Human movement-related potentials vs desynchronization of EEG alpha rhythm: a high-resolution EEG study

Connectivity of neural signals to the primary motor area during preparatory periods for movement following external and internal cues

PURPOSE

We investigated the connectivity of neural signals from movement-related cortical areas to the primary motor area (M1) in the hemisphere contralateral to the movement side during the period of movement-related magnetic fields before movement.

MATERIALS AND METHODS

Participants were 13 healthy adults, and nerual signals were recorded using magnetoencephalography. Spontaneous extension of the right wrist was performed at the participant's own pace and following a visual cue in internal (IC) and external (EC) cue tasks. The connectivity of neural signals to M1 from each movement-related motor area was assessed by Granger causality analysis (GCA). The GCA was performed on the neural activity elicited in a frequency band between 7.8 and 46.9 Hz during the pre-movement periods, which occurred durng the readiness field (RF) and the negative slope prime (NSp). F-values, as connectivity values obtained by GCA, were compared between the EC and IC cue tasks.

RESULTS

For NSp periods, the connectivity of neural signals from the left superior frontal area (SF-L) to M1 was dominant in the IC task, whereas that from the left superior parietal area (SP-L) to M1 was dominant in the EC task. The F value in the GCA from SP-L to M1 was greater in the EC task during RF than in the IC task during equivalent periods.

CONSLUSIONS

In the present study, there were differences in the connectivity of neural signals to M1 between IC and EC tasks. The present results suggested that the pattern of pre-movement neural activity that resulted in a movement was not uniform but differed between movement tasks just before the movement.

Identification and analysis of reference-independent movement event-related desynchronization

Characterization of the electroencephalography (EEG) signals related to motor activity, such as alpha- and beta-band motor event-related desynchronizations (ERDs), is essential for Brain Computer Interface (BCI) development. Determining the best electrode combination to detect the ERD is crucial for the success of the BCI. Considering that the EEG signals are bipolar, this involves the choice of the main and reference electrodes. So far, no strategy to guarantee signals free of the activity from the reference electrode has achieved consensus among the scientific community. Therefore, mapping the ERD in terms of the spatial distribution of the main and reference electrodes can provide additional perspectives for the BCI field. The goal of this work is to identify subject-specific channels where ERD is temporally coupled to the initiation of an upper-limb motor task. We defined a criterion to determine the presence of the ERD linked to the movement onset and searched, separately for each subject, for the single channel with the most prominent ERD. The search was conducted over all available channels composed by a pair of electrodes, and the selected signals were analyzed according to their temporal and spatial characteristics. We found that alpha- and beta-band ERD temporarily linked to movement onset can be detected in atypical channels (pairs of electrodes) across the scalp. The selected channels were different across subjects. Four ERD temporal patterns were observed in terms of the initiation instant of the ERD. These patterns revealed that the M1 cortex seems to be related to later ERDs. Moreover, they were also associated to different cortical processes related to the motor task. To the best of our knowledge, this is the first time these findings are reported. Aiming at BCI development, further experiments with more subjects and with motor-imagery tasks are desirable for more robustness and applicability of these findings.

Improved motor imagery skills after repetitive passive somatosensory stimulation: a parallel-group, pre-registered study

Introduction

Motor-imagery-based Brain-Machine Interface (MI-BMI) has been established as an effective treatment for post-stroke hemiplegia. However, the need for long-term intervention can represent a significant burden on patients. Here, we demonstrate that motor imagery (MI) instructions for BMI training, when supplemented with somatosensory stimulation in addition to conventional verbal instructions, can help enhance MI capabilities of healthy participants.

Methods

Sixteen participants performed MI during scalp EEG signal acquisition before and after somatosensory stimulation to assess MI-induced cortical excitability, as measured using the event-related desynchronization (ERD) of the sensorimotor rhythm (SMR). The non-dominant left hand was subjected to neuromuscular electrical stimulation above the sensory threshold but below the motor threshold (St-NMES), along with passive movement stimulation using an exoskeleton. Participants were randomly divided into an intervention group, which received somatosensory stimulation, and a control group, which remained at rest without stimulation.

Results

The intervention group exhibited a significant increase in SMR-ERD compared to the control group, indicating that somatosensory stimulation contributed to improving MI ability.

Discussion

This study demonstrates that somatosensory stimulation, combining electrical and mechanical stimuli, can improve MI capability and enhance the excitability of the sensorimotor cortex in healthy individuals.

Body Surface Potential Mapping: A Perspective on High-Density Cutaneous Electrophysiology

The electrophysiological signals recorded by cutaneous electrodes, known as body surface potentials (BSPs), are widely employed biomarkers in medical diagnosis. Despite their widespread application and success in detecting various conditions, the poor spatial resolution of traditional BSP measurements poses a limit to their diagnostic potential. Advancements in the field of bioelectronics have facilitated the creation of compact, high-quality, high-density recording arrays for cutaneous electrophysiology, allowing detailed spatial information acquisition as BSP maps (BSPMs). Currently, the design of electrode arrays for BSP mapping lacks a standardized framework, leading to customizations for each clinical study, limiting comparability, reproducibility, and transferability. This perspective proposes preliminary design guidelines, drawn from existing literature, rooted solely in the physical properties of electrophysiological signals and mathematical principles of signal processing. These guidelines aim to simplify and generalize the optimization process for electrode array design, fostering more effective and applicable clinical research. Moreover, the increased spatial information obtained from BSPMs introduces interpretation challenges. To mitigate this, two strategies are outlined: observational transformations that reconstruct signal sources for intuitive comprehension, and machine learning-driven diagnostics. BSP mapping offers significant advantages in cutaneous electrophysiology with respect to classic electrophysiological recordings and is expected to expand into broader clinical domains in the future.

Brain and muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals

Objective.Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols.Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level.Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand (AH). Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their AH. Interestingly, brain features performed better in this latter condition with respect to healthy subjects.Significance.Our results provide hints to improve the design of Brain-Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients' characteristics and to the intended rehabilitative target.

From preparation to post-processing: Insights into evoked and induced cortical activity during pre-cued motor reactions in children and adolescents

INTRODUCTION

The motor system undergoes significant development throughout childhood and adolescence. The contingent negative variation (CNV), a brain response reflecting preparation for upcoming actions, offers valuable insights into these changes. However, previous CNV studies of motor preparation have primarily focused on adults, leaving a gap in our understanding of how cortical activity related to motor planning and execution matures in children and adolescents.

METHODS

The study addresses this gap by investigating the maturation of motor preparation, pre-activation, and post-processing in 46 healthy, right-handed children and adolescents aged 5-16 years. To overcome the resolution limitations of previous studies, we combined 64-electrode high-density Electroencephalography (EEG) and advanced analysis techniques, such as event-related potentials (ERPs), mu-rhythm desynchronization as well as source localization approaches. The combined analyses provided an in-depth understanding of cortical activity during motor control.

