Sensorimotor Rhythm Research Articles

The Effects of Motor Expertise on Sensorimotor Rhythm Desynchronization during Execution and Imagery of Sequential Movements

The purpose of the study was to investigate sensorimotor rhythm desynchronization during the performance of a motor execution and motor imagery task of different complexity, while varying motor musical expertise of subjects. We compared EEG patterns of professional pianists and non-pianists, who either executed or imagined finger tapping movements of different complexity. Results show that the power in alpha (8–12 Hz) and beta (13–30 Hz) rhythms decreases with the complexity of both performed and imagined movements. Motor expertise influenced alpha rhythm desynchronization in the motor execution task – in the group of pianists there were differences in alpha power decrease depending on the complexity of the performed movement. There was no such relationship among non-pianists. In the imagery task, there was a tendency toward an interaction of motor expertise and low and high alpha rhythm components. In the beta band, there was an interaction of frequency and area of the skull occurring in the movement execution condition – high and low beta rhythm components had different topography.

F190. Mindfulness based stress reduction’s influence on brain-computer interface control

Introduction Sensorimotor rhythm (SMR) based brain-computer interfaces (BCIs) can be used to control an external device through motor imagery. SMRs are oscillations that can be measured by electroencephalography (EEG) over the sensorimotor cortex. The ability to imagine movements and modulate SMRs is variable across individuals. Previous work has suggested that long-term mind-body awareness training (MBAT) offers merits in improving the performance of SMR based BCIs. However, whether such improved BCI performance results from a natural affinity for mental training or directly from MBAT is not clear. The aim of this study is to test the hypothesis that short-term MBAT can exert an influence over BCI control. Methods To assess the effects of MBAT on BCI control, 28 subjects attended an 8-week mindfulness based stress reduction (MBSR) class. BCI learning was compared between these MBAT subjects and 21 wait-listed controls, across several days of SMR based BCI training sessions. Subjects performed motor imagery of hand movements, while EEG rhythm at 12 Hz (3 Hz band, over C3/C4) was extracted and used to decode subjects’ intention to move a computer cursor. Subjects were instructed to imagine left (right) hand movement to move the cursor left (right), movement in both hands to move the cursor up, and a voluntary rest to move the cursor down. In separate blocks of trials, subjects attempted to steer the cursor to a target that required left/right (LR) movement only, up/down (UD) only, and combined 2D movement (2D). Percent valid correct (PVC) calculated as the number of hits divided by the total number of non-timeout trials was used to assess accuracy. Results Overall, MBAT subjects demonstrated improvements in accuracy across training sessions in the UD and 2D paradigms with increases in performance of 13% (p Conclusion MBAT subjects demonstrated mildly enhanced learning in our study, suggesting MBAT may be able to improve SMR based BCI training. Our past work showed an advantage for subjects with years of MBAT training; the present work extends this finding to 2 months of training. Work is ongoing to increase the sample size in the present study and examine whether cognitive, behavioral, or electrophysiological measures can explain the observed results. While works so far have focused on basic tasks such as computer cursor movement, it will be interesting to test whether MBAT can enhance learning of more complicated BCI tasks.

F160. Investigation of the effects of transcranial direct current stimulation and neurofeedback by continuous performance test and beck inventories

