# Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications

arXiv: https://arxiv.org/abs/2409.08058 · HTML: https://mlsanity.com/paper/2409.08058/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning](https://mlsanity.com/paper/1801.07756/) (2018)
- [Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks](https://mlsanity.com/paper/1901.06958/) (2019)
- [Tackling Electrode Shift In Gesture Recognition with HD-EMG Electrode Subsets](https://mlsanity.com/paper/2401.02773/) (2024)

## Built on by (most cited first, 1 of 1)

- [MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition](https://mlsanity.com/paper/2502.17457/) (2025)

## Abstract (arXiv)

Machine learning offers promising methods for processing signals recorded with wearable devices such as surface electromyography (sEMG) and electroencephalography (EEG). However, in these applications, despite high within-session performance, intersession performance is hindered by electrode shift, a known issue across modalities. Existing solutions often require large and expensive datasets and/or lack robustness and interpretability. Thus, we propose the Spatial Adaptation Layer (SAL), which can be applied to any biosignal array model and learns a parametrized affine transformation at the input between two recording sessions. We also introduce learnable baseline normalization (LBN) to reduce baseline fluctuations. Tested on two HD-sEMG gesture recognition datasets, SAL and LBN outperformed standard fine-tuning on regular arrays, achieving competitive performance even with a logistic regressor, with orders of magnitude less, physically interpretable parameters. Our ablation study showed that forearm circumferential translations account for the majority of performance improvements.