Toward a generalizable deep CNN for neural drive estimation across muscles and participants

Across Sessions and Subjects Domain Adaptation for Building Robust Myoelectric Interface

Gesture interaction via surface electromyography (sEMG) signal is a promising approach for advanced human-computer interaction systems. However, improving the performance of the myoelectric interface is challenging due to the domain shift caused by the signal's inherent variability. To enhance the interface's robustness, we propose a novel adaptive information fusion neural network (AIFNN) framework, which could effectively reduce the effects of multiple scenarios. Specifically, domain adversarial training is established to inhibit the shared network's weights from exploiting domain-specific representation, thus allowing for the extraction of domain-invariant features. Effectively, classification loss, domain diversence loss and domain discrimination loss are employed, which improve classification performance while reduce distribution mismatches between the two domains. To simulate the application of myoelectric interface, experiments were carried out involving three scenarios (intra-session, inter-session and inter-subject scenarios). Ten non-disabled subjects were recruited to perform sixteen gestures for ten consecutive days. The experimental results indicated that the performance of AIFNN was better than two other state-of-the-art transfer learning approaches, namely fine-tuning (FT) and domain adversarial network (DANN). This study demonstrates the capability of AIFNN to maintain robustness over time and generalize across users in practical myoelectric interface implementations. These findings could serve as a foundation for future deployments.

One-shot random forest model calibration for hand gesture decoding

Objective.Most existing machine learning models for myoelectric control require a large amount of data to learn user-specific characteristics of the electromyographic (EMG) signals, which is burdensome. Our objective is to develop an approach to enable the calibration of a pre-trained model with minimal data from a new myoelectric user.Approach.We trained a random forest (RF) model with EMG data from 20 people collected during the performance of multiple hand grips. To adapt the decision rules for a new user, first, the branches of the pre-trained decision trees were pruned using the validation data from the new user. Then new decision trees trained merely with data from the new user were appended to the pruned pre-trained model.Results.Real-time myoelectric experiments with 18 participants over two days demonstrated the improved accuracy of the proposed approach when compared to benchmark user-specific RF and the linear discriminant analysis models. Furthermore, the RF model that was calibrated on day one for a new participant yielded significantly higher accuracy on day two, when compared to the benchmark approaches, which reflects the robustness of the proposed approach.Significance.The proposed model calibration procedure is completely source-free, that is, once the base model is pre-trained, no access to the source data from the original 20 people is required. Our work promotes the use of efficient, explainable, and simple models for myoelectric control.

Cumulative Spike Train Estimation for Muscle Excitation Assessment From Surface EMG Using Spatial Spike Detection

Estimating cumulative spike train (CST) of motor units (MUs) from surface electromyography (sEMG) is essential for the effective control of neural interfaces. However, the limited accuracy of existing estimation methods greatly hinders the further development of neural interface. This paper proposes a simple but effective approach for identifying CST based on spatial spike detection from high-density sEMG. Specifically, we use a spatial sliding window to detect spikes according to the spatial propagation characteristics of the motor unit action potential, focusing on the spikes of activated MUs in a local area rather than those of a specific MU. We validated the effectiveness of our proposed method through an experiment involving wrist flexion/extension and pronation/supination, comparing it with a recognized CST estimation method and an MU decomposition based method. The results demonstrated that the proposed method obtained higher accuracy on multi-DoF wrist torque estimation leveraging the estimated CST compared to the other three methods. On average, the correlation coefficient (R) and the normalized root mean square error (nRMSE) between the estimation results and recorded force were 0.96 ± 0.03 and 10.1% ± 3.7%, respectively. Moreover, there was an extremely high interpretive extent between the CSTs of proposed method and the MU decomposition method. The outcomes reveal the superiority of the proposed method in identifying CSTs and can provide promising driven signals for neural interface.