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

• URL: http://arxiv.org/abs/2409.08058v2
• Date: Wed, 29 Jan 2025 14:50:58 GMT
• Title: Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications
• Authors: Joao Pereira, Michael Alummoottil, Dimitrios Halatsis, Dario Farina,
• Abstract summary: We propose the Spatial Adaptation Layer (SAL), which can be applied to any biosignal array model. 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.
• Score: 0.7499722271664147
• License:
• Abstract: 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.

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