Transcranial direct current stimulation (tDCS) method is a noninvasive neuromodulation technique based on weak direct current stimulation through the scalp. Neurofeedback (NFB) is a learning strategy which may help to alter brain wave parameters of a person by giving electroencephalography (EEG) feedback via special developed computer programs. Based on the aforementioned evidence, the study was designed to examine primarily the possible fascinating and supportive effects of tDCS application in addition to NFB training on healthy volunteers. Secondly, we aimed to examine brain wave parameters of by giving EEG feedback alteration by Continuous Performance Test (CPT), Beck Inventories (BI) used in order to measure; the selective attention, response time and suppression. Sixteen healthy volunteers were divided into two groups consisting of eight subjects. One of the groups were trained by sensorimotor rhythm (SMR) NFB training; 2 days per week, 10 sessions of 30 min, other group received 10 min tDCS before each NFB training session. CPT, BI were used in order to measure the selective attention, response time and suppression. Obtained EEG band values and rates were evaluated offline. According to statistical analyses, average of depression scores of NFB + tDCS group (7.000 ± 4.07) was decreased compared to before (9,625 ± 5.60) NFB + tDCS practice, it was significantly decreased when compared with statistical analyze of t-test (t (7) = 2628, p < .05). After trainings, the results of CPT were compared with repeated measure ANOVA.Sessions were analyzed by comparing 1st, 2nd, 5th and 10th practice results. Although there was no significant difference of right answers of the group of NFB (F (4,28) = 2215, p = .093), NFB + tDCS were significantly increased (F (4,28) = 6557, p = .001). Each groups CPT score were compared by repeated measures of ANOVA, statistically significance was found between the correct response latencies of the NFB group (F (4,28) = 3282, p = .025) and NFB + tDCS group (F(4,28) = 3143, p = .030). When the frequency bands were compared between the NFB training group with post hoc Bonferroni pairwise comparisons showed that difference was significant between 1st practice (1.515 ± .39) and at the 10th application (1.996 ± .42 (p = .002). When the inter-application of frequency bands at the group of NFB + tDCS statistically significant between the first 1th stimulation (10.165 ± 1.34) and 10th stimulation (14.128 ± 1.72)(p = .012), The difference on theta/beta band is significant at 2th practice (2.074 ± .098) to 10th practice (1.824 ± .084) (.013). While NFB + tDCS had significance at theta/beta ratios and alpha band amplitudes, NFB group had significance at theta/SMR ratios. Results suggest that NFB training shows its effects at the end of 10 sessions, and additional use of tDCS did not accelerate improvement as expected. However, its therapeutic effect on anxiety and depression scores and inhibition deficits of subjects were clear.

Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.

EEG-based brain-computer interface (BCI) technology creates non-biological pathways for conveying a user's mental intent solely through noninvasively measured neural signals. While optimizing the performance of a single task has long been the focus of BCI research, in order to translate this technology into everyday life, realistic situations, in which multiple tasks are performed simultaneously, must be investigated. In this paper, we explore the concept of cognitive flexibility, or multitasking, within the BCI framework by utilizing a 2-D cursor control task, using sensorimotor rhythms (SMRs), and a four-target visual attention task, using steady-state visual evoked potentials (SSVEPs), both individually and simultaneously. We found no significant difference between the accuracy of the tasks when executing them alone (SMR-57.9% ± 15.4% and SSVEP-59.0% ± 14.2%) and simultaneously (SMR-54.9% ± 17.2% and SSVEP-57.5% ± 15.4%). These modest decreases in performance were supported by similar, non-significant changes in the electrophysiology of the SSVEP and SMR signals. In this sense, we report that multiple BCI tasks can be performed simultaneously without a significant deterioration in performance; this finding will help drive these systems toward realistic daily use in which a user's cognition will need to be involved in multiple tasks at once.

Platform Session – NIBS: An open-loop TMS study evaluating the impact of mu and beta oscillations on motor-evoked potential amplitudes

Neuronal networks exhibit oscillatory activity at various frequencies, which are thought to coordinate local neuronal processing and cortico-cortical communication through alternating periods of excitation and inhibition. In the motor cortex, single neuron spiking rates (Haegens et al., 2011) and neuronal population activity (Miller et al., 2012) are highest at mu and beta oscillatory troughs (maximum surface negativity) and lowest at peaks (maximum surface positivity). This relationship is strongest when subjects are at rest, when power in these frequency bands is known to be high (Pfurtscheller and Da Silva, 1999). Yet, how sensorimotor oscillatory phase and power interact to influence corticospinal excitability in intact humans is not known. Here, we explored this issue in an open-loop TMS design (i.e., timing of TMS delivery was determined by the passage of time, but not brain oscillatory state), focusing on mu and beta oscillatory activity due to their relevance to human motor control. We collected 600 MEPs for each of 20 healthy subjects using single-pulse TMS (Magstim, UK) at 120% or resting motor threshold delivered to the scalp hotspot for the right first dorsal interosseous during simultaneous EEG recordings (BrainProducts GmbH, Germany). Subsequently, data from the C4 sensor were Hjorth-transformed (central: C4; surround: FC2, FC6, CP2, CP6) and the instantaneous oscillatory phase and pre-stimulus power within 150 ms of the TMS pulse in the mu and beta bands was calculated. Peak and trough trials were defined as trials where TMS occurred at maximum surface positivity (45–135°) and maximum surface negativity (225–315°) of the oscillatory cycle, respectively. The influence of sensorimotor oscillatory phase (categorical variable, peak vs. trough), power (continuous variable) and their interaction on MEP amplitudes was statistically evaluated using separate trial-by-trial linear mixed-effects models for each frequency band. For mu activity, there was a significant PHASE x POWER interaction (p = 0.001), evident as a significantly more positive relationship between pre-stimulus mu power and MEP amplitudes for troughs than peaks (mu peak slope = −0.001 [95% CI = −0.029 to 0.027]; mu trough slope = 0.063 [95% CI = 0.034 to 0.090]). Notably, when mu power was high, MEPs were larger at troughs versus peaks, while the opposite was true when mu power was low. For beta activity, there was a significant main effect of POWER (p = 0.007), revealing a positive relationship between pre-stimulus beta power and MEP amplitudes independent of beta phase. Corticospinal excitability was highest at mu troughs during periods of high mu power, and during periods of high beta power regardless of beta phase. We conclude that sensorimotor oscillatory power and phase interact to shape human motor function, supporting the view that sensorimotor rhythms dynamically gate corticospinal excitability.

