Biology subjects

Taleshi, M.

Publications and source records attributed to Taleshi, M..

3 recordsLinked to original sources

bioRxiv2025

Sensitivity of Non-Invasive Motor-Unit-Based Gesture Recognition to Signal Degradation

The information encoded by motor units has been successfully harnessed to establish high-fidelity human-machine interfaces (HMI). However, the sensitivity of these interfaces using high-density surface electromyography decomposition, a prevalent method for observing motor unit behaviour, to signal degradations commonly encountered in practical settings remains unexplored. Here, we investigated the effects of additive white Gaussian noise (WGN), channel loss, and electrode shift on pseudo-real-time MU-driven motion classification. Across six wrist movements by 13 participants, we evaluated the performance of two classifiers: linear discriminant analysis (LDA) and deep neural networks (DNN), under different noise conditions. The results indicate that spatial perturbations, including channel loss and electrode shift, significantly affected classification accuracy, with LDA being more susceptible than DNN. Conversely, under intense signal noise (WGN with 5 dB SNR), LDA outperformed DNN, and its simplicity potentially provides greater robustness in a challenging environment. Hence, application-specific signal processing considerations are required depending on the target HMI application. IMPACT STATEMENTPerturbation severely impairs non-invasive motor-unit-based gesture recognition. High signal fidelity and robust system design are essential for practical human-machine interaction.

bioengineering↗

bioRxiv2025

Effects of Spatial and Signal-Imposed Noises on Motor Unit Decomposition

High-density surface electromyography (HD-sEMG) decomposition offers insights into the neural drive through observation of individual motor units (MUs). However, ensuring that this method remains reliable under real-world signal degradations is crucial for its broader application. Therefore, we investigated the impact of three commonly modeled signal degradations on convolutive blind-source-separation (BSS) MU decomposition. 192 HD-sEMG channels were recorded from the forearm muscles of thirteen healthy participants during six wrist movements. Three broad categories of perturbation were introduced, including additive white Gaussian noise (WGN), channel loss, and electrode shift. These perturbations were chosen to mimic challenges encountered in practice, such as ambient electrical noise, electrode failures, and sensor displacement in order to test the MU decomposition algorithms sensitivity. Then, the effects of perturbations on the quantity and quality of extracted MUs and neural drive estimation were assessed. Under non-perturbed conditions, an average of 179 {+/-} 40 MUs were extracted. Severe global WGN significantly reduced extracted MUs by approximately 81%. In contrast, more localized WGN, or channel loss as high as 15%, and electrode shift had minimal impact, with reductions in the number of MU decomposed being less than 6%. Reconstruction of neural drive through smoothed cumulative spike trains was significantly impaired by global WGN, thus leading to increased root mean square error when compared to conditions, while localized perturbations had negligible effects. Therefore, the BSS-based MU decomposition methods seem to be robust against localized noise, channel loss, and minor electrode shifts but are vulnerable to global additive noise. These results highlight the importance of carefully applying MU decomposition approaches in practical settings, maintaining high SNRs in EMG recordings, and preprocessing noise treatment to specific MU-decomposition needs.

bioengineering↗

bioRxiv2025

Impact of Noise on Deep Learning-Based Pseudo-Online Gesture Recognition with High-Density EMG

Deep neural network (DNN)-based approaches have demonstrated high accuracy in surface electromyography (sEMG)-driven gesture recognition under controlled conditions. However, estimation performance is known to degrade substantially when exposed to perturbations commonly encountered in the real-world. Here, we investigate the impact of typical noises on an autoencoder augmented recurrent neural network gesture estimator driven by engineered features and feature sets. High-density sEMG (HD-sEMG) signals offering rich information particularly suited to DNN-based algorithms were collected from thirteen participants performing wrist movements. Three types of synthetic disturbances were introduced: additive white Gaussian noise (WGN), channel loss, and electrode shift. Results indicate that when using amplitude-based features (specifically, the root mean square value and the mean absolute value), the estimator maintains robust performance under increasing WGN and channel loss, whereas its performance deteriorates markedly with features reflecting signal dynamics and fluctuations, like slope sign changes and zero crossings. Under electrode shift conditions, employing a combined feature set enhances the classifiers resilience. Importantly, the degree of performance degradation depends on both the type and intensity of the noise. These findings confirm the need for noise-resilient architectures in order to achieve practical, everyday sEMG-driven human-machine interfaces. Clinical RelevanceQuantifying the sensitivity of sEMG-based gesture classifiers to noise can help clinicians tailor electrode placement and training and ultimately decrease user frustration and improve the acceptance of assistive devices.

bioengineering↗