RESULTS

Our data showed that children exhibited prolonged reaction times, increased errors, and a distinct pattern of cortical activation compared to adolescents. The findings suggest that the supplementary motor area (SMA) plays a progressively stronger role in motor planning and response evaluation as children age. Additionally, we observe a decrease in sensory processing and post-movement activity with development, potentially reflecting increased efficiency. Interestingly, adolescent subjects, unlike young adults in previous studies, did not yet show contralateral activation of motor areas during the motor preparation phase (late CNV).

CONCLUSION

The progressive increase in SMA activation and distinct cortical activation patterns in younger participants suggest immature motor areas. These immature regions might be a primary cause underlying the age-related increase in motor action control efficiency. Additionally, the study demonstrates a prolonged maturation of cortical motor areas, extending well into early adulthood, challenging the assumption that motor control is fully developed by late adolescence. This research, extending fundamental knowledge of motor control development, offers valuable insights that lay the foundation for understanding and treating motor control difficulties.

Selective cortical adaptations associated with neural efficiency in visuospatial tasks - the comparison of electroencephalographic profiles of expert and novice artists

Visuospatial cognition encapsulates an individual's ability to efficiently navigate and make sense of the multimodal cues from their surroundings, and therefore has been linked to expert performance across multiple domains, including sports, performing arts, and highly skilled tasks, such as drawing (Morrone and Minini, 2023). As neural efficiency posits a task-specific functional reorganization facilitated by long-term training, the present study employs a visuospatial construction task as a means of investigating the neurophysiological adaptations associated with expert visuospatial cognitive performance. Electroencephalogram (EEG) data acquisitions were used to evaluate the event-related changes (ER%) and statistical topographic maps of nine expert versus nine novice artists. The expert artists displayed overall higher global ER% compared to the novices within task-active intervals. Significant increases in relative ER% were found in the theta (t (10) = 3.528, p = 0.003, CI = [27.3,120.9]), lower-alpha (t (10) = 3.751, p = 0.002, CI = [28.2,110.5]), upper-alpha (t (10) = 3.829, p = 0.002, CI = [50.2,189.8]), and low beta (t (10) = 4.342, p < 0.001, CI = [37.0,114.9]) frequency bands, when comparing the experts to the novice participants. These results were particularly found in the frontal (t (14) = 2.014, p = 0.032, CI = [7.7,245.4]) and occipital (t (14) = 2.647, p = 0.010, CI = [45.0,429.7]) regions. Further, a significant decrease in alpha ER% from lower to upper activity (t (8) = 4.475, p = 0.001, CI = [21.0, 65.8]) was found across cortical regions in the novice group. Notably, greater deviation between lower and upper-alpha activity was found across scalp locations in the novice group, compared to the experts. Overall, the findings demonstrate potential local and global EEG-based indices of selective cortical adaptations within a task requiring a high degree of visuospatial cognition, although further work is needed to replicate these findings across other domains.

Prediction error in implicit adaptation during visually- and memory-guided reaching tasks

Human movements are adjusted by motor adaptation in order to maintain their accuracy. There are two systems in motor adaptation, referred to as explicit or implicit adaptation. It has been suggested that the implicit adaptation is based on the prediction error and has been used in a number of motor adaptation studies. This study aimed to examine the effect of visual memory on prediction error in implicit visuomotor adaptation by comparing visually- and memory-guided reaching tasks. The visually-guided task is thought to be implicit learning based on prediction error, whereas the memory-guided task requires more cognitive processes. We observed the adaptation to visuomotor rotation feedback that is gradually rotated. We found that the adaptation and retention rates were higher in the visually-guided task than in the memory-guided task. Furthermore, the delta-band power obtained by electroencephalography (EEG) in the visually-guided task was increased immediately following the visual feedback, which indicates that the prediction error was larger in the visually-guided task. Our results show that the visuomotor adaptation is enhanced in the visually-guided task because the prediction error, which contributes update of the internal model, was more reliable than in the memory-guided task. Therefore, we suggest that the processing of the prediction error is affected by the task-type, which in turn affects the rate of the visuomotor adaptation.

Altered Functional Networks of Alpha and Low-Beta Bands During Upper Limb Movement and Association with Motor Impairment in Chronic Stroke

Background: Impaired movement after stroke is closely associated with altered brain functions, and thus the investigation on neural substrates of patients with stroke can pave a way for not only understanding the underlying mechanisms of neuropathological traits, but also providing an innovative solution for stroke rehabilitation. The objective of this study was to precisely investigate altered brain functions in terms of power spectral and brain network analyses. Methods: Altered brain function was investigated by using electroencephalography (EEG) measured while 34 patients with chronic stroke performed movement tasks with the affected and unaffected hands. The relationships between functional brain network indices and Fugl-Meyer Assessment (FMA) scores were also investigated. Results: A stronger low-beta event-related desynchronization was found in the contralesional hemisphere for both affected and unaffected movement tasks compared with that of the ipsilesional hemisphere. More efficient whole-brain networks (increased strength and clustering coefficient, and prolonged path length) in the low-beta frequency band were revealed when moving the unaffected hand compared with when moving the affected hand. In addition, the brain network indices of the contralesional hemisphere indicated higher efficiency and cost-effectiveness than those of the ipsilesional hemisphere in both the alpha and low-beta frequency bands. Moreover, the alpha network indices (strength, clustering coefficient, path length, and small-worldness) were significantly correlated with the FMA scores. Conclusions: Efficient functional brain network indices are associated with better motor outcomes in patients with stroke and could be useful biomarkers to monitor stroke recovery during rehabilitation.

Associating Functional Neural Connectivity and Specific Aspects of Sensorimotor Control in Chronic Stroke

Hand sensorimotor deficits often result from stroke, limiting the ability to perform daily living activities. Sensorimotor deficits are heterogeneous among stroke survivors. Previous work suggests a cause of hand deficits is altered neural connectivity. However, the relationships between neural connectivity and specific aspects of sensorimotor control have seldom been explored. Understanding these relationships is important for developing personalized rehabilitation strategies to improve individual patients' specific sensorimotor deficits and, thus, rehabilitation outcomes. Here, we investigated the hypothesis that specific aspects of sensorimotor control will be associated with distinct neural connectivity in chronic stroke survivors. Twelve chronic stroke survivors performed a paretic hand grip-and-relax task while EEG was collected. Four aspects of hand sensorimotor grip control were extracted, including reaction time, relaxation time, force magnitude control, and force direction control. EEG source connectivity in the bilateral sensorimotor regions was calculated in α and β frequency bands during grip preparation and execution. Each of the four hand grip measures was significantly associated with a distinct connectivity measure. These results support further investigations into functional neural connectivity signatures that explain various aspects of sensorimotor control, to assist the development of personalized rehabilitation that targets the specific brain networks responsible for the individuals' distinct sensorimotor deficits.

