Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications
This addresses the issue of electrode shift for wearable biosignal applications, offering an interpretable and parameter-efficient solution, though it is incremental as it builds on existing domain adaptation methods.
The paper tackled the problem of electrode shift in biosignal sensor arrays, which hinders intersession performance in applications like gesture recognition, by proposing a Spatial Adaptation Layer (SAL) and learnable baseline normalization (LBN). The result showed that SAL and LBN outperformed standard fine-tuning on high-density sEMG datasets, achieving competitive performance with orders of magnitude fewer, interpretable parameters.
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.