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EMGBlend introduces a self-supervised framework for pretraining on heterogeneous EMG datasets, addressing differences in electrode layouts, frequency support, and data source imbalances to improve gesture recognition and force decoding tasks.
This paper introduces a hybrid quantum-inspired Kolmogorov-Arnold network for privacy-aware federated learning of ECG data, demonstrating reduced parameters and communication costs while improving classification metrics compared to traditional MLP.