Mu Rhythm Research Articles

Influences of speaking task demands on sensorimotor oscillations in adults who stutter: Implications for speech motor control

ObjectiveMotivated by previous inconsistent findings, this study aims to improve understanding of sensorimotor beta (β; 15–30 Hz) and alpha (α; 8–14 Hz) speech-related power differences between stuttering and non-stuttering adults. MethodsElectroencephalography was recorded as adults who stutter (AWS) and matched fluent controls answered questions in Quiet and Informational Masked backgrounds. Bilateral sensorimotor β and α power during speech planning and execution were measured from mu (μ) rhythm components. ResultsCompared to controls, AWS exhibited reduced left hemisphere β and α power in both speaking conditions during speech planning and execution. AWS displayed reduced left α power in the Informational Masking compared to Quiet. Within AWS β and α power, which were tightly coupled, oppositely predicted stuttering severity and β-α dissociation (β minus α) was the strongest predictor. ConclusionNeither β nor α power are reliable markers of speech motor stability due to their sensitivity to speech task automaticity. However, relationships between these two sensorimotor rhythms warrant further investigation for understanding motor control. SignificanceData help explain previous mixed findings in reference to extant models of speech motor control in stuttering and may have clinical implications for developing neurostimulation protocols targeting improved speech fluency.

Motor-related oscillations reveal the involvement of sensorimotor processes during recognition memory

Certain object properties may render an item as more memorable than others. One such property is manipulability, or the extent to which an object can be interacted with using our hands. This study sought to determine if the manipulability of an item modulates memory task performance on both a behavioural and neural level. We recorded electroencephalography (EEG) from a large sample of right-handed individuals (N = 53) during a visual item recognition memory task. The task contained stimuli of both high and low manipulability. Analysis focused on activity in the theta rhythm (3.5–7 Hz), which has been implicated in sensorimotor integration, and the mu rhythm (8–14 Hz), the primary oscillation associated with sensorimotor related behaviours. At both encoding and retrieval, theta oscillations were greater over the left motor region for high manipulability stimuli, suggesting that an item’s sensorimotor properties are assessed immediately upon presentation. Manipulability did not affect activity in the mu rhythm. However, mu oscillations over the left motor region were lower during the retrieval of old versus new items and response time was faster for old items, aligning with the cortical reinstatement hypothesis. These results collectively reveal an association between motor oscillations and memory processes, highlight the involvement of sensorimotor processing at both encoding and retrieval.

Motor sequence-specific learning-induced corticospinal plasticity is biased towards sensorimotor mu rhythm peak phases.

Motor cortical (M1) transcranial magnetic stimulation (TMS) interventions increase corticospinal output and improve motor learning when delivered during sensorimotor mu rhythm trough but not peak phases, suggesting that the mechanisms supporting motor learning may be most active during mu trough phases. Based on these findings, we predicted that motor sequence learning-related corticospinal plasticity would be most evident when measured during mu trough phases. Healthy adults were assigned to either a sequence or random group. Participants in the sequence group practiced the implicit serial reaction time task (SRTT), which contained an embedded, repeating 12-item sequence. Participants in the random group practiced a version of the SRTT that contained no sequence. We measured mu phase-independent and mu phase-dependent MEP amplitudes using EEG-informed single-pulse TMS before, immediately, and 30 minutes after the SRTT in both groups. All participants performed a retention test one hour after SRTT acquisition. In both groups, mu phase-independent MEP amplitudes increased following SRTT acquisition, but the pattern of mu phase-dependent MEP amplitude increases after SRTT acquisition differed between groups. Relative to the random group, the sequence group showed greater increases in peak-specific corticospinal output 30 minutes after SRTT acquisition. Contrary to our original hypothesis, results revealed that motor sequence learning recruits peak-rather than trough-specific neurophysiological mechanisms. These findings suggest that mu peak phases may provide protected time windows for motor memory consolidation and demonstrate the presence of a mu phase-dependent motor learning mechanism in the human brain.

