Integrative neuroscience increasingly recognizes that the brain evolved primarily as a biological system for generating movement. Viewed as a complex oscillator, the brain is now widely investigated through electroencephalography (EEG), which occupies a central position in both motor neuroscience and cognitive research, particularly in the study of consciousness. In this perspective article, we revisit experimental findings from both animal models and humans demonstrating how the analysis of brain oscillatory dynamics including neural entrainment allows the investigation of mental states, motor performance, and consciousness. By examining three well-established electrophysiological markers (the P300 evoked potential, the readiness potential, and the somatosensory N30 wave), we propose that new neurophysiological mechanisms may be identified and explored through future experimentation. We further suggest that insights from oculomotor research, especially the concept of the neural integrator and its extension to working memory and dynamic attractor models, may help clarify the functional interplay between movement generation and consciousness.

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.

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.

Cerebellar transcranial direct current stimulation (Cb-tDCS) is a promising tool for non-invasive modulation of cerebellar function and is under investigation for treating cerebellum-related disorders. However, its local and remote effects on sensory processing remain poorly understood. We investigated the immediate and long-term effects of Cb-tDCS on sensory-evoked responses in the cerebellum and primary somatosensory cortex (S1) of awake mice. Sensory-evoked potentials (SEPs) were recorded in Crus I/II and S1 during and after short (15 s) or long (20 min) sessions of anodal or cathodal Cb-tDCS. In addition, vGLUT1 and GAD65-67 immunoreactivity were quantified, and spectral changes in local field potentials were assessed. Anodal and cathodal Cb-tDCS respectively induced an immediate increase and decrease in the trigeminal component in Crus I/II but no aftereffects were observed 20 min post-stimulation. In S1, Cb-tDCS resulted in polarity and intensity-dependent modulation of the N1 component during stimulation, which was opposite to the changes induced in Crus I/II, as well as a polarity-dependent modulation after stimulation. In addition, anodal Cb-tDCS was associated with reduced GAD65-67 immunoreactivity in S1, whereas vGLUT1 remained unchanged. While power spectrum analysis revealed no changes in Crus I/II, Cb-tDCS induced polarity-dependent post-stimulation changes in S1 spectral power, with higher values after cathodal stimulation. These findings show that Cb-tDCS differentially modulates sensory processing in cerebellar and cortical circuits. While cerebellar effects are mainly transient, stimulation induces longer-lasting changes in the remote cortical area investigated, S1. This underscores the need to consider both local and distant network effects when applying Cb-tDCS in translational and clinical settings.

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.

This study examines neurophysiological changes in microgravity by comparing EEG data from two ground analog 60-day head-down tilt bed rest (HDBR) experiments (ESA/DLR “Cocktail” and “RSL”) and the NEUROSPAT experiment in space. The primary objective was to determine whether HDBR could effectively model spaceflight’s impact on the human brain’s EEG signal. In the HDBR dataset, increases in relative delta (2–4 Hz) ( p < 0.01) and theta (4–8 Hz) ( p < 0.001) power bands were observed during a two-month HDBR experiment, predominantly in the left temporal and parieto-occipital regions. Conversely, the NEUROSPAT dataset showed a significant increase in beta (12–30 Hz) ( p < 0.05) power in the left somatosensory cortex during in-flight conditions, suggesting a potential adaptation to disrupted proprioceptive input and motor control in microgravity. The contrasting findings between the two datasets indicate that while HDBR can simulate some aspects of microgravity, it may not serve as a model for all central nervous system changes, especially those related to proprioception and motor functions. This highlights the need for further research, including larger sample sizes, consistent EEG recording conditions, and integration of additional physiological and cognitive markers to fully understand the effects of prolonged microgravity.

