Continuous estimation of high-dimensional finger kinematics from forearm surface electromyography (EMG) could enable natural control for hand prostheses, AR/XR interfaces, and teleoperation. However, the complexity of human hand gestures and the entanglement of forearm muscles make accurate recognition intrinsically challenging. Existing approaches typically reduce task complexity by relying on classification-based machine learning, limiting the controllable degrees of freedom and compromising on natural interaction. We present an end-to-end framework for continuous EMG-to-kinematics regression using only consumer-grade hardware. The framework combines an 8-channel EMG armband, a single webcam, and an automatic synchronization procedure, enabling the collection of the EMG Finger-Kinematics dataset (EMG-FK), a 10-h dataset of synchronized EMG and 15 finger joint angles from 20 participants performing rich, unconstrained right-hand motions. We also introduce the Temporal Riemannian Regressor (TRR), a lightweight GRU-based model that uses sequences of multi-band Riemannian covariance features to decode finger motion. Across EMG-FK and the public emg2pose benchmark, TRR outperforms state-of-the-art methods in both intra- and cross-subject evaluation. On EMG-FK, it reaches an average absolute error of 9.79 °± 1.48 in intra-subject and 16.71 °± 3.97 in cross-subject. Finally, we demonstrate real-time deployment on a Raspberry Pi 5 and intuitive control of a robotic hand; TRR runs at nearly 10 predictions/s and is roughly an order of magnitude faster than state-of-the-art approaches. Together, these contributions lower the barrier to reproducible, real-time EMG-based decoding of high-dimensional finger motion, and pave the way toward more natural and intuitive control of embedded EMG-based systems.

Electromyography (EMG) offers a natural and non-invasive interface for human-computer interaction, and machine learning (ML) is increasingly used to recognize gestures from recorded EMG signals. However, the statistical distributions of EMG show high inter-person variability, making cross-subject interpretation unreliable. Calibration phases with guided exercises are currently used in commercial devices to address this issue. Modern solutions rely on unsupervised domain adaptation (UDA) methods which align the sample distributions of different subjects without requiring tedious calibration exercises. However, when a real concept shift occurs (e.g. a similar signal pattern corresponds to different gestures in different subjects), these alignment methods are inefficient. This paper presents a novel linear and shallow UDA method based on Linear Discriminant Analysis and K-Means to address cross-subject calibration. To better handle real concept shifts, this parsimonious and easy-to-use method is non-conservative, meaning that it relies only on target samples and initial pseudo-labels rather than performing domain alignment. We perform in-depth evaluation against state-of-the-art deep-learning methods across multiple datasets and feature extraction pipelines and emulate a realistic system using streamed EMG to show the significant potential of our method. Our findings show that our approach significantly improves cross-subject accuracy compared to existing methods, effectively closing the accuracy gap between intra-subject and cross-subject classification. It requires only a few gesture repetitions to converge to accurate decision boundaries and remains robust to variations in data characteristics.

BACKGROUND:Music and pink noise share spectral similarities; however, their cognitive and emotional properties differ. Previous studies using magnetoencephalography have shown that somatosensory evoked components occurring before 60 ms are not modulated by concomitant musical auditory stimulation. To date, no study has examined the effects of pink noise on somatosensory evoked potentials. OBJECTIVE:This study investigated, using electroencephalography (EEG), whether continuous auditory stimulation, either music or pink noise, can modulate early (before 60 ms) somatosensory evoked potentials elicited by electrical stimulation of the median nerve. METHODS:Somatosensory evoked potentials elicited by right median nerve electrical stimulation were recorded through electroencephalogram in two groups of participants (MUSIC and NOISE) in four conditions (pre, during 1, during 2, and after auditory stimulation). In addition, a control group underwent three consecutive SEP recordings, temporally aligned with the intervention groups' sequence. A permutation-based ANOVA (10,000 permutations) was performed over the entire 10-55 ms time window for the F1, C1, and CP3 channels, followed by corrected post hoc comparisons (pre vs. During1, pre vs. During2, pre vs. post). RESULTS:A significant increase in the amplitude of the P45 component was observed in the left central site during noise exposure, with post hoc comparisons indicating increased amplitudes in during1 and during2 relative to pre, with a larger effect size in during2. Music resulted in a reduction in the early part (~30 ms) of the left centroparietal positive brain response, as highlighted by post hoc comparisons between during2 and preconditions. In the control group, no comparisons were significant, indicating the absence of reliable differences between repetitive recordings. CONCLUSION:Middle-latency SEP components (30-45 ms) showed distinct amplitude modulations during both music and pink noise auditory exposure.

Electromyograms (EMG)-based hand gesture recognition systems are a promising technology for human/machine interfaces. However, one of their main limitations is the long calibration time that is typically required to handle new users. The paper discusses and analyses the challenge of cross-subject generalization thanks to an original dataset containing the EMG signals of 14 human subjects during hand gestures. The experimental results show that, though an accurate generalization based on pooling multiple subjects is hardly achievable, it is possible to improve the cross-subject estimation by identifying a robust low-dimensional subspace for multiple subjects and aligning it to a target subject. A visualization of the subspace enables us to provide insights for the improvement of cross-subject generalization with EMG signals.

Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the “move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses.

