parameters. |
Keywords
» Artificial intelligence » Fine tuning » Gesture recognition » Machine learning