Investigating the impact of visual perspective in a motor imagery-based brain-robot interaction: A pilot study with healthy participants

Introduction

Motor Imagery (MI)-based Brain Computer Interfaces (BCI) have raised gained attention for their use in rehabilitation therapies since they allow controlling an external device by using brain activity, in this way promoting brain plasticity mechanisms that could lead to motor recovery. Specifically, rehabilitation robotics can provide precision and consistency for movement exercises, while embodied robotics could provide sensory feedback that can help patients improve their motor skills and coordination. However, it is still not clear whether different types of visual feedback may affect the elicited brain response and hence the effectiveness of MI-BCI for rehabilitation.

Methods

In this paper, we compare two visual feedback strategies based on controlling the movement of robotic arms through a MI-BCI system: 1) first-person perspective, with visual information that the user receives when they view the robot arms from their own perspective; and 2) third-person perspective, whereby the subjects observe the robot from an external perspective. We studied 10 healthy subjects over three consecutive sessions. The electroencephalographic (EEG) signals were recorded and evaluated in terms of the power of the sensorimotor rhythms, as well as their lateralization, and spatial distribution.

Results

Our results show that both feedback perspectives can elicit motor-related brain responses, but without any significant differences between them. Moreover, the evoked responses remained consistent across all sessions, showing no significant differences between the first and the last session.

Discussion

Overall, these results suggest that the type of perspective may not influence the brain responses during a MI- BCI task based on a robotic feedback, although, due to the limited sample size, more evidence is required. Finally, this study resulted into the production of 180 labeled MI EEG datasets, publicly available for research purposes.

Collaborative neural processes predict successful cognitive-motor performance

Psychomotor efficiency is achieved by expert performers who exhibit refined attentional strategies and efficient motor program execution. Further understanding of the psychomotor efficiency hypothesis requires examination of the co-activation of key electroencephalographic (EEG) indices, including frontal theta (Fθ) power, left temporal alpha (T3α) power, the sensory-motor rhythm (SMR), and frontocentral alpha power (FCα). This study examined the relationship between these selected neural processes and the odds of successful cognitive-motor performance. EEG indices of successful and failed putts observed in twenty-seven skilled golfers were subjected to mixed-effects logistic regression analysis. The results revealed that concurrent elevations of Fθ and T3α were associated with increased odds of successful performance. The co-activation from motoric processes indicated by SMR and FCα also elevated the odds. Overall, the findings emphasize that refined attention allocation and effective motor program processing are essential cognitive features of superior cognitive-motor performance for skilled golfers.

MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements

Motor brain-computer interface (BCI) can intend to restore or compensate for central nervous system functionality. In the motor-BCI, motor execution (ME), which relies on patients' residual or intact movement functions, is a more intuitive and natural paradigm. Based on the ME paradigm, we can decode voluntary hand movement intentions from electroencephalography (EEG) signals. Numerous studies have investigated EEG-based unimanual movement decoding. Moreover, some studies have explored bimanual movement decoding since bimanual coordination is important in daily-life assistance and bilateral neurorehabilitation therapy. However, the multi-class classification of the unimanual and bimanual movements shows weak performance. To address this problem, in this work, we propose a neurophysiological signatures-driven deep learning model utilizing the movement-related cortical potentials (MRCPs) and event-related synchronization/ desynchronization (ERS/D) oscillations for the first time, inspired by the finding that brain signals encode motor-related information with both evoked potentials and oscillation components in ME. The proposed model consists of a feature representation module, an attention-based channel-weighting module, and a shallow convolutional neural network module. Results show that our proposed model has superior performance to the baseline methods. Six-class classification accuracies of unimanual and bimanual movements achieved 80.3%. Besides, each feature module of our model contributes to the performance. This work is the first to fuse the MRCPs and ERS/D oscillations of ME in deep learning to enhance the multi-class unimanual and bimanual movements' decoding performance. This work can facilitate the neural decoding of unimanual and bimanual movements for neurorehabilitation and assistance.

Electrophysiological evidence for the effectiveness of images versus text in warnings

Warning sign plays an important role in risk avoidance. Many studies have found that images are better warnings than text, while others have revealed flaws of image-only warning signs. To better understand the factors underlying the effectiveness of different types of warning signs (image only, text only, or image and text), this study adopted event-related potential technology to explore the differences at the neurocognitive level using the oddball paradigm and the Go/No-go paradigm. Together, the behavioral and electroencephalogram results showed that text-only warnings had the lowest effectiveness, but there was little difference between the image-only and image-and-text warnings. The differences in the effects of the three warning signs were mainly in the areas of attention and cognitive control, implying differences in the underlying cognitive processes. Therefore, in the design of warning signs, the effects of different design attributes on cognitive processing should be taken into account based on actual needs in order to improve the effectiveness of the signs.

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain-Computer Interface Based on Movement-Related Cortical Potentials

To enhance the treatment of motor function impairment, patients' brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain-computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.

Mirror visual feedback during unilateral finger movements is related to the desynchronization of cortical electroencephalographic somatomotor alpha rhythms

Using a mirror adequately oriented, the motion of just one hand induces the illusion of the movement with the other hand. Here, we tested the hypothesis that such a mirror phenomenon may be underpinned by an electroencephalographic (EEG) event-related desynchronization/synchronization (ERD/ERS) of central alpha rhythms (around 10 Hz) as a neurophysiological measure of the interactions among cerebral cortex, basal ganglia, and thalamus during movement preparation and execution. Eighteen healthy right-handed male participants performed standard auditory-triggered unilateral (right) or bilateral finger movements in the No Mirror (M-) conditions. In the Mirror (M+) condition, the unilateral right finger movements were performed in front of a mirror oriented to induce the illusion of simultaneous left finger movements. EEG activity was recorded from 64 scalp electrodes, and the artifact-free event-related EEG epochs were used to compute alpha ERD. In the M- conditions, a bilateral prominent central alpha ERD was observed during the bilateral movements, while left central alpha ERD and right alpha ERS were seen during unilateral right movements. In contrast, the M+ condition showed significant bilateral and widespread alpha ERD during the unilateral right movements. These results suggest that the above illusion of the left movements may be related to alpha ERD measures reflecting excitatory desynchronizing signals in right lateral premotor and primary somatomotor areas possibly in relation to basal ganglia-thalamic loops.