EEG sensorimotor rhythms dynamics in children with cerebral palsy during the course of neurorehabilitation, depending in the success of their imagination of movements

An analysis of changes in sensorimotor rhythms of the electroencephalogram (EEG) and features of the restoration of motor functions during a course of neurorehabilitation using a non-invasive brain-computer-hand exoskeleton interface was carried out in 50 children aged 7–15 years of both sexes suffering from cerebral palsy (CP). EEG was recorded in 32 leads under conditions of rest and kinesthetic imagination of hand extension movements. Depending on the success of the classifier program in determining imaginary states based on the EEG pattern, the children were divided into two groups – with high and low success when imagining movements. In children of the studied groups, when undergoing a course of neurorehabilitation, differences were revealed in the nature of changes in EEG amplitude in the sensorimotor mu (8–13 Hz) and beta (15–25 Hz) rhythms frequency ranges. When imagining movements of the right hand, intergroup differences for the mu rhythm reached the level of statistical significance in the medial electrodes of the fronto-central and parietal areas of the neocortex, for the beta rhythm – in the medial lead of the parietal region. Children in the group with high success showed a decrease or slight increase in the amplitude of the mu- and beta rhythm in these brain areas at the last session of the course compared to the first. We hypothesize that children in this group are not only better able to kinesthetically imagine movements throughout the course of neurorehabilitation, but also, based on feedback signals, learn to effectively adjust their strategies for movement imagination. Children in the group with low success showed an increase in the amplitude of sensorimotor rhythms in these brain areas, indicating the development of inhibition in the frontoparietal motor network. Patients, whose conditions were determined more accurately by the classifier, achieved higher rates of motor rehabilitation. The results of the study are important for clarifying the brain mechanisms of motor functions restoration in patients with cerebral palsy under the influence of a course of neurorehabilitation.

Study of the functioning of mirror neurons in normal conditions, in neurological and psychiatric diseases: a systematic review of the literature

The mirror neuron system is a fundamental brain system of the brain that provides the ability to understand the actions of others and plays a key role in motor learning and empathy. In this review, we have thoroughly examined the internal and external factors that influence the functioning of the mirror neuron system. Issues such as difficulties in interpreting the mu rhythm and its relationship to theory of mind, which are particularly salient when analysing the mirror neuron system in the context of mental illness, have been identified. The role of the mirror neuron system in the formation of various mental disorders and neurological diseases has been considered. Additionally, a promising direction for future research is highlighted — the study of the mirror neuron system in the context of Parkinson's disease, focusing on the peculiarities of the functioning of the dopaminergic system of the mirror neuron system under normal conditions and in the presence of pathology. Prospective directions for further research are suggested, including the analysis of the mu-rhythm, the role of mentalization, studying the mirror neuron system in mental and neurological diseases.

Differences in Mu rhythm when seeing grasping/motor actions in a real context versus on screens

Mu rhythm (∼8–12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance. We recorded subjects’ brain activities with electroencephalographic (EEG) procedures, and analyzed Mu rhythm present in left (C3) and right (C4) somatosensory areas in relation to the 24 motor activities included in each visual stimulus (screen vs. reality) (24 motor and grasping actions x 40 participants x 2 conditions = 1920 trials). We found lower Mu spectral power in the somatosensory area after the onset of the motor actions in real performance than on-screened content, more pronounced in the left hemisphere. In our results, the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. This could be relevant in research areas where the somatosensory cortex is important, such as online learning, virtual reality, or brain-computer interfaces.

Sensorimotor brain–computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.

Effects of observing own/others hand movement in different perspectives on mu rhythm suppression: an EEG study

BackgroundPrevious studies have reported that the sense of “self” is associated with specific brain regions and neural network activities. In addition, the mirror system, which functions when executing or observing an action, might contribute to differentiating the self from others and form the basis of the sense of self as a fundamental physical representation. This study investigated whether differences in mu suppression, an indicator of mirror system activity, reflect cognitions related to self-other discrimination.MethodsThe participants were 30 of healthy college students. The participants observed short video clips of hand movements performed by themselves or actors from two perspectives (i.e., first-person and third-person). The electroencephalogram (EEG) mu rhythm (8–13 Hz) was measured during video observation as an index of mirror neuron system activity. EEG activity related to self-detection was analyzed using participants’ hand movements as self-relevant stimuli.ResultsThe results showed that mu suppression in the 8–13-Hz range exhibited perspective-dependent responses to self/other stimuli. There was a significant self-oriented mu suppression response in the first-person perspective. However, the study found no significant response orientation in the third-person perspective. The results suggest that mirror system activity may involve self-other discrimination differently depending on the perspective.ConclusionsIn summary, this study examined the mirror system’s activity for self and others using the EEG’s mu suppression. As a result, it was suggested that differences in self and others or perspectives may influence mu suppression.

Unified theory of alpha, mu, and tau rhythms via eigenmodes of brain activity.