The Differences in Sensorimotor Rhythm Power During Performing In-Phase and Anti-Phase Patterns in Bimanual Coordination

The Differences in Sensorimotor Rhythm Power During Performing In-Phase and Anti-Phase Patterns in Bimanual Coordination

Enhancement of motor-imagery ability via combined action observation and motor-imagery training with proprioceptive neurofeedback

Varied individual ability to control the sensory-motor rhythms may limit the potential use of motor-imagery (MI) in neurorehabilitation and neuroprosthetics. We employed neurofeedback training of MI under action observation (AO: AOMI) with proprioceptive feedback and examined whether it could enhance MI-induced event-related desynchronization (ERD). Twenty-eight healthy young adults participated in the neurofeedback training. They performed MI while watching a video of hand-squeezing motion from a first-person perspective. Eleven participants received correct proprioceptive feedback of the same hand motion with the video, via an exoskeleton robot attached to their hand, upon their successful generation of ERD. Another nine participants received random feedback. The training lasted for approximately 20 min per day and continued for 6 days within an interval of 2 weeks. MI-ERD power was evaluated separately, without AO, on each experimental day. The MI-ERD power of the participants receiving correct feedback, as opposed to random feedback, was significantly increased after training. An additional experiment in which the remaining eight participants were trained with auditory instead of proprioceptive feedback failed to show statistically significant increase in MI-ERD power. The significant training effect obtained in shorter training time relative to previously proposed methods suggests the superiority of AOMI training and physiologically-congruent proprioceptive feedback to enhance the MI-ERD power. The proposed neurofeedback training could help patients with motor deficits to attain better use of brain-machine interfaces for rehabilitation and/or prosthesis.

B-11. Application of EEG-based brain computer interface to movement and sensory disorders

Objective To examine the effect of the EEG-based brain computer interface (BCI) training for patients with stroke, writer’s cramp and neuropathic pain. Method: Using the EEG recorded with Ag/AgCl electrodes placed at C3 and C4, as designated according to the International 10/20 system, we gave real time visual feedback to the patients with PC monitor which is placed in front of them. Participants were required to imagine the affected wrist extending in stroke and neuropathic pain patients. Patients with writer’s cramp were requested to relax their wrist flexor while extending their wrist. If these tasks were able to have an effect to the affected sensory motor cortex, we can see the change of patient’s sensory motor rhythm, for example what is called event-related desynchronization (ERD). While practicing this biofeedback system, patients made an effort to control their affected side EEG activity representing the ERD in stroke and neuropathic pain patients, and diminishing the event-related synchronization (ERS) in writer’s cramp patients. Results: After several months BCI training, some patients with stroke and neuropathic pain were able to change their EEG activity easily to have the ERD and after the 6 months training one writer’s cramp patient was able to diminish the ERS resulted in good performance. Moreover we could also confirm this change in stroke patients representing the sensory motor cortex activity by recording the functional MRI. Implications/Impact on Rehabilitation: The results suggest that this type of EEG-based brain computer interface training is useful method to improve some kinds of movement and sensory disorders.

S3-3. Clinical application of electroencephalogram-based brain-machine interfaces

A large number of electroencephalogram-based brain-machine interface (BMI) systems developed by industry and academia are now available for neurorehabilitation. However, the lack of phase 1–2 clinical trials for BMI interventions to post-stroke hemiplegia has been limited their treatment outcome. To test the safety and feasibility of BMI interventions, we conducted trials with a customly developed BMI system for patients with acute/subacute hemiparetic stroke. Four inpatients participated in this study in the early phase (7–24 days from stroke onset). Our portable BMI system fed back the amplitude of sensorimotor rhythms extracted from scalp electroencephalograms in real time. On average, 120–200 training trials of the hand motor task were successfully conducted for 3.3 days (range 2–4 days) at bedside without any adverse effects. The results clearly showed that the portable BMI system was safe and feasible, and also demonstrated electrophysiological plasticity in the damaged hemisphere via interaction with BMI technology in patients with hemiparetic stroke in the acute/subacute phases.

