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arXiv Machine Learning By Tianhao Wu, Jiawei Yu, Xiangdong Hu, Xu Wu, Hongyu Wu, Sidharth Anupkrishnan, Amirmohammad Radmehr, Yi Chen Liu, Phuc Nguyen, Yi Wu, Jian Liu

EMG-FM-Bench: A Comprehensive Benchmark for Foundation Model Transfer and Adaptation on Electromyography

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EMG-FM-Bench is a benchmark that unifies 20 public electromyography datasets, totaling over one million EMG segments, to evaluate how foundation models transfer and adapt to EMG tasks. It tests nine pretrained models across four scenarios: frozen versus fully fine‑tuned performance, the benefit of pretraining versus training from scratch, generalization to new users with limited data, and performance across different EMG tasks. Results show that linear probing reveals useful pretrained representations, full fine‑tuning can significantly alter performance, pretraining benefits vary by model, performance drops on new users, and few‑shot adaptation improves macro‑F1 in most cases but only partially recovers lost performance.

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