Imagined Movements Research Articles

ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network

Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. Deep learning (DL) has found extensive applications in the processing of MI signals, wherein convolutional neural networks (CNN) have demonstrated superior performance compared to conventional machine learning (ML) approaches. Nevertheless, challenges related to subject independence and subject dependence persist, while the inherent low signal-to-noise ratio of EEG signals remains a critical aspect that demands attention. Accurately deciphering intentions from EEG signals continues to present a formidable challenge. This paper introduces an advanced end-to-end network that effectively combines the efficient channel attention (ECA) and temporal convolutional network (TCN) components for the classification of motor imagination signals. We incorporated an ECA module prior to feature extraction in order to enhance the extraction of channel-specific features. A compact convolutional network model uses for feature extraction in the middle part. Finally, the time characteristic information is obtained by using TCN. The results show that our network is a lightweight network that is characterized by few parameters and fast speed. Our network achieves an average accuracy of 80.71% on the BCI Competition IV-2a dataset.

What networks in the brain system sustain imagination?

The brain cannot stop elaborating information. While the circuitries implied in processing sensory information, and those involved in programming and producing movements, have been extensively studied and characterized, what circuits elicit and sustain the endogenous activity (which might be referred to as imaginative activity) has not been clarified to a similar extent. The two areas which have been investigated most intensely are visual and motor imagery. Visual imagery mostly involves the same areas as visual processing and has been studied by having the subject face specific visual imagery tasks that are related to the use of the visual sketchpad as a component of the working memory system. Much less is known about spontaneous, free visual imagination, what circuits drive it, how and why. Motor imagery has been studied with several approaches: the neural circuits activated in the brain during performance of a movement have been compared with those involved in visually or kinaesthetically imagining performing the same movement, or in observing another person performing it. Some networks are similarly activated in these situations, although primary motor neurons are only activated during motor execution. Imagining the execution of an action seems unable to activate circuits involved in eliciting accompanying motor adjustments (such as postural adaptations) that are unconsciously (implicitly) associated to the execution of the movement. A more faithful neuronal activation is obtained through kinaesthetic motor imagination-imagining how it feels to perform the movement. Activation of sensory-motor and mirror systems, elicited by observing another person performing a transitive action, can also recruit circuits that sustain implicit motor responses that normally accompany the overt movement. This last aspect has originated the expanding and promising field of action observation therapy (AOT). The fact that the various kinds of motor imagery differentially involve the various brain networks may offer some hints on what neural networks sustain imagery in general, another activity that has an attentive component-recalling a memory, covertly rehearsing a speech, internally replaying a behaviour-and a vague, implicit component that arises from the freely flowing surfacing of internal images, not driven by intentional, conscious control.

Motor imagery therapy on upper limb motor control after stroke

BackgroundStroke is a common neurological disease, the main pathological feature of which is cerebral ischemia or bleeding caused by sudden blockage or rupture of cerebral vessels. Impaired upper limb motor control after stroke severely affects patients’ quality of life and ability to perform daily activities. Traditional rehabilitation methods mainly rely on physical therapy and drug therapy, but there are limited effects.Subjects and MethodsIt is to explore the clinical effect of motor imagery therapy in the study of upper limb motor control after stroke. Fifty patients with stroke and the influence of upper limb movement were selected as experimental subjects, 25 as an experimental group and 25 as a control group. The experimental group received six months of rehabilitation training using motor imagery therapy, and the control group received six months of rehabilitation training using traditional drug alteplase.ResultsThe experimental results were evaluated by SPSS23.0 system. P<0.05 indicated that the difference was statistically significant, and the upper limb motor function and muscle strength of 20 patients in the experimental group were significantly improved after half a year, More than 80 percent have recovered, with significant differences (P<0.05).ConclusionsAfter the motor imagination therapy is integrated into stroke patients, it can promote the reorganization of brain nerves, enhance the motor memory of muscles, improve the patients’ attention and concentration on sports, and help them recover the motor function of the upper limbs.Acknowledgement2021 Shanghai Society of Integrated Traditional Chinese and Western Medicine Community Medicine and Health Management Research Project (No. 2021-117); 2022 Shanghai Jing’an District Medical Research Fund Project (No. 2022QT05).

Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across four brain-computer interface (BCI) paradigms, we gauged their information representation capability. Rooted in comprehensive literature review findings, we proposed EEGNeX, a novel, purely ConvNet-based architecture. We pitted it against both existing cutting-edge strategies and the Mother of All BCI Benchmarks (MOABB) involving 11 distinct EEG motor imagination (MI) classification tasks and revealed that EEGNeX surpasses other state-of-the-art methods. Notably, it shows up to 2.1%–8.5% improvement in the classification accuracy in different scenarios with statistical significance (p < 0.05) compared to its competitors. This study not only provides deeper insights into designing efficient NN models for EEG data but also lays groundwork for future explorations into the relationship between bioelectric brain signals and NN architectures. For the benefit of broader scientific collaboration, we have made all benchmark models, including EEGNeX, publicly available at (https://github.com/chenxiachan/EEGNeX).

Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain-Computer Interfaces by Using Multi-Branch CNN.

A brain-computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the eegmmidb dataset and scores of 74.3% and 84.1% for the BCI IV-2a and IV-2b datasets, respectively. However, the proposed model has a bit higher computational cost, i.e., it takes around 3.5 times more computational time per sample than EEGNet_Fusion.

Changes in absolute alpha power during execution, imagination and observation of a finger movement task

Background: Neurons that fire both during execution and observation of a motor act, called mirror neurons, provide strong evidence of its existence in humans. Real, observed and imagined movements seem to share most of the same neural pathways. However, little is known about the neural mechanisms and functional processes of the different areas and the integration of information between the observation and imagined systems. Objective: Compare alpha absolute power levels in sensorimotor areas between real, imagined and observed movement conditions. Methods: The sample consisted of a group of 12 healthy young individuals (4 men and 8 women), right-handed and with mean age of 30 ± 10. The task consisted of 4 blocks of 20 trials of rhythmic flexion-extension movements of the index finger by pressing the left button of a mouse properly fixed on a table. The activation level was analyzed by measuring the absolute power of the alpha band (8-13hz). Results: ANOVA with repeated measures showed that in the real condition, alpha absolute power was higher in the electrodes Cz, P3 and Pz compared to the electrode C3. In the imagined condition, the electrodes P3 and Pz presented higher alpha absolute power compared to the electrode C3. For the observed condition, electrodes P3, Pz and P4 presented higher alpha power compared to the electrode C3. In addition, higher alpha absolute power was observed in the electrode Pz compared to the electrodes Cz and C4. Conclusion: Higher alpha activation was observed on the electrode C3 in all conditions. These findings indicate that the observed movement also shares neural circuits when compared to the real and imagined movements. It was observed that in all conditions there was a significant decrease in alpha power on the C3 electrode, which indicates activation of the motor cortex in a similar way in the conditions.

Towards hippocampal navigation for brain–computer interfaces

Automatic wheelchairs directly controlled by brain activity could provide autonomy to severely paralyzed individuals. Current approaches mostly rely on non-invasive measures of brain activity and translate individual commands into wheelchair movements. For example, an imagined movement of the right hand would steer the wheelchair to the right. No research has investigated decoding higher-order cognitive processes to accomplish wheelchair control. We envision an invasive neural prosthetic that could provide input for wheelchair control by decoding navigational intent from hippocampal signals. Navigation has been extensively investigated in hippocampal recordings, but not for the development of neural prostheses. Here we show that it is possible to train a decoder to classify virtual-movement speeds from hippocampal signals recorded during a virtual-navigation task. These results represent the first step toward exploring the feasibility of an invasive hippocampal BCI for wheelchair control.

Gestural Tendencies in Composing Pieces for Solo Clarinet in Sequenza IX for Clarinet, Garisan, and Klibat

Abstract: Gesture in a broader sense is more than a physical movement but, can also be modes of expression representing specific meanings. Movements with meaning embodies gesture and gesture exists in various artforms like music because of its capacity to express. Its capability to transform through time and space makes it akin to energy and it is parallel with the art of composing music. Musical gesture however, are being studied at different levels so as to classify and to relate different functions of gestures. This article intends to demonstrate the use of musical gestures into composing music for a solo wind instrument. Three solo pieces for clarinet were written by three different composers approaching musical composition through gestural writing. To realize the imagined sound and movements of the clarinet, the composers’ composing process concentrated more on the gestural movements and tone colours of the selected wind instrument. The musical gesture in these three solo pieces is actually capturing the movement and sense of movement through music, and its essence lies in the subconscious lines connected to create direction and motion in both pieces. This compositional approach also gives the composers more compositional freedom and enriches their compositional process.&#x0D; Keywords: music composition, musical gesture, solo pieces for clarinet, contemporary music.