A compact description of the frequency structure and topography of human alpha-band rhythms is obtained by use of the first four brain activity eigenmodes previously derived from corticothalamic neural field theory. Just two eigenmodes that overlap in frequency are found to reproduce the observed topography of the classical alpha rhythm for subjects with a single, occipitally concentrated alpha peak in their electroencephalograms. Alpha frequency splitting and relative amplitudes of double alpha peaks are explored analytically and numerically within this four-mode framework using eigenfunction expansion and perturbation methods. These effects are found to result primarily from the different eigenvalues and corticothalamic gains corresponding to the eigenmodes. Three modes with two non-overlapping frequencies suffice to reproduce the observed topography for subjects with a double alpha peak, where the appearance of a distinct second alpha peak requires an increase of the corticothalamic gain of higher eigenmodes relative to the first. Conversely, alpha blocking is inferred to be linked to a relatively small attention-dependent reduction of the gain of the relevant eigenmodes, whose effect is enhanced by the near-critical state of the brain and whose sign is consistent with inferences from neural field theory. The topographies and blocking of the mu and tau rhythms within the alpha-band are explained analogously via eigenmodes. Moreover, the observation of three rhythms in the alpha band is due to there being exactly three members of the first family of spatially nonuniform modes. These results thus provide a simple, unified description of alpha band rhythms and enable experimental observations of spectral structure and topography to be linked directly to theory and underlying physiology.

Increased sensorimotor activity during categorisation of emotionally ambiguous faces.

Actions are rarely devoid of emotional content. Thus, a more complete picture of the neural mechanisms underlying the mental simulation of observed actions requires more research using emotion information. The present study used high-density electroencephalography to investigate mental simulation associated with facial emotion categorisation. Alpha-mu rhythm modulation was measured at each frequency, from 8Hz to 13 Hz, to infer the degree of sensorimotor simulation. Results suggest the sensitivity of the sensorimotor activity to emotional information, because (1) categorising static images of neutral faces as happy or sad was associated with stronger suppression in the central region than categorising clearly happy faces, (2) there was preliminary evidence indicating that the strongest suppression in the central region was in response to neutral faces, followed by sad and then happy faces and (3) in the control task, which required categorising images with the head oriented right, left, or forward as right or left, differences between conditions showed a pattern more indicative of task difficulty rather than sensorimotor engagement. Dissociable processing of emotional information in facial expressions and directionality information in head orientations was further captured in beta band activity (14-20 Hz). Stronger mu suppression to neutral faces indicates that sensorimotor simulation extends beyond crude motor mimicry. We propose that mu rhythm responses to facial expressions may serve as a biomarker for empathy circuit activation. Future research should investigate whether atypical or inconsistent mu rhythm responses to facial expressions indicate difficulties in understanding or sharing emotions.

EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: a scoping review

ObjectiveTherapeutic interventions for children and young people with dystonia and dystonic/dyskinetic cerebral palsy are limited. EEG-based neurofeedback is emerging as a neurorehabilitation tool. This scoping review maps research investigating EEG-based sensorimotor neurofeedback in adults and children with neurological motor impairments, including augmentative strategies. MethodsMEDLINE, CINAHL and Web of Science databases were searched up to 2023 for relevant studies. Study selection and data extraction were conducted independently by at least two reviewers. ResultsOf 4380 identified studies, 133 were included, only three enrolling children. The most common diagnosis was adult-onset stroke (77%). Paradigms mostly involved upper limb motor imagery or motor attempt. Common neurofeedback modes included visual, haptic and/or electrical stimulation. EEG parameters varied widely and were often incompletely described. Two studies applied augmentative strategies. Outcome measures varied widely and included classification accuracy of the Brain-Computer Interface, degree of enhancement of mu rhythm modulation or other neurophysiological parameters, and clinical/motor outcome scores. Few studies investigated whether functional outcomes related specifically to the EEG-based neurofeedback. ConclusionsThere is limited evidence exploring EEG-based sensorimotor neurofeedback in individuals with movement disorders, especially in children. Further clarity of neurophysiological parameters is required to develop optimal paradigms for evaluating sensorimotor neurofeedback. SignificanceThe expanding field of sensorimotor neurofeedback offers exciting potential as a non-invasive therapy. However, this needs to be balanced by robust study design and detailed methodological reporting to ensure reproducibility and validation that clinical improvements relate to induced neurophysiological changes.

A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.

Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT). Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%. This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

Effects of home-based EEG neurofeedback training as a non-pharmacological intervention for Parkinson's disease

ObjectivesAberrant movement-related cortical activity has been linked to impaired motor function in Parkinson's disease (PD). Dopaminergic drug treatment can restore these, but dosages and long-term treatment are limited by adverse side-effects. Effective non-pharmacological treatments could help reduce reliance on drugs. This experiment reports the first study of home-based electroencephalographic (EEG) neurofeedback training as a non-pharmacological candidate treatment for PD. Our primary aim was to test the feasibility of our EEG neurofeedback intervention in a home setting. MethodsSixteen people with PD received six home visits comprising symptomology self-reports, a standardised motor assessment, and a precision handgrip force production task while EEG was recorded (visits 1, 2 and 6); and 3 × 1-hr EEG neurofeedback training sessions to supress the EEG mu rhythm before initiating handgrip movements (visits 3 to 5). ResultsParticipants successfully learned to self-regulate mu activity, and this appeared to expedite the initiation of precision movements (i.e., time to reach target handgrip force off-medication pre-intervention = 628 ms, off-medication post-intervention = 564 ms). There was no evidence of wider symptomology reduction (e.g., Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III Motor Examination, off-medication pre-intervention = 29.00, off-medication post intervention = 30.07). Interviews indicated that the intervention was well-received. ConclusionBased on the significant effect of neurofeedback on movement-related cortical activity, positive qualitative reports from participants, and a suggestive benefit to movement initiation, we conclude that home-based neurofeedback for people with PD is a feasible and promising non-pharmacological treatment that warrants further research.

Impacts of Kinematic Information on Action Anticipation and the Related Neurophysiological Associations in Volleyball Experts.

In this study, we investigated the cognitive mechanisms underlying action anticipation in volleyball players, especially concerned with the differences between experts and amateurs. Participants included both expert (male, N = 26) and amateur (male, N = 23) volleyball players, who were asked to predict spiking movements containing high, medium, and low levels of kinematic information while their electrophysiological activities were recorded. The high-information stimuli included the whole spiking action, the medium-information stimuli ended at 120 ms, and the low-information stimuli ended at 160 ms before hand-ball contact. The results showed that experts significantly outperformed amateurs in both prediction accuracy (68% in experts vs. 55% in amateurs) and reaction time (475.09 ms in experts vs. 725.81 ms in amateurs) under the medium-information condition. Analysis of alpha rhythm activity revealed that experts exhibited the strongest desynchronization under the low-information condition, suggesting increased attentional engagement. In contrast, amateurs showed the weakest desynchronization under the medium-information condition. Furthermore, mu rhythm activity analysis showed greater desynchronization in the duration of 100-300 ms before hand-ball contact for experts, correlating with their higher anticipation accuracy. These findings highlight the significant kinematic information-processing abilities of volleyball experts and elucidate the neural mechanisms underlying efficient attentional engagement and mirroring. Therefore, this study provides valuable insights for the development of targeted training programs through which to enhance athletic performance.

Applications of Brain Wave Classification for Controlling an Intelligent Wheelchair

The independence and autonomy of both elderly and disabled people have been a growing concern in today’s society. Therefore, wheelchairs have proven to be fundamental for the movement of these people with physical disabilities in the lower limbs, paralysis, or other type of restrictive diseases. Various adapted sensors can be employed in order to facilitate the wheelchair’s driving experience. This work develops the proof concept of a brain–computer interface (BCI), whose ultimate final goal will be to control an intelligent wheelchair. An event-related (de)synchronization neuro-mechanism will be used, since it corresponds to a synchronization, or desynchronization, in the mu and beta brain rhythms, during the execution, preparation, or imagination of motor actions. Two datasets were used for algorithm development: one from the IV competition of BCIs (A), acquired through twenty-two Ag/AgCl electrodes and encompassing motor imagery of the right and left hands, and feet; and the other (B) was obtained in the laboratory using an Emotiv EPOC headset, also with the same motor imaginary. Regarding feature extraction, several approaches were tested: namely, two versions of the signal’s power spectral density, followed by a filter bank version; the use of respective frequency coefficients; and, finally, two versions of the known method filter bank common spatial pattern (FBCSP). Concerning the results from the second version of FBCSP, dataset A presented an F1-score of 0.797 and a rather low false positive rate of 0.150. Moreover, the correspondent average kappa score reached the value of 0.693, which is in the same order of magnitude as 0.57, obtained by the competition. Regarding dataset B, the average value of the F1-score was 0.651, followed by a kappa score of 0.447, and a false positive rate of 0.471. However, it should be noted that some subjects from this dataset presented F1-scores of 0.747 and 0.911, suggesting that the movement imagery (MI) aptness of different users may influence their performance. In conclusion, it is possible to obtain promising results, using an architecture for a real-time application.

Exploring empathic engagement in immersive media: An EEG study on mu rhythm suppression in VR.