The review article by Torricelli et al. [36] revisits with clarity and precision the scientific foundations of the kinematic invariants found in the execution of movements, offering a unified view of motor primitives.This could allow a better understanding of phenomena at higher levels of organization which involve internal models, the perception of the movement of others, and sophisticated cognitive abilities.If the motor invariants revealed by the recordings of the kinematics of biological movements may help to recognize an object, a face, or an intention, to explore a landscape, to grasp an object, and to act accordingly with physical and ecological constraints, the underlying neuronal mechanisms are far from being completely elucidated.The basic neurophysiological mechanisms have been well defined within the different levels of organization extending from molecular biology to the neural network.However, to date, there is no real consensus about unifying mechanisms sustaining the essential motor function of the brain.In this commentary, we will first attempt to link motor invariance to the fundamental neurophysiological mechanisms previously discovered in oculomotor skills and more recently applied to general motor skills.Secondly, we will expand on the idea put forward by Torricelli et al. on page 20 that the submovements are dynamically supported by motor oscillatory activity and defend the hypothesis that neural oscillations contribute to the emergence of kinematic invariants during the production and observation of movements.In addition, we hypothesize about the existence of multiple integrators acting on the neural oscillations for producing the position signals at the basis of motor and perceptive kinematics invariants. From the oculomotor integrators to the limb integratorsFaced with the difficulty of discovering the basic mechanism explaining the different motor invariants, we may recognize that oculomotor neurophysiology has demonstrated certain effectiveness in highlighting the fundamental mechanisms of the control of eye movements on which many motor and perceptual behaviors depend.A simple but

ObjectiveDifferent visual stimuli are classically used for triggering visual evoked potentials comprising well-defined components linked to the content of the displayed image. These evoked components result from the average of ongoing EEG signals in which additive and oscillatory mechanisms contribute to the component morphology. The evoked related potentials often resulted from a mixed situation (power variation and phase-locking) making basic and clinical interpretations difficult. Besides, the grand average methodology produced artificial constructs that do not reflect individual peculiarities. This motivated new approaches based on single-trial analysis as recently used in the brain-computer interface field.ApproachWe hypothesize that EEG signals may include specific information about the visual features of the displayed image and that such distinctive traits can be identified by state-of-the-art classification algorithms based on Riemannian geometry. The same classification algorithms are also applied to the dipole sources estimated by sLORETA.Main results and significanceWe show that our classification pipeline can effectively discriminate between the display of different visual items (Checkerboard versus 3D navigational image) in single EEG trials throughout multiple subjects. The present methodology reaches a single-trial classification accuracy of about 84% and 93% for inter-subject and intra-subject classification respectively using surface EEG. Interestingly, we note that the classification algorithms trained on sLORETA sources estimation fail to generalize among multiple subjects (63%), which may be due to either the average head model used by sLORETA or the subsequent spatial filtering failing to extract discriminative information, but reach an intra-subject classification accuracy of 82%.

The search for the best wellness practice has promoted the development of devices integrating different technologies and guided meditation. However, the final effects on the electrical activity of the brain remain relatively sparse. Here, we have analyzed of the alpha and theta electroencephalographic oscillations during the realization of the arrest reaction (AR; eyes close/eyes open transition) when a chromotherapy session performed in a dedicated room [Rebalance (RB) device], with an ergonomic bed integrating pulsed-wave light (PWL) stimulation, guided breathing, and body scan exercises. We demonstrated that the PWL induced an evoked-related potential characterized by the N2-P3 components maximally recorded on the fronto-central areas and accompanied by an event-related synchronization (ERS) of the delta-theta-alpha oscillations. The power of the alpha and theta oscillations was analyzed during repeated ARs testing realized along with the whole RB session. We showed that the power of the alpha and theta oscillations was significantly increased during the session in comparison to their values recorded before. Of the 14 participants, 11 and 6 showed a significant power increase of the alpha and theta oscillations, respectively. These increased powers were not observed in two different control groups (n = 28) who stayed passively outside or inside the RB room but without any type of stimulation. These preliminary results suggest that PWL chromotherapy and guided relaxation induce measurable electrical brain changes that could be beneficial under neuropsychiatric perspectives.

Covariance matrices of EEG signals from 15 subjects presented with visual stimuli of 3D-Tunnels and Checkerboards. This dataset is the basis of an article named "Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans".

Non-invasive BMI applications are increasingly used in different contexts ranging from industrial, clinical and gaming. After having tested the difference between a classical EEG recorder with electroconductive gel (ANT system) and the MUSE EEG headband, we studied the BCI performances of the later during the control of a small robot. We demonstrated that the participants were able to successfully control the robot using an online brain-computer interface based on the signal power in different frequency bands (delta, theta and alpha) characterizing the eyes-opened and relaxed eyes-closed states. Additionally, we performed a correlation analysis which demonstrated that the BCI commands were more related to a delta or theta power decrease for the determination of the classifier output probability and to the alpha power increase for the speed control of the robot.

Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival. Here, we hypothesize that the EEG signals contain the distinctive features characterizing movement intent already expressed before movement execution and that such distinctive information can be identified by state-of-the-art classification algorithms based on Riemannian geometry. We demonstrated for the first time that a classifier based on covariance matrices and Riemannian geometry can effectively discriminate between neutral, festive, and violent mental states only on the basis of non-invasive EEG signals in both the actor and observer participants. These results pave the way for new electrophysiological discrimination of mental states based on non-invasive EEG recordings and cutting-edge machine learning techniques.