The influence of face mask on social spaces depends on the behavioral immune system

Interacting with objects and people requires specifying localized spaces where these interactions can take place. Previous studies suggest that the space for interacting with objects (i.e., the peripersonal space) contributes to defining the space for interacting with people (i.e., personal and interpersonal spaces). Furthermore, situational factors, such as wearing a face mask, have been shown to influence social spaces, but how they influence the relation between action and social spaces and are modulated by individual factors is still not well understood. In this context, the present study investigated the relationship between action peripersonal and social personal and interpersonal spaces in participants approached by male and female virtual characters wearing or not wearing a face mask. We also measured individual factors related to the behavioral immune system, namely willingness to take risks, perceived infectability and germ aversion. The results showed that compared to peripersonal space, personal space was smaller and interpersonal space was larger, but the three spaces were positively correlated. All spaces were altered by gender, being shorter when participants faced female characters. Personal and interpersonal spaces were reduced with virtual characters wearing a face mask, especially in participants highly aversive to risks and germs. Altogether, these findings suggest that the regulation of the social spaces depends on the representation of action peripersonal space, but with an extra margin that is modulated by situational and personal factors in relation to the behavioral immune system.

The neural response is heightened when watching a person approaching compared to walking away: Evidence for dynamic social neuroscience

The action observation network has been proposed to play a key role in predicting the action intentions (or goals) of others, thereby facilitating social interaction. Key information when interacting with others is whether someone (an agent) is moving towards or away from us, indicating whether we are likely to interact with the person. In addition, to determine the nature of a social interaction, we also need to take into consideration the distance of the agent relative to us as the observer. How this kind of information is processed within the brain is unknown, at least in part because prior studies have not involved live whole-body motion. Consequently, here we recorded mobile EEG in 18 healthy participants, assessing the neural response to the modulation of direction (walking towards or away) and distance (near vs. far distance) during the observation of an agent walking. We evaluated whether cortical alpha and beta oscillations were modulated differently by direction and distance during action observation. We found that alpha was only modulated by distance, with a stronger decrease of power when the agent was further away from the observer, regardless of direction. Critically, by contrast, beta was found to be modulated by both distance and direction, with a stronger decrease of power when the agent was near and facing the participant (walking towards) compared to when they were near but viewed from the back (walking away). Analysis revealed differences in both the timing and distribution of alpha and beta oscillations. We argue that these data suggest a full understanding of action observation requires a new dynamic neuroscience, investigating actual interactions between real people, in real world environments.

Prediction of balance function for stroke based on EEG and fNIRS features during ankle dorsiflexion

Balance rehabilitation is exceedingly crucial during stroke rehabilitation and is highly related to the stroke patients’ secondary injuries (caused by falling). Stroke patients focus on walking ability rehabilitation during the early stage. Ankle dorsiflexion can activate the brain areas of stroke patients, similar to walking. The combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was a new method, providing more beneficial information. We extracted the event-related desynchronization (ERD), oxygenated hemoglobin (HBO), and Phase Synchronization Index (PSI) features during ankle dorsiflexion from EEG and fNIRS. Moreover, we established a linear regression model to predict Berg Balance Scale (BBS) values and used an eightfold cross validation to test the model. The results showed that ERD, HBO, PSI, and age were critical biomarkers in predicting BBS. ERD and HBO during ankle dorsiflexion and age were promising biomarkers for stroke motor recovery.

Closed-loop motor imagery EEG simulation for brain-computer interfaces

In a brain-computer interface (BCI) system, the testing of decoding algorithms, tasks, and their parameters is critical for optimizing performance. However, conducting human experiments can be costly and time-consuming, especially when investigating broad sets of parameters. Attempts to utilize previously collected data in offline analysis lack a co-adaptive feedback loop between the system and the user present online, limiting the applicability of the conclusions obtained to real-world uses of BCI. As such, a number of studies have attempted to address this cost-wise middle ground between offline and live experimentation with real-time neural activity simulators. We present one such system which generates motor imagery electroencephalography (EEG) via forward modeling and novel motor intention encoding models for conducting sensorimotor rhythm (SMR)-based continuous cursor control experiments in a closed-loop setting. We use the proposed simulator with 10 healthy human subjects to test the effect of three decoder and task parameters across 10 different values. Our simulated approach produces similar statistical conclusions to those produced during parallel, paired, online experimentation, but in 55% of the time. Notably, both online and simulated experimentation expressed a positive effect of cursor velocity limit on performance regardless of subject average performance, supporting the idea of relaxing constraints on cursor gain in online continuous cursor control. We demonstrate the merits of our closed-loop motor imagery EEG simulation, and provide an open-source framework to the community for closed-loop SMR-based BCI studies in the future. All code including the simulator have been made available on GitHub.

Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions

Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-to-noise ratio of these signals, and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal preprocessing, feature extraction, and decision making. In this article, we define a new BCI framework, called enhanced fusion framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. First, we include an additional preprocessing step of the signal: a differentiation of the EEG signal that makes it time invariant. Second, we add an additional frequency band as a feature for the system: the sensorimotor rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals, and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing MI-based brain-computer interface experiments. On this dataset, the new system achieved 88.80% accuracy. We also propose an optimized version of our system that is able to obtain up to 90.76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.

Exploring EEG spectral and temporal dynamics underlying a hand grasp movement

For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex's functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3rd order polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.

Response-related sensorimotor rhythms under scopolamine and MK-801 exposures in the touchscreen visual discrimination test in rats

The human mu rhythm has been suggested to represent an important function in information processing. Rodent homologue rhythms have been assumed though no study has investigated them from the cognitive aspect yet. As voluntary goal-directed movements induce the desynchronization of mu rhythm, we aimed at exploring whether the response-related brain activity during the touchscreen visual discrimination (VD) task is suitable to detect sensorimotor rhythms and their change under cognitive impairment. Different doses of scopolamine or MK-801 were injected subcutaneously to rats, and epidural electroencephalogram (EEG) was recorded during task performance. Arciform ~ 10 Hz oscillations appeared during visual processing, then two characteristic alpha/beta desynchronization-resynchronization patterns emerged mainly above the sensorimotor areas, serving presumably different motor functions. Beyond causing cognitive impairment, both drugs supressed the touch-related upper alpha (10–15 Hz) reactivity for desynchronization. Reaction time predominantly correlated positively with movement-related alpha and beta power both in normal and impaired conditions. These results support the existence of a mu homologue rodent rhythm whose upper alpha component appeared to be modulated by cholinergic and glutamatergic mechanisms and its power change might indicate a potential EEG correlate of processing speed. The VD task can be utilized for the investigation of sensorimotor rhythms in rats.