Flow, a state of deep absorption in an activity, is linked to enhanced performance and well-being. Mindfulness, emphasizing present-moment awareness and acceptance, may promote flow. This systematic review and meta-analysis examined the impact of mindfulness-based interventions (MBIs) on flow state and trait in randomized controlled trials (RCTs). Following PRISMA guidelines, PubMed, Scopus, ProQuest, and Google Scholar were searched (2022, updated January 2024). Inclusion criteria followed PICOS standards: healthy participants following a MBI or control intervention, using validated flow assessments. Exclusion criteria were non-peer-reviewed, qualitative studies, non-RCT designs, and clinical populations. Risk of bias was assessed with Cochrane's RoB2 tool. Eight RCTs (293 participants, 52% female, median intervention duration 7 weeks) met inclusion criteria. The random-effects meta-analysis showed a positive effect of MBIs on flow outcomes (SMD = 0.777, 95% CI [0.505, 1.049], p < 0.0001), with low heterogeneity (I2 = 22.59%, PI = [0.3230, 1.2314]). Publication bias was minimal, as indicated by Egger's test (p = 0.108) and trim-and-fill analyses. Although 50% of the included studies were rated as having a high risk of bias, sensitivity analyses did not reveal important deviations from mean effect. Studies suggest that MBIs meaningfully enhance flow state and trait. Mechanistic insights suggest that MBIs improve flow by enhancing attention, present-moment awareness, and reducing self-critical thoughts. However, small sample sizes and high risk of bias warrant caution. Future research should investigate dose-response and follow-up effects of interventions on specific dimensions of flow and ensure a rigorous assessment of bias risk and evidence synthesis.

Spaceflight exposes astronauts to unique conditions like microgravity, which may affect brain function, though it remains underexplored compared to other physiological systems. Astronauts often report temporary neurological symptoms, such as disorientation, visual disturbances, and motor issues, potentially linked to structural and electrophysiological brain changes. To investigate this, electroencephalography (EEG) is a reliable tool to study brain activity in space, measuring oscillatory activity and functional connectivity (FC). This study analyzed EEG data from five male astronauts during three stages: pre-flight, during low Earth orbit (LEO), and post-flight in a 2-min task-free eyes-closed (EC) condition followed by another 2-min of eyes-open (EO) condition. The focus was on beta band (12–30 Hz) activity, which is associated with motor control and proprioception. Results showed increased beta power during spaceflight when compared to pre-flight (EC: p < 0.01) and post-flight (EC: p < 0.01; EO: p < 0.05) conditions. FC strength also increased during spaceflight when compared to pre-flight (EO: p < 0.05) and post-flight (EC: p < 0.01; EO: p < 0.01) conditions. These differences were found primarily in the sensorimotor cortex (SMC) and frontotemporal regions, suggesting the brain’s adaptation to altered vestibular and proprioceptive inputs during microgravity. As these results reflect astronaut’s movement adaptation to microgravity, this study highlights the importance of understanding central nervous system (CNS) changes during spaceflights to ensure optimal performance and protect astronaut’s health during long-duration missions.

While the role of brain rhythms in respiratory and speech motor control has been mainly explored during brief utterances, the specific involvement of brain rhythms in the transition of regulating subglottic pressure phases which are concomitant to specific muscle activation during prolonged phonation remains unexplored. This study investigates whether power spectral variations of the electroencephalogram brain rhythms are related specifically to prolonged phonation phases. High-density EEG and surface EMG were recorded in nineteen healthy participants while they repeatedly produced the syllable [pa] without taking a new breath, until reaching respiratory exhaustion. Aerodynamic, acoustic, and electrophysiological signals were analyzed to detect the brain areas involved in different phases of prolonged phonation. Each phase was defined by successive thoracic and abdominal muscle activity maintaining estimated subglottic pressure. The results showed significant changes in power spectrum, with desynchronization and synchronization in delta, theta, low-alpha, and high-alpha bands during transitions among the phases. Brain source analysis estimated that the first phase (P1), associated with vocal initiation and elastic rib cage recoil, involved frontal regions, suggesting a key role in voluntary phonation preparation. Subsequent phases (P2, P3, P4) showed multiband dynamics, engaging motor and premotor cortices, anterior cingulate, sensorimotor regions, thalamus, and cerebellum, indicating progressive adaptation and fine-tuning of respiratory and articulatory muscle control. Additionally, the involvement of temporal and insular regions in delta rhythm suggests a role in maintaining phonetic representation and preventing spontaneous verbal transformations. These findings provide new insights into the mechanisms and brain regions involved in prolonged phonation. These findings pave the way for applications in vocal brain-machine interfaces, clinical biofeedback for respiratory and vocal disorders, and the development of more ecologically valid paradigms in speech neuroscience.