Influence of Sports on Cortical Connectivity in Patients with Spinal Cord Injury-A High-Density EEG Study

Background: Minutes after an injury to the spinal cord, structural and functional reorganization of the connected brain areas may be initiated. Exercise enhances this neuroplasticity in the further course of the condition, which might modulate the connectivity patterns in brain regions responsible for movement execution and imagination. However, connectivity patterns have not been analyzed as a correlate for activity effects on neuroplasticity after spinal cord injury (SCI). We hypothesize that wheelchair sport has a modulating effect on the cortical connectivity in patients with SCI, such that distinguished activity patterns can be observed between sportive and non-sportive individuals with SCI and healthy participants. Methods: Sportive (n = 16) and non-sportive (n = 7) patients with SCI as well as sportive (n = 16) and non-sportive (n = 14) healthy participants were instructed to either observe, imagine, or conduct an observed movement while high-density EEG (HD-EEG) was recorded. Functional connectivity was computed from the recorded signals, and the coefficients were compared between groups and conditions using a non-parametric repeated measures analysis. Results: We found that depending on being sportive or not, patients with SCI and controls would react differently to the conditions, but the effects depended on the location in the brain as well as the analyzed frequency range (p &lt; 0.05). Further analysis indicates that non-sportive patients showed higher connectivity received by the right posterior parietal cortex and a lower connectivity received by the left M1 compared to sportive patients. These effects were mainly observed during movement imagination, not during movement. Sportive and non-sportive participants in the healthy control group showed smaller differences than the patients. Conclusions: The results suggest a modulative effect of sports on connectivity patterns during movement imagination and to some extent during movement. This effect was predominantly found in patients with SCI, and to a lesser extent in healthy participants with opposing connectivity patterns. We suggest that this might be due to increased cortical excitability and the elevated brain derived neurotrophic factor (BDNF) level in patients with SCI that is enhanced by exercise.

Handedness effects on imagery of dominant- versus non-dominant-hand movements: An electroencephalographic investigation.

Mental representations of our bodies are thought to influence how we interact with our surroundings. We can examine these mental representations through motor imagery, the imagination of movement using scalp EEG recordings. The visual modality of motor imagery emphasises 'seeing' the imagined movement and is associated with increased activity in the alpha rhythm (8-14 Hz) measured over the occipital regions. The kinaesthetic modality emphasises 'feeling' the movement and is associated with decreased activity in the mu rhythm (8-14 Hz) measured over the sensorimotor cortices. These two modalities can be engaged in isolation or together. We recorded EEG activity while 37 participants (17 left-hand dominant) completed an objective hand motor imagery task. Left-handers exhibited significant activity differences between occipital and motor regions only during imagery of right-hand (non-dominant-hand) movements. This difference was primarily driven by less oscillatory activity in the mu rhythm, which may reflect a shift in imagery strategy wherein participants placed more effort into generating the kinaesthetic sensations of non-dominant-hand imagery. Spatial features of 8-14 Hz activity generated from principal component analysis (PCA) provide further support for a strategy shift. Right-handers also exhibited significant differences between alpha and mu activity during imagery of non-dominant movements. However, this difference was not primarily driven by either rhythm, and no differences were observed in the group's PCA results. Together, these findings indicate that individuals imagine movement differently when it involves their dominant versus non-dominant hand, and left-handers may be more flexible in their motor imagery strategies.

EEG-based classification combining Bayesian convolutional neural networks with recurrence plot for motor movement/imagery

Electroencephalogram (EEG)-based Motor imagery (MI) is a key topic in the brain-computer interface (BCI). The EEG-based real execution and motor imagery multi-class classification tasks are also crucial, but only a few kinds of literature research it. In addition, classification accuracy still has room for improvement, and the inter-individual variability problems in BCI applications need to be solved. To address these issues, we developed a novel model (RP-BCNNs) that combines the recurrence plot (RP) and Bayesian Convolutional Neural Networks (BCNNs). First, we employ an RP computation for preprocessed EEG signals of each channel and merge all RPs of all channels into one based on the weighted average method. Then, we feed the RP features into BCNNs to classify 2-class, 3-class, 4-class, and 5-class on real/imaginary movements classification tasks. The results show that the RP-BCNNs model outperforms the state-of-the-art methods, achieving average accuracies of 92.86%, 94.12%, 91.37%, 92.61% for real movements and 94.07%, 93.77%, 90.54%, 91.85% for imaginary movements. Our findings suggest that combining complex network methods with deep learning can improve the classification performance of EEG-based BCI systems (e.g., motor imagery, emotion recognition, and epileptic seizure classification).