Constructing Time-varying Directed EEG network by Multivariate Nonparametric Dynamical Granger Causality

Time-varying directed electroencephalography (EEG) network is the potential tool for studying the dynamical causality among brain areas at a millisecond level; which conduces to understanding how our brain effectively adapts to information processing, giving inspiration to causality- and brain-inspired machine learning. Currently, its construction still mainly relies on the parametric approach such as multivariate adaptive autoregressive (MVAAR), represented by the most widely used adaptive directed transfer function (ADTF). Restricted by the model assumption, the corresponding performance largely depends on the MVAAR modeling which usually encounters difficulty in fitting complex spectral features. In this study, we proposed to construct EEG directed network with multivariate nonparametric dynamical Granger causality (mndGC) method that infers the causality of a network, instead, in a data-driven way directly and therefore avoids the trap in the model-dependent parametric approach. Comparisons between mndGC and ADTF were conducted both with simulation and real data application. Simulation study demonstrated the superiority of mndGC both in noise resistance and capturing the instantaneous directed network changes. When applying to the real motor imagery (MI) data set, distinguishable network characters between left- and right-hand MI during different MI stages were better revealed by mndGC. Our study extends the nonparametric causality exploration and provides practical suggestions for the time-varying directed EEG network analysis.

EEG-Based Spectral Analysis Showing Brainwave Changes Related to Modulating Progressive Fatigue During a Prolonged Intermittent Motor Task

Repeatedly performing a submaximal motor task for a prolonged period of time leads to muscle fatigue comprising a central and peripheral component, which demands a gradually increasing effort. However, the brain contribution to the enhancement of effort to cope with progressing fatigue lacks a complete understanding. The intermittent motor tasks (IMTs) closely resemble many activities of daily living (ADL), thus remaining physiologically relevant to study fatigue. The scope of this study is therefore to investigate the EEG-based brain activation patterns in healthy subjects performing IMT until self-perceived exhaustion. Fourteen participants (median age 51.5 years; age range 26−72 years; 6 males) repeated elbow flexion contractions at 40% maximum voluntary contraction by following visual cues displayed on an oscilloscope screen until subjective exhaustion. Each contraction lasted ≈5 s with a 2-s rest between trials. The force, EEG, and surface EMG (from elbow joint muscles) data were simultaneously collected. After preprocessing, we selected a subset of trials at the beginning, middle, and end of the study session representing brain activities germane to mild, moderate, and severe fatigue conditions, respectively, to compare and contrast the changes in the EEG time-frequency (TF) characteristics across the conditions. The outcome of channel- and source-level TF analyses reveals that the theta, alpha, and beta power spectral densities vary in proportion to fatigue levels in cortical motor areas. We observed a statistically significant change in the band-specific spectral power in relation to the graded fatigue from both the steady- and post-contraction EEG data. The findings would enhance our understanding on the etiology and physiology of voluntary motor-action-related fatigue and provide pointers to counteract the perception of muscle weakness and lack of motor endurance associated with ADL. The study outcome would help rationalize why certain patients experience exacerbated fatigue while carrying out mundane tasks, evaluate how clinical conditions such as neurological disorders and cancer treatment alter neural mechanisms underlying fatigue in future studies, and develop therapeutic strategies for restoring the patients' ability to participate in ADL by mitigating the central and muscle fatigue.

Left hemisphere dominance for bilateral kinematic encoding in the human brain

Neurophysiological studies in humans and nonhuman primates have revealed movement representations in both the contralateral and ipsilateral hemispheres. Inspired by clinical observations, we ask if this bilateral representation differs for the left and right hemispheres. Electrocorticography was recorded in human participants during an instructed-delay reaching task, with movements produced with either the contralateral or ipsilateral arm. Using a cross-validated kinematic encoding model, we found stronger bilateral encoding in the left hemisphere, an effect that was present during preparation and was amplified during execution. Consistent with this asymmetry, we also observed better across-arm generalization in the left hemisphere, indicating similar neural representations for right and left arm movements. Notably, these left hemisphere electrodes were centered over premotor and parietal regions. The more extensive bilateral encoding in the left hemisphere adds a new perspective to the pervasive neuropsychological finding that the left hemisphere plays a dominant role in praxis.

Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain-Computer Interface

Hybrid Brain-Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of "more normal" brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation.

EEG-Based Evidence of Mirror Neuron Activity from App-Mediated Stroke Patient Observation

Background and Objectives: The mirror neuron system in the sensorimotor region of the cerebral cortex is equally activated during both action observation and execution. Action observation training mimics the functioning of the mirror neuron system, requiring patients to watch and imitate the actions necessary to perform activities of daily living. StrokeCare is a user-friendly application based on the principles of action observation training, designed to assist people recovering from stroke. Therefore, when observing the daily life behavior provided in the StrokeCare app, whether the MNS is activated and mu inhibition appears. Materials and Methods: We performed electroencephalography (EEG) on 24 patients with chronic stroke (infarction: 11, hemorrhage: 13) during tasks closely related to daily activities, such as dressing, undressing, and walking. The StrokeCare app provided action videos for patients to watch. Landscape imagery observation facilitated comparison among tasks. We analyzed the mu rhythm from the C3, CZ, and C4 regions and calculated the mean log ratios for comparison of mu suppression values. Results: The EEG mu power log ratios were significantly suppressed during action observation in dressing, undressing, walking, and landscape conditions, in decreasing order. However, there were no significant activity differences in the C3, C4 and CZ regions. The dressing task showed maximum suppression after a color spectrum was used to map the relative power values of the mu rhythm for each task. Conclusions: These findings reveal that the human mirror neuron system was more strongly activated during observation of actions closely related to daily life activities than landscape images.

Body-Space Interactions: Same Spatial Encoding but Different Influence of Valence for Reaching and Defensive Purposes

The space around our body, the so-called peripersonal space, is where interactions with nearby objects may occur. "Defensive space" and "Reaching space", respectively, refer to two opposite poles of interaction between our body and the external environment: protecting the body and performing a goal-directed action. Here, we hypothesized that mechanisms underlying these two action spaces are differentially modulated by the valence of visual stimuli, as stimuli with negative valence are more likely to activate protective actions whereas stimuli with positive valence may activate approaching actions. To test whether such distinction in cognitive/evaluative processing exists between Reaching and Defensive spaces, we measured behavioral responses as well as neural activations over sensorimotor cortex using EEG while participants performed several tasks designed to tap into mechanisms underlying either Defensive (e.g., respond to touch) or Reaching space (e.g., estimate whether object is within reaching distance). During each task, pictures of objects with either positive or negative valence were presented at different distances from the participants' body. We found that Defensive space was smaller for positively compared with negatively valenced visual stimuli. Furthermore, sensorimotor cortex activation (reflected in modulation of beta power) during tactile processing was enhanced when coupled with negatively rather than positively valenced visual stimuli regarding Defensive space. On the contrary, both the EEG and behavioral measures capturing the mechanisms underlying Reaching space did not reveal any modulation by valence. Thus, although valence encoding had differential effects on Reaching and Defensive spaces, the distance of the visual stimulus modulated behavioral measures as well as activity over sensorimotor cortex (reflected in modulations of mu power) in a similar way for both types of spaces. Our results are compatible with the idea that Reaching and Defensive spaces involve the same distance-dependent neural representations of sensory input, whereas task goals and stimulus valence (i.e., contextual information) are implemented at a later processing stage and exert an influence on motor output rather than sensory/space encoding.