Event-related changes detection in sensorimotor rhythm

Brain activities initiate motion in the human body. In our research we try to detect brain electrical activities and generate control signals for robotic devices like prosthetic legs. Human legs are associated with a small representation area in the sensorimotor (SMR) cortex, which is located deep inside the inter hemispheric fissure. It is difficult to observe any electroencephalographic activity related to the legs. Detection of sensorimotor signals, based on leg imagery, could potentially be useful in medical applications, i.e. for systems that are using brain-computer interface for lower limbs assistance. We investigate reactivity of sensorimotor rhythm i.e., mu rhythm, as a result of given tasks, such as, motor execution (ME) and motor imagery (MI) of the leg. Resulting SMR was analyzed, for each task state and evaluated in terms of eventrelated de synchronization and event-related synchronization patterns. Higher power concentration was observed in the foot representation and peripheral areas, during both ME and MI tasks. No contralateral dominance was detected during left or right discrimination tasks. Results provide a foundation for leg imagery based, interfacing and control signals creation. This could be used for locomotion functions' restoration in a lower limb wearable rehabilitation system. Spinal cord injury patients could, also, be potential users of this type of biomechanical systems.

Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis.

Brain‐computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here, we provide a first meta‐analysis evaluating the clinical effectiveness of BCI‐based post‐stroke motor rehabilitation. Trials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post‐stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random‐effects inverse variance method was used to calculate the summary effect size. We initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post‐stroke survivors that fulfilled the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta‐analysis. Motor improvements, mostly quantified by the upper limb Fugl‐Meyer Assessment (FMA‐UE), exceeded the minimal clinically important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference of 0.79 (95% CI: 0.37 to 1.20) in FMA‐UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI‐induced functional and structural neuroplasticity at a subclinical level. This suggests that BCI technology could be an effective intervention for post‐stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.

Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?

In light of the shortcomings of current restorative brain-computer interfaces (BCI), this study investigated the possibility of using EMG to detect hand/wrist extension movement intention to trigger robot-assisted training in individuals without residual movements. We compared movement intention detection using an EMG detector with a sensorimotor rhythm based EEG-BCI using only ipsilesional activity. This was carried out on data of 30 severely affected chronic stroke patients from a randomized control trial using an EEG-BCI for robot-assisted training. The results indicate the feasibility of using EMG to detect movement intention in this severely handicapped population; probability of detecting EMG when patients attempted to move was higher (p 0.001) than at rest. Interestingly, 22 out of 30 (or 73%) patients had sufficiently strong EMG in their finger/wrist extensors. Furthermore, in patients with detectable EMG, there was poor agreement between the EEG and EMG intent detectors, which indicates that these modalities may detect different processes. A substantial segment of severely affected stroke patients may benefit from EMG-based assisted therapy. When compared to EEG, a surface EMG interface requires less preparation time, which is easier to don/doff, and is more compact in size. This study shows that a large proportion of severely affected stroke patients have residual EMG, which yields a direct and practical way to trigger robot-assisted training.

Decoding Imagined 3D Hand Movement Trajectories From EEG: Evidence to Support the Use of Mu, Beta, and Low Gamma Oscillations.

Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8–12 Hz) and beta (12–28 Hz) bands.Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks (i.e., imagined 3D trajectory of the hand joint) and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep).Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28–40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations.Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements.

Ongoing brain rhythms shape I-wave properties in a computational model

BackgroundResponses to transcranial magnetic stimulation (TMS) are notoriously variable. Previous studies have observed a dependence of TMS-induced responses on ongoing brain activity, for instance sensorimotor rhythms. This suggests an opportunity for the development of more effective stimulation protocols through closed-loop TMS-EEG. However, it is not yet clear how features of ongoing activity affect the responses of cortical circuits to TMS. Objective/HypothesisHere we investigate the dependence of TMS-responses on power and phase of ongoing oscillatory activity in a computational model of TMS-induced I-waves. MethodsThe model comprises populations of cortical layer 2/3 (L2/3) neurons and a population of cortical layer