semg signal processing

sEMG signals acquired near the heart are severely contaminated by ECG artifacts; conventional filtering and template subtraction methods achieve limited suppression, while existing deep learning approaches struggle to balance denoising performance and computational efficiency. This paper proposes the first end-to-end sEMG denoising architecture that integrates a lightweight Mamba state-space model with a 1D convolutional network. Evaluated on the NINAP and MIT-BIH datasets, the method achieves high-precision ECG artifact suppression. Compared to state-of-the-art baselines, it improves PSNR by 3.2 dB, reduces model parameters by 67%, and significantly enhances both signal fidelity and real-time processing capability—particularly in preserving intrinsic electromyographic dynamics such as motor unit recruitment patterns and firing rate modulation.

This study addresses the challenge of high computational cost and poor real-time performance in automated classification of neural muscular disorders using high-sampling-rate needle electromyography (nEMG) signals, where the impact of downsampling on diagnostic information retention remains unclear. The authors propose a unified evaluation framework that, for the first time, integrates shape-aware downsampling, feature space analysis, and classification performance to systematically quantify how different downsampling strategies affect waveform integrity and diagnostic capability in high-frequency temporal biosignals. Evaluated on a three-class neuromuscular disorder classification task, the framework identifies an optimal downsampling configuration that balances computational efficiency with preservation of diagnostic information. Results demonstrate that shape-aware downsampling significantly outperforms conventional methods, offering a generalizable analytical paradigm for efficient processing of high-frequency temporal biosignals.

This study addresses the challenge of unsupervised clustering and classification of surface electromyographic (sEMG) signals during functional upper-limb movements. The authors propose a four-stage data-driven pipeline comprising signal preprocessing, multi-criteria feature selection, Mahalanobis distance–based hierarchical clustering for gesture selection, and classification evaluation. By integrating Hilbert envelope analysis, mutual information, principal component analysis (PCA), and decision tree–based feature importance, they identify six representative gestures from the NINAPRO DB4 dataset that exhibit both biomechanical diversity and physiological plausibility, and determine a 200 ms window as optimal for temporal segmentation. Experimental results demonstrate that Extra Trees and artificial neural network classifiers achieve superior performance, confirming the method’s effectiveness and scalability for low-latency prosthetic control.

This study addresses the challenge of deploying hand gesture recognition systems on low-power embedded devices, where existing approaches relying on multi-channel electromyographic (EMG) signals and complex models are impractical. For the first time, it systematically demonstrates the feasibility of achieving high-accuracy classification of ten distinct gestures using only a single-channel surface EMG (sEMG) signal combined with lightweight machine learning. By integrating multi-domain features—including time-domain, frequency-domain, higher-order zero-crossing rate, and relative intensity—and applying Pearson correlation-based feature selection followed by PCA or LDA for dimensionality reduction, the authors evaluate classification performance using neural networks (NN), k-nearest neighbors (KNN), and support vector machines (SVM). The optimal configuration achieves 90% accuracy, substantially reducing both hardware requirements and computational overhead, thereby offering an effective solution for low-cost, low-power gesture-based interaction.

This study addresses poor generalization in non-invasive surface electromyography (sEMG) gesture decoding, primarily caused by inter-subject and intra-subject signal variability. We propose modeling multi-channel forearm sEMG signals as covariance matrices embedded in the Symmetric Positive-Definite (SPD) Riemannian manifold—thereby explicitly characterizing the spatial synergy topology of sEMG for the first time. Within this geometric framework, unsupervised clustering and supervised classification are jointly realized without domain adaptation or subject-specific calibration. The method is computationally efficient and highly robust, achieving significant improvements over state-of-the-art baselines on mainstream benchmarks—particularly yielding a 12.3% average accuracy gain in cross-subject gesture recognition. Our core contribution is establishing an sEMG-to-SPD manifold mapping paradigm, offering a geometric learning approach to myoelectric decoding that is both interpretable and strongly generalizable.

This study addresses the challenge posed by strong artifacts in transcranial magnetic stimulation (TMS)-evoked electroencephalography (EEG) signals, which hinder their application in closed-loop neuromodulation and brain–computer interfaces. The work presents the first standardized benchmark dataset for TMS-EEG denoising and systematically evaluates two mainstream source-domain denoising pipelines under conditions lacking ground-truth physiological signals, assessing both artifact suppression efficacy and preservation of genuine TMS-evoked responses. The proposed preprocessing framework demonstrates robust performance, significantly enhancing signal quality and establishing a unified benchmark for algorithm development. This advancement facilitates more reliable use of TMS-EEG in neuroscience research, clinical settings, and embedded brain–computer interface systems.

This study addresses the performance degradation of existing surface electromyography (sEMG)-based gesture recognition methods as the number of gestures increases, their limited capacity to model long-range temporal dependencies, and the lack of effective fusion with other physiological modalities such as inertial and eye-tracking signals. To overcome these challenges, this work proposes EMG-CrossFormer, an end-to-end hybrid convolutional–Transformer architecture that, for the first time, integrates learnable gesture queries into a Transformer framework for multimodal sEMG recognition. The model employs cascaded cross-attention mechanisms to jointly capture local and global spatiotemporal features and supports flexible input from arbitrary modalities. Experiments demonstrate that EMG-CrossFormer achieves 79.16% accuracy using sEMG alone across four NinaPro datasets, which further improves to 92.79% when inertial signals are incorporated—significantly outperforming six state-of-the-art baselines.

This study addresses the inconsistent performance of surface electromyography (sEMG)-based human-machine interfaces across diverse populations, which may stem from demographic influences on sEMG features. For the first time, it systematically quantifies associations between 147 commonly used sEMG features and demographic variables—including age, sex, and body mass index—using gesture data collected from 81 demographically diverse participants. Combining linear mixed-effects modeling with partial least squares regression, the analysis reveals that 33% (49 out of 147) of the features exhibit significant demographic bias. These findings highlight a potential source of systematic unfairness in current decoding approaches and provide critical evidence for developing fair, generalizable neural interfaces capable of robust performance across heterogeneous user populations.