INTELLIGENCE LEVEL MIGHT BE PREDICTED BY THE CHARACTERISTICS OF EEG SIGNALS AT SPECIFIC FREQUENCIES AND BRAIN REGIONS

The total energy the brain consumed and the intensities of information flows across different brain regions in an intellectual activity may help to explain an individual’s intelligence level. To verify this assumption, 43 students aged 18–25 were recruited as the research subjects. Their intelligence quotients (IQ) were scored by using Wechsler Adult Intelligence Scale (WAIS), while their electroencephalogram (EEG) signals were recorded simultaneously by using Neuroscan system. The total energy and distribution patterns of EEG signals were acquired in Curry 8.0. The intensities of information flow across different brain regions were measured by Phase Slope Index (PSI). 20 channels and 190 channel combinations were selected for data analysis. The results show that the IQ score negatively correlates to the EEG energy and positively correlates to the intensities of information flows at specific frequency bands in specific channel pairs, especially in some long distance (18–24[Formula: see text]cm) channel pairs.

The Early Start Denver Model Intervention and Mu Rhythm Attenuation in Autism Spectrum Disorders

We examined the relationship between the Early start Denver model (ESDM) intervention and mu rhythm attenuation, an EEG paradigm reflecting neural processes associated with action perception and social information processing. Children were assigned to either receive comprehensive ESDM intervention for two years, or were encouraged to pursue resources in the community. Two years after intervention, EEG was collected during the execution and observation of grasping actions performed by familiar and unfamiliar agents. The ESDM group showed significantly greater attenuation when viewing a parent or caregiver executing a grasping action, compared with an unfamiliar individual executing the same action. Our findings suggest that the ESDM may have a unique impact on neural circuitry underlying social cognition and familiarity.

EEG measures for clinical research in major vascular cognitive impairment: recommendations by an expert panel

Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.

The Interrelation Between Peripersonal Action Space and Interpersonal Social Space: Psychophysiological Evidence and Clinical Implications

The peripersonal space is an adaptive and flexible interface between the body and the environment that fulfills a dual-motor function: preparing the body for voluntary object-oriented actions to interact with incentive stimuli and preparing the body for defensive responses when facing potentially harmful stimuli. In this position article, we provide arguments for the sensorimotor rooting of the peripersonal space representation and highlight the variables that contribute to its flexible and adaptive characteristics. We also demonstrate that peripersonal space represents a mediation zone between the body and the environment contributing to not only the control of goal-directed actions but also the organization of social life. The whole of the data presented and discussed led us to the proposal of a new theoretical framework linking the peripersonal action space and the interpersonal social space and we highlight how this theoretical framework can account for social behaviors in populations with socio-emotional deficits.

Ongoing Electroencephalographic Rhythms Related to Exploratory Movements in Transgenic TASTPM Mice

BACKGROUND

The European PharmaCog study (http://www.pharmacog.org) has reported a reduction in delta (1-6 Hz) electroencephalographic (EEG) power (density) during cage exploration (active condition) compared with quiet wakefulness (passive condition) in PDAPP mice (hAPP Indiana V717F mutation) modeling Alzheimer's disease (AD) amyloidosis and cognitive deficits.

OBJECTIVE

Here, we tested the reproducibility of that evidence in TASTPM mice (double mutation in APP KM670/671NL and PSEN1 M146V), which develop brain amyloidosis and cognitive deficits over aging. The reliability of that evidence was examined in four research centers of the PharmaCog study.

METHODS

Ongoing EEG rhythms were recorded from a frontoparietal bipolar channel in 29 TASTPM and 58 matched "wild type" C57 mice (range of age: 12-24 months). Normalized EEG power was calculated. Frequency and amplitude of individual delta and theta frequency (IDF and ITF) peaks were considered during the passive and active conditions.

RESULTS

Compared with the "wild type" group, the TASTPM group showed a significantly lower reduction in IDF power during the active over the passive condition (p < 0.05). This effect was observed in 3 out of 4 EEG recording units.

CONCLUSION

TASTPM mice were characterized by "poor reactivity" of delta EEG rhythms during the cage exploration in line with previous evidence in PDAPP mice. The reliability of that result across the centers was moderate, thus unveiling pros and cons of multicenter preclinical EEG trials in TASTPM mice useful for planning future studies.

Motor imagery EEG classification based on ensemble support vector learning

BACKGROUND AND OBJECTIVE

Brain-computer interfaces build a communication pathway from the human brain to a computer. Motor imagery-based electroencephalogram (EEG) classification is a widely applied paradigm in brain-computer interfaces. The common spatial pattern, based on the event-related desynchronization (ERD)/event-related synchronization (ERS) phenomenon, is one of the most popular algorithms for motor imagery-based EEG classification. Moreover, the spatiotemporal discrepancy feature based on the event-related potential phenomenon has been demonstrated to provide complementary information to ERD/ERS-based features. In this paper, aiming to improve the performance of motor imagery-based EEG classification in a few-channel situation, an ensemble support vector learning (ESVL)-based approach is proposed to combine the advantages of the ERD/ERS-based features and the event-related potential-based features in motor imagery-based EEG classification.

METHODS

ESVL is an ensemble learning algorithm based on support vector machine classifier. Specifically, the decision boundary with the largest interclass margin is obtained using the support vector machine algorithm, and the distances between sample points and the decision boundary are mapped to posterior probabilities. The probabilities obtained from different support vector machine classifiers are combined to make prediction. Thus, ESVL leverages the advantages of multiple trained support vector machine classifiers and makes a better prediction based on the posterior probabilities. The class discrepancy-guided sub-band-based common spatial pattern and the spatiotemporal discrepancy feature are applied to extract discriminative features, and then, the extracted features are used to train the ESVL classifier and make predictions.

RESULTS

The BCI Competition IV datasets 2a and 2b are employed to evaluate the performance of the proposed ESVL algorithm. Experimental comparisons with the state-of-the-art methods are performed, and the proposed ESVL-based approach achieves an average max kappa value of 0.60 and 0.71 on BCI Competition IV datasets 2a and 2b respectively. The results show that the proposed ESVL-based approach improves the performance of motor imagery-based brain-computer interfaces.