Sensory perception emerges from the integration of multiple inputs from different sensory modalities, a process previously attributed to higher-order cortices. However, increasing evidence suggests that the primary visual cortex also processes nonvisual stimuli. Here, we investigated the response of the primary visual cortex to visual, auditory and somatosensory stimuli in awake, head-fixed mice using evoked local field potentials, multi- and single-unit recordings. Our results demonstrate that the primary visual cortex responds to auditory and somatosensory inputs with distinct frequency band modulations and firing rate patterns across monocular and binocular regions. Notably, somatosensory stimuli elicited the fastest response latencies, suggesting a privileged role in murine sensory processing. Auditory and somatosensory stimuli modulated the primary visual cortex activity similarly to contralateral visual inputs, whereas ipsilateral visual stimulation resulted in weaker responses. These findings indicate that the primary visual cortex is not solely dedicated to vision but also responds to auditory and somatosensory stimuli, supporting a potential role in multisensory processing.

Patients with Duchenne muscular dystrophy (DMD) commonly show specific cognitive deficits in addition to a severe muscle impairment caused by the absence of dystrophin expression in skeletal muscle. These cognitive deficits have been related to the absence of dystrophin in specific regions of the central nervous system, notably cerebellar Purkinje cells (PCs). Dystrophin has recently been involved in GABAA receptors clustering at postsynaptic densities, and its absence, by disrupting this clustering, leads to decreased inhibitory input to PC. We performed an in vivo electrophysiological study of the dystrophin-deficient muscular dystrophy X-linked (mdx) mouse model of DMD to compare PC firing and local field potential (LFP) in alert mdx and control C57Bl/10 mice. We found that the absence of dystrophin is associated with altered PC firing and the emergence of fast (~160-200 Hz) LFP oscillations in the cerebellar cortex of alert mdx mice. These abnormalities were not related to the disrupted expression of calcium-binding proteins in cerebellar PC. We also demonstrate that cerebellar long-term depression is altered in alert mdx mice. Finally, mdx mice displayed a force weakness, mild impairment of motor coordination and balance during behavioural tests. These findings demonstrate the existence of cerebellar dysfunction in mdx mice. A similar cerebellar dysfunction may contribute to the cognitive deficits observed in patients with DMD.

The present study aimed to investigate the spontaneous dynamics of large-scale brain networks underlying mindfulness as a dispositional trait, through resting-state electroencephalography (EEG) microstates analysis. Eighteen participants had attended a standardized mindfulness-based stress reduction training (MBSR), and 18 matched waitlist individuals (CTRL) were recorded at rest while they were passively exposed to auditory stimuli. Participants' mindfulness traits were assessed with the Five Facet Mindfulness Questionnaire (FFMQ). To further explore the relationship between microstate dynamics at rest and mindfulness traits, participants were also asked to rate their experience according to five phenomenal dimensions. After training, MBSR participants showed a highly significant increase in FFMQ score, as well as higher observing and non-reactivity FFMQ sub-scores than CTRL participants. Microstate analysis revealed four classes of microstates (A-D) in global clustering across all subjects. The MBSR group showed lower duration, occurrence and coverage of microstate C than the control group. Moreover, these microstate C parameters were negatively correlated to non-reactivity sub-scores of FFMQ across participants, whereas the microstate A occurrence was negatively correlated to FFMQ total score. Further analysis of participants' self-reports suggested that MBSR participants showed a better sensory-affective integration of auditory interferences. In line with previous studies, our results suggest that temporal dynamics of microstate C underlie specifically the non-reactivity trait of mindfulness. These findings encourage further research into microstates in the evaluation and monitoring of the impact of mindfulness-based interventions on the mental health and well-being of individuals.

In this study, we investigated the electrical brain responses in a high-density EEG array (64 electrodes) elicited specifically by the word memory cue in the Think/No-Think paradigm in 46 participants. In a first step, we corroborated previous findings demonstrating sustained and reduced brain electrical frontal and parietal late potentials elicited by memory cues following the No-Think (NT) instructions as compared to the Think (T) instructions. The topographical analysis revealed that such reduction was significant 1000 ms after memory cue onset and that it was long-lasting for 1000 ms. In a second step, we estimated the underlying brain generators with a distributed method (swLORETA) which does not preconceive any localization in the gray matter. This method revealed that the cognitive process related to the inhibition of memory retrieval involved classical motoric cerebral structures with the left primary motor cortex (M1, BA4), thalamus, and premotor cortex (BA6). Also, the right frontal-polar cortex was involved in the T condition which we interpreted as an indication of its role in the maintaining of a cognitive set during remembering, by the selection of one cognitive mode of processing, Think, over the other, No-Think, across extended periods of time, as it might be necessary for the successful execution of the Think/No-Think task.

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.