Asynchronous Motor Imagery BCI and LiDAR-Based Shared Control System for Intuitive Wheelchair Navigation

Mapping drivers’ thoughts directly to mobility system control would make driving more intuitive as if the mobility system is an extension of their own body. Such a system would allow patients with motor disabilities to drive, as it would not require any physical movement. In this paper, we therefore propose a brain-controlled mobility system that analyzes real-time neural signals elicited from motor imagery, an imagination of different body movements. As such asynchronous brain-computer interfaces (BCIs) are prone to error, our system contains shared control capabilities that take into consideration continuously updated information of the surrounding environment along with electroencephalogram (EEG) signals to improve navigating performance without precise and accurate control from the driver. With our shared control method that uses a wheelchair with light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensors, we held a comparative study in which participants drove our wheelchair with and without our shared control approach using either our brain-controlled system or a keyboard in a physical environment. The experimental results show that among the five participants, the three participants that failed the driving task with the asynchronous BCI-based system could also successfully complete it using our shared control approach. Furthermore, our approach narrows the gap between driving with neural signals and driving with a widely used interface in terms of both elapsed time and safety. These results show not only the potential of brain signals for driving, but also the applicability of BCIs to real-life situations.

Virtual reality and motor imagery for early post-stroke rehabilitation

BackgroundMotor impairment is a common consequence of stroke causing difficulty in independent movement. The first month of post-stroke rehabilitation is the most effective period for recovery. Movement imagination, known as motor imagery, in combination with virtual reality may provide a way for stroke patients with severe motor disabilities to begin rehabilitation.MethodsThe aim of this study is to verify whether motor imagery and virtual reality help to activate stroke patients’ motor cortex. 16 acute/subacute (< 6 months) stroke patients participated in this study. All participants performed motor imagery of basketball shooting which involved the following tasks: listening to audio instruction only, watching a basketball shooting animation in 3D with audio, and also performing motor imagery afterwards. Electroencephalogram (EEG) was recorded for analysis of motor-related features of the brain such as power spectral analysis in the alpha and beta frequency bands and spectral entropy. 18 EEG channels over the motor cortex were used for all stroke patients.ResultsAll results are normalised relative to all tasks for each participant. The power spectral densities peak near the alpha band for all participants and also the beta band for some participants. Tasks with instructions during motor imagery generally show greater power spectral peaks. The p-values of the Wilcoxon signed-rank test for band power comparison from the 18 EEG channels between different pairs of tasks show a 0.01 significance of rejecting the band powers being the same for most tasks done by stroke subjects. The motor cortex of most stroke patients is more active when virtual reality is involved during motor imagery as indicated by their respective scalp maps of band power and spectral entropy.ConclusionThe resulting activation of stroke patient’s motor cortices in this study reveals evidence that it is induced by imagination of movement and virtual reality supports motor imagery. The framework of the current study also provides an efficient way to investigate motor imagery and virtual reality during post-stroke rehabilitation.

A Phase-based EEG Epoch Selection Method for Decoding Bi-directional Hand Movement Imagination in Stroke Patients.

Electroencephalogram (EEG) based non-invasive Brain Computer Interface (BCI) system is gaining significant attention as a promising solution for stroke rehabilitation. Accurate selection of informative EEG time segment, that accommodates the specific neural activity patterns associated with the underlying mental task can help to improve the efficacy of the BCI system. In this work, we propose a phase-based EEG epoch selection algorithm to extract the discriminative EEG time segment corresponding to bi-directional hand motor imagery. The imagined center-out hand movement in two directions is decoded using the selected epoch of the EEG, recorded from 16 stroke patients with hemiparesis and specifically hand weakness. Phase Lock Value (PLV) EEG features extracted from the selected EEG epoch is used as discriminative feature for binary classification of imagined hand movement direction using Linear Discriminant Analysis. The use of selected EEG epoch yielded an improvement of 11.5% and 11.7% in the average direction classification accuracy of calibration and feedback session data respectively, compared to the baseline method employing the whole EEG signal. In addition to improvement in decoding accuracy, the epoch selection also yielded an average Information Transfer Rate (ITR) of 39.8±24.6 bits per minute, which is 86% improvement compared to the baseline method.Clinical Relevance- The proposed Motor Imagery (MI)-BCI system may be of clinical relevance as an active rehabilitation tool for stroke-affected patients, to enhance neural plasticity and recovery of centre-out activities of affected hand and forms a strong platform for MI-BCI coupled with exoskeletons or prosthesis rehabilitation.