CONCLUSION

The proposed ESVL classifier could use the posterior probabilities to realize ensemble learning and the ESVL-based motor imagery classification approach takes advantage of the merits of ERD/ERS based feature and event-related potential based feature to improve the experimental performance.

Human Mirror Neuron System Based Alarms in the Cockpit: A Neuroergonomic Evaluation

Controlled Flight Into Terrain (CFIT) events still remain among the deadliest accidents in aviation. When facing the possible occurrence of such an event, pilots have to immediately react to the ground proximity alarm ("Pull Up" alarm) in order to avoid the impending collision. However, the pilots' reaction to this alarm is not always optimal. This may be at least partly due to the low visual saliency of the current alarm and the deleterious effects of stress that alleviate the pilot's reactions. In the present study, two experiments (in a laboratory and in a flight simulator) were conducted to (1) investigate whether hand gesture videos (a hand pulling back the sidestick) can trigger brainwave frequencies related to the mirror neuron system; (2) determine whether enhancing the visual characteristics of the "Pull Up" alarm could improve pilots' response times. Electrophysiological results suggest that hand gesture videos attracted more participants' attention (greater alpha desynchronization in the parieto-occipital area) and possibly triggered greater activity of the mirror neuron system (greater mu and beta desynchronizations at central electrodes). Results obtained in the flight simulator revealed that enhancing the visual characteristics of the original "Pull Up" alarm improved the pilots' reaction times. However, no significant difference in reaction times between an enlarged "Pull Up" inscription and the hand gesture video was found. Further work is needed to determine whether mirror neuron system based alarms could bring benefits for flight safety, in particular, these alarms should be assessed during a high stress context.

Effects of EEG examination and ABA-therapy on resting-state EEG in children with low-functioning autism

Objective

We aimed to study the effects of EEG examination and ABA-therapy on resting-state EEG in children with low-functioning autism and tactile defensiveness.

Methods

We have performed this study with three cohorts of preschoolers: children with autistic spectrum disorder (ASD) who needed applied behavior analysis (ABA) therapy due to their tactile defensiveness; children with ASD who didn't need ABA therapy; and the control group of healthy children. Number of microstates was determined in the initial and final parts of the resting-state EEGs.

Results and conclusions

Children with higher tactile defensiveness for the most part had specific EEG microstates associated with unpleasant emotions and senses. The EEG microstates of children with ASD who did not need ABA therapy, had more similarities with the EEG microstates of typically developing children except for temporary changes. Meanwhile, the children with tactile defensiveness demonstrated typical patterns of EEG microstates from start to finish of the procedure.

Does Motor Cortex Engagement During Movement Preparation Differentially Inhibit Nociceptive Processing in Patients with Chronic Whiplash Associated Disorders, Chronic Fatigue Syndrome and Healthy Controls? An Experimental Study

BACKGROUND

Patients with chronic fatigue syndrome (CFS) and chronic whiplash associated disorders (cWAD) present a reduced ability to activate central descending nociceptive inhibition after exercise, compared to measurements before exercise. It was hypothesised that a dysfunctional motor-induced inhibition of nociception partly explains this dysfunctional exercise-induced hypoalgesia. This study investigates if engagement of the motor system during movement preparation inhibits nociception-evoked brain responses in these patients as compared to healthy controls (HC).

METHODS

The experiment used laser-evoked potentials (LEPs) during three conditions (no task, mental task, movement preparation) while recording brain activity with a 32-channel electroencephalogram in 21 patients with cWAD, 20 patients with CFS and 18 HC. Two-factor mixed design Analysis of variance were used to evaluate differences in LEP amplitudes and latencies.

RESULTS

No differences in N1, N2, N2P2, and P2 LEP amplitudes were found between the HC, CFS, and cWAD groups. After nociceptive stimulation, N1, N2 (only at hand location), N2P2, and P2 LEP amplitudes significantly decreased during movement preparation compared to no task (within group differences).

CONCLUSION

Movement preparation induces a similar attenuation of LEPs in patients with CFS, patients with cWAD and HC. These findings do not support reduced motor-induced nociceptive inhibition in these patients.

The Human Dynamic Clamp Reveals the Fronto-Parietal Network Linking Real-Time Social Coordination and Cognition

How does the brain allow us to interact with others? Social neuroscience has already provided some answers to these questions but has tended to treat high-level, cognitive interpretations of social behavior separately from the sensorimotor mechanisms upon which they rely. The goal here is to identify the underlying neural processes and mechanisms linking sensorimotor coordination and intention attribution. We combine the human dynamic clamp, a novel paradigm for studyingrealistic social behavior, with high-resolution electroencephalography. The collection of humanness and intention attribution reports, kinematics, and neural data affords an opportunity to relate brain activity to the ongoing social behavior. Behavioral results demonstrate that sensorimotor coordination influences the judgments of cooperativeness and humanness. Analysis of brain dynamics reveals two distinct networks related to the integration of visuo-motor information from self and other which overlap over the right parietal region. Furthermore, judgment of humanness and cooperation of others modulate the functional connectivity between this right parietal hub and the prefrontal cortex. These results reveal how distributed neural dynamics integrates information from "low-level" sensorimotor mechanisms and "high-level" social cognition to support the realistic social behaviors that play out in real time during interactive scenarios.

Development of a flickering action video based steady state visual evoked potential triggered brain computer interface-functional electrical stimulation for a rehabilitative action observation game

BACKGROUND:

This study focused on developing an upper limb rehabilitation program. In this regard, a steady state visual evoked potential (SSVEP) triggered brain computer interface (BCI)-functional electrical stimulation (FES) based action observation game featuring a flickering action video was designed.

OBJECTIVE:

In particular, the synergetic effect of the game was investigated by combining the action observation paradigm with BCI based FES.

METHODS:

The BCI-FES system was contrasted under two conditions: with flickering action video and flickering noise video. In this regard, 11 right-handed subjects aged between 22–27 years were recruited. The differences in brain activation in response to the two conditions were examined.

RESULTS:

The results indicate that T3 and P3 channels exhibited greater Mu suppression in 8–13 Hz for the action video than the noise video. Furthermore, T4, C4, and P4 channels indicated augmented high beta (21–30 Hz) for the action in contrast to the noise video. Finally, T4 indicated suppressed low beta (14–20 Hz) for the action video in contrast to the noise video.

CONCLUSION:

The flickering action video based BCI-FES system induced a more synergetic effect on cortical activation than the flickering noise based system.