This study investigates the influence of immersive media, particularly Virtual Reality (VR), on empathic responses, in comparison to traditional television (TV), using electroencephalography (EEG). We employed mu rhythm suppression as a measurable neural marker to gauge empathic engagement, as its increase generally signifies heightened empathic responses. Our findings exhibit a greater mu rhythm suppression in VR conditions compared to TV conditions, suggesting a potential enhancement in empathic responses with VR. Furthermore, our results revealed that the strength of empathic responses was not confined to specific actions depicted in the video clips, underscoring the possibility of broader implications. This research contributes to the ongoing discourse on the effects of different media environments on empathic engagement, particularly emphasizing the unique role of immersive technologies such as VR. It invites further investigation into how such technologies can shape and potentially enhance the empathic experience.

Mu-Suppression Neurofeedback Training Targeting the Mirror Neuron System: A Pilot Study

Neurofeedback training (NFT) is a promising adjuvant intervention method. The desynchronization of mu rhythm (8–13 Hz) in the electroencephalogram (EEG) over centro-parietal areas is known as a valid indicator of mirror neuron system (MNS) activation, which has been associated with social skills. Still, the effect of neurofeedback training on the MNS requires to be well investigated. The present study examined the possible impact of NFT with a mu suppression training protocol encompassing 15 NFT sessions (45 min each) on 16 healthy neurotypical participants. In separate pre- and post-training sessions, 64-channel EEG was recorded while participants (1) observed videos with various types of movements (including complex goal-directed hand movements and social interaction scenes) and (2) performed the "Reading the Mind in the Eyes Test" (RMET). EEG source reconstruction analysis revealed statistically significant mu suppression during hand movement observation across MNS-attributed fronto-parietal areas after NFT. The frequency analysis showed no significant mu suppression after NFT, despite the fact that numerical mu suppression appeared to be visible in a majority of participants during goal-directed hand movement observation. At the behavioral level, RMET accuracy scores did not suggest an effect of NFT on the ability to interpret subtle emotional expressions, although RMET response times were reduced after NFT. In conclusion, the present study exhibited preliminary and partial evidence that mu suppression NFT can induce mu suppression in MNS-attributed areas. More powerful experimental designs and longer training may be necessary to induce substantial and consistent mu suppression, particularly while observing social scenarios.

Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces

ABSTRACT The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as ‘BCI inefficiency’. Although past research has attempted to find a solution by investigating factors influencing users’ MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user’s ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance.

Decoding familiar visual object categories in the mu rhythm oscillatory response

Whilst previous research has linked attenuation of the mu rhythm to the observation of specific visual categories, and even to a potential role in action observation via a putative mirror neuron system, much of this work has not considered what specific type of information might be coded in this oscillatory response when triggered via vision. Here, we sought to determine whether the mu rhythm contains content-specific information about the identity of familiar (and also unfamiliar) graspable objects. In the present study, right-handed participants (N = 27) viewed images of both familiar (apple, wine glass) and unfamiliar (cubie, smoothie) graspable objects, whilst performing an orthogonal task at fixation. Multivariate pattern analysis (MVPA) revealed significant decoding of familiar, but not unfamiliar, visual object categories in the mu rhythm response. Thus, simply viewing familiar graspable objects may automatically trigger activation of associated tactile and/or motor properties in sensorimotor areas, reflected in the mu rhythm. In addition, we report significant attenuation in the central beta band for both familiar and unfamiliar visual objects, but not in the mu rhythm. Our findings highlight how analysing two different aspects of the oscillatory response – either attenuation or the representation of information content – provide complementary views on the role of the mu rhythm in response to viewing graspable object categories.

Live vs video interaction: sensorimotor and visual cortical oscillations during action observation.

Increasingly, in the field of communication, education, and business, people are switching to video interaction, and interlocutors frequently complain that the perception of nonverbal information and concentration suffer. We investigated this issue by analyzing electroencephalogram (EEG) oscillations of the sensorimotor (mu rhythm) and visual (alpha rhythm) cortex of the brain in an experiment with action observation live and on video. The mu rhythm reflects the activity of the mirror neuron system, and the occipital alpha rhythm shows the level of visual attention. We used 32-channel EEG recorded during live and video action observation in 83 healthy volunteers. The ICA method was used for selecting the mu- and alpha-components; the Fourier Transform was used to calculate the suppression index relative to the baseline (stationary demonstrator) of the rhythms. The main range of the mu rhythm was indeed sensitive to social movement and was highly dependent on the conditions of interaction-live or video. The upper mu-range appeared to be less sensitive to the conditions, but more sensitive to different movements. The alpha rhythm did not depend on the type of movement; however, a live performance initially caused a stronger concentration of visual attention. Thus, subtle social and nonverbal perceptions may suffer in remote video interactions.