Constraints on hand-foot coordination associated with phase dependent modulation of corticospinal excitability during motor imagery.

Interlimb coordination involving cyclical movements of hand and foot in the sagittal plane is more difficult when the limbs move in opposite directions compared with the same direction (directional constraint). Here we first investigated whether the directional constraint on hand-foot coordination exists in motor imagery (imagined motion). Participants performed 10 cyclic coordinated movements of right wrist flexion-extension and right ankle dorsiflexion-plantarflexion as quickly and precisely as possible, in the following three conditions; (1) actual movements of the two limbs, (2) imaginary movements of the two limbs, and (3) actual movement of one limb combined with imaginary movement of the other limb. Each condition was performed under two directions; the same and the opposite direction. Task execution duration was measured as the time between the first and second press of a button by the participants. The opposite directional movement took a significantly longer time than did the same directional movement, irrespective of the condition type. This suggests that directional constraint of hand-foot coordination occurs even in motor imagery without actual motor commands or kinesthetic signals. We secondarily examined whether the corticospinal excitability of wrist muscles is modulated in synchronization with an imaginary foot movement to estimate the neural basis of directional constraint on imaginary hand-foot coordination. The corticospinal excitability of the forearm extensor in resting position increased during dorsiflexion and decreased during plantarflexion similarly in both actual and imaginary foot movements. This corticospinal modulation depending on imaginary movement phase likely produces the directional constraint on the imaginary hand-foot coordination.

Towards Improving Motor Imagery Brain–Computer Interface Using Multimodal Speech Imagery

PurposeThe brain–computer interface (BCI) based on motor imagery (MI) has attracted extensive interest due to its spontaneity and convenience. However, the traditional MI paradigm is limited by weak features in evoked EEG signal, which often leads to lower classification performance.MethodsIn this paper, a novel paradigm is proposed to improve the BCI performance, by the speech imaginary combined with silent reading (SR) and writing imagery (WI), instead of imagining the body movements. In this multimodal (imaginary voices and movements) paradigm, the subjects silently read Chinese Pinyin (pronunciation) and imaginarily write the Chinese characters, according to a cue.ResultsEight subjects participated in binary classification tasks, by carrying out the traditional MI and the proposed paradigm in different experiments for comparison. 77.03% average classification accuracy was obtained by the new paradigm versus 68.96% by the traditional paradigm.ConclusionThe results of experiments show that the proposed paradigm evokes stronger features, which benefits the classification. This work opens a new view on evoking stronger EEG features by multimodal activities/stimuli using specific paradigms for BCI.

Is there a continuum of agentive awareness across physical and mental actions? The case of quasi-movements

While humans routinely distinguish between physical and mental actions, overt movements (OM) and kinesthetically imagined movements (IM) are often viewed as forming a continuum of activities. Here, we theoretically conceptualized this continuum hypothesis for agentive awareness related to OM and IM and tested it experimentally using quasi-movements (QM), a little studied type of covert actions, which is considered as an inner part of the OM-IM continuum. QM are performed when a movement attempt is minimized down to full extinction of overt movement and muscle activity. We asked participants to perform OM, IM and QM and collected their electromyography data. According to participants’ reports, they experienced QM as OM in terms of intentions and expected sensory feedback, while the verbal descriptors were independent from muscle activation. These results do not fit the OM-QM-IM continuum and suggest qualitative distinction for agentive awareness between IM and QM/OM.

AutoEncoder Filter Bank Common Spatial Patterns to decode Motor Imagery from EEG.

The present paper introduces a novel method to decode imagined movement from electroencephalographic (EEG) signals. Decoding the imagined movement with good accuracy is a challenging topic in motor imagery (MI) BCIs, poor accuracy may indeed hinder the application of such systems in practice. The present paper introduces an extension of the well-established Filter Bank Common Spatial Patterns (FBCSP) algorithm, named AutoEncoder(AE)-FBCSP, to benefit from the ability of AE to learn how to map data from the feature space onto a latent space where information relevant for classification are is embedded. The proposed method is based on a global (cross-subject) and subsequent transfer learning subject-specific (intra-subject) approach. A multi-way extension of AE-FBCSP is also introduced in this paper. The proposed methodology consists of recording high-density EEG (64 electrodes). Features are extracted by means of FBCSP and used to train a custom AE, in an unsupervised way, to project the features into a compressed latent space. Latent features then are used to train a supervised classifier (feed forward neural network) to decode the imagined movement. The algorithm was tested using a dataset of EEG extracted from a publicly available database of data collected from 109 subjects. AE-FBCSP was extensively tested in the 3-way (right-hand vs left-hand motor imagery vs resting) classification and also in the 2-way, 4-way and 5-way ones, both in cross- and intra-subject analysis. AE-FBCSP outperformed standard FBCSP in a statistically significant way (p 0.05) and outperformed also comparable methods in the literature applied to the same dataset. AE-FBCSP achieved an average accuracy of 89.09% in the 3-way subject-specific classification. With AE-FBCSP, 71.43% of subjects achieved a very high accuracy ( 87.68%) whereas no subject achieved an accuracy 87.68% with FBCSP. One of the most interesting outcomes is that AE-FBCSP remarkably increased the number of subjects that responded with a very high accuracy, which is a fundamental requirement for BCI systems to be applied in practice.

Increased sensitivity of spatial filters by combining the magnetic and electrical components of the sensorimotor cortical beta rhythm

Modulation of human magnetic or electrical sensorimotor rhythms during motor imagery is widely used in fundamental and applied neurophysiological research. To date, there is evidence of a better sensitivity of magnetic field sensors to beta-rhythm modulation, however, the potential synergistic effect of combining the two modalities has not yet been investigated. In this study, simultaneous registration of electroencephalogram (EEG) and magnetoencephalogram (MEG) was carried out in eight healthy volunteers during voluntary and imaginary movements, as well as during electrical stimulation of the median nerve. In all subjects, it was possible to identify desynchronization (suppression) of mu and beta rhythms during the performance of sensorimotor tasks, as well as beta synchronization after the end of movement or stimulation. Using the common projections of the covariance matrices of signals of the electric, magnetic, and combined (“MEEG”) modalities, the most sensitive individual spatial filters were calculated separately for each type of reaction. Relative to the pre-stimulus control, the changes in the amplitude of the sensorimotor rhythm components turned out to be the largest in the combined MEEG modality. At the same time, for mu-desynchronization, MEEG turned out to be significantly better than MEG, and for beta-desynchronization, MEEG turned out to be significantly better than both MEG and EEG. For beta synchronization, a shift in the position of sources in the fronto-medial direction was shown, and there were no significant differences in amplitude between modalities. It was also shown that for beta desynchronization, most subjects identified MEG sources with identical EEG projections or without pronounced EEG projections, which indicates the presence of several small tangentially located cortical dipoles involved in beta rhythm desynchronization. The results obtained indicate that in studies of modulation of sensorimotor rhythm components, in particular beta desynchronization, the combination of MEG and EEG leads to greater sensitivity. The multifocal nature of the magnetic beta rhythm and its varying severity in EEG sources indicate the presence of independent regulatory circuits of cortical-thalamic or intracortical origin.

Electrical activity of the brain during the imagination of purposeful hand movements in persons playing stringed and keyboard musical instruments

The imagination of purposeful movements and their implementation activates the same areas of the cerebral cortex, which is actively used in the creation of the brain computer interface and its implementation in the field of healthcare in the form of neuro-controlled exoskeletal limbs and robots that perform complex medical manipulations using the human brain. Playing musical instruments contributes to the development of upper limb motor skills and the development of interneuronal connections of the cerebral cortex. The aim of the work was to study brain activity when imagining purposeful movement in persons playing musical instruments. Brain activity was measured using electroencephalography. Interhemispheric asymmetry was determined using a complex method to determine the leading hemisphere. Because of the conducted research, it was found that the skills of playing musical instruments, even in the absence of sensory afferentation from the periphery during the imaginary initiation of movement, contribute to a stronger combined activation of the sensory and motor areas of the cortex.