Spatially and Temporally Distinct Encoding of Muscle and Kinematic Information in Rostral and Caudal Primary Motor Cortex

The organizing principle of human motor cortex does not follow an anatomical body map, but rather a distributed representational structure in which motor primitives are combined to produce motor outputs. Electrophysiological recordings in primates and human imaging data suggest that M1 encodes kinematic features of movements, such as joint position and velocity. However, M1 exhibits well-documented sensory responses to cutaneous and proprioceptive stimuli, raising questions regarding the origins of kinematic motor representations: are they relevant in top-down motor control, or are they an epiphenomenon of bottom-up sensory feedback during movement? Here we provide evidence for spatially and temporally distinct encoding of kinematic and muscle information in human M1 during the production of a wide variety of naturalistic hand movements. Using a powerful combination of high-field functional magnetic resonance imaging and magnetoencephalography, a spatial and temporal multivariate representational similarity analysis revealed encoding of kinematic information in more caudal regions of M1, over 200 ms before movement onset. In contrast, patterns of muscle activity were encoded in more rostral motor regions much later after movements began. We provide compelling evidence that top-down control of dexterous movement engages kinematic representations in caudal regions of M1 prior to movement production.

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky-Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

Spectral dissociation of lateralized brain rhythms

Using high resolution spectral methods to uncover neuromarkers of social, cognitive and behavioral function, we have found that hemi-lateralized pairs of oscillations such as left and right occipital alpha or left and right rolandic mu dissociate spectrally. That is, they show a shifted frequency distribution, with one member of the pair peaking at a slightly lower frequency than the other. Resorting to the analysis of EEG spatio-spectral patterns, we provide examples of dissociations in the 10Hz frequency band. Occasionally, hemi-lateralized pairs blend into medial aggregates, probably when functional interactions lead to strongly coherent dynamics through frequency-locking or metastability. Our observations support the hypothesis that homologous pairs of neuromarkers have characteristically distinct intrinsic frequencies and coordinate their oscillations into synchronous ensembles only transiently. This property could play a role in the balance of integration and segregation in the brain: spectral separation of the oscillations from homologous cortical areas allows them to function independently under certain circumstances, all the while preserving a potential for stronger interactions supported by structural and functional symmetries. Spectral dissociation (and its methodological corollary: spectral analysis with high frequency resolution) may be harnessed to better track the individual power of each member of a hemi-lateralized pair and their respective time-course, leading to enhanced internal validity and reproducibility of research on neural oscillations. Resulting insights may shed light on the functional interaction between homologous cortices in studies of attention (alpha), e.g. during perceptual and social interaction tasks, and in studies of somatomotor processing (mu), e.g. in bimanual coordination and neuroprosthetics.

Activation of the primary motor cortex using fully-implanted electrical sciatic nerve stimulation

Functional degradation of the motor cortex usually results from brain injury, stroke, limb amputation, aging or other diseases. Currently, there are no ideal means of treatment, other than medication and sports rehabilitation. The present study investigated whether electrical stimulation of the sciatic nerve can activate the motor-related area of the brain. The study is based on a self-developed fully implantable nerve electrical stimulator and a self-developed multi-channel electroencephalogram (EEG) electrode array. The sciatic nerves of Sprague-Dawley rats (sorted into old and young groups) were stimulated by the electrical stimulator under anesthesia, and the EEG signal was recorded simultaneously. The relationship between sciatic nerve stimulation and brain activity was analyzed. The results showed that when the sciatic nerve was stimulated by the implanted electrical stimulator, motor-related channels were activated, causing contraction of the left leg. It was found that at the frequency band of 8-16 Hz, the EEG signal in the right motor area was higher than at other frequency bands. This phenomenon was identical in both young and old rats. The results indicated that electrical stimulation of the sciatic nerve can activate the motor region of the rat brain, and provided evidence that stimulation of the sciatic nerve could be a method of preventing motor cortex degeneration.

Musical Role Asymmetries in Piano Duet Performance Influence Alpha-Band Neural Oscillation and Behavioral Synchronization

Recent work in interpersonal coordination has revealed that neural oscillations, occurring spontaneously in the human brain, are modulated during the sensory, motor, and cognitive processes involved in interpersonal interactions. In particular, alpha-band (8-12 Hz) activity, linked to attention in general, is related to coordination dynamics and empathy traits. Researchers have also identified an association between each individual's attentiveness to their co-actor and the relative similarity in the co-actors' roles, influencing their behavioral synchronization patterns. We employed music ensemble performance to evaluate patterns of behavioral and neural activity when roles between co-performers are systematically varied with complete counterbalancing. Specifically, we designed a piano duet task, with three types of co-actor dissimilarity, or asymmetry: (1) musical role (starting vs. joining), (2) musical task similarity (similar vs. dissimilar melodic parts), and (3) performer animacy (human-to-human vs. human-to-non-adaptive computer). We examined how the experience of these asymmetries in four initial musical phrases, alternatingly played by the co-performers, influenced the pianists' performance of a subsequent unison phrase. Electroencephalography was recorded simultaneously from both performers while playing keyboards. We evaluated note-onset timing and alpha modulation around the unison phrase. We also investigated whether each individual's self-reported empathy was related to behavioral and neural activity. Our findings revealed closer behavioral synchronization when pianists played with a human vs. computer partner, likely because the computer was non-adaptive. When performers played with a human partner, or a joining performer played with a computer partner, having a similar vs. dissimilar musical part did not have a significant effect on their alpha modulation immediately prior to unison. However, when starting performers played with a computer partner with a dissimilar vs. similar part there was significantly greater alpha synchronization. In other words, starting players attended less to the computer partner playing a similar accompaniment, operating in a solo-like mode. Moreover, this alpha difference based on melodic similarity was related to a difference in note-onset adaptivity, which was in turn correlated with performer trait empathy. Collectively our results extend previous findings by showing that musical ensemble performance gives rise to a socialized context whose lasting effects encompass attentiveness, perceptual-motor coordination, and empathy.

Observation of the point-light animation of a grasping hand activates sensorimotor cortex in nine-month-old infants

Measuring changes in sensorimotor alpha band activity in nine-month-old infants we sought to understand the involvement of the sensorimotor cortex during observation of the Point-Light (PL) animation of a grasping hand. Attenuation of alpha activity was found both when the PL display moved towards the to-be-grasped object and when the object was deleted from the video. Before the beginning of the movement of the PL stimuli, only in the presence of the object evoked attenuation of sensorimotor alpha activity was documented, possibly interpreted either as movement prediction or as graspable object perception. Our main findings demonstrate that, during observation of stimuli moving with biological kinematics, the infants' sensorimotor system is activated when the pictorial information is absent or highly reduced, and independently of the presence of the goal-directed object. The possible compensatory function of the sensorimotor system during observation of highly degraded moving stimuli is discussed.