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Electromyography (EMG) signals are widely used in assistive exoskeleton control for predicting human joint torque due to their ability to extract muscle activations before movement onset. The standard procedure for learning the EMG-to-torque model involves training the model on a training set of EMG-torque data, followed by validating the model on a separate test set. The comparison between models is generally undertaken on the test set. However, the analysis of model performance on the data outside the test set remains unaddressed. The lack of a guarantee for unseen data reduces the reliability of EMG-to-torque models in practical exoskeleton control. In this paper, we address this issue by proposing a bounded-generalization-error neural network (BGNN) for EMG-based torque prediction. Using gradient descent to train the network, we formulate at each training step a theoretical upper bound on the generalization error, reflecting the prediction error across the entire data distribution, including unseen data beyond the test set. The NN training is terminated when this upper bound reaches its minimum, thereby achieving the tightest guarantee on the generalization error. Experimental results on torque prediction demonstrated that, while ensuring such a bounded generalization error, our method still gave results comparable to those of classical models. The use of our BGNN in assistive exoskeleton control was also tested with 13 participants on a pick-and-place task with an upper limb exoskeleton. Experimental results on assistive control revealed that our method can reduce human physical fatigue without compromising movement speed or accuracy compared to natural human movement characteristics, particularly for generalization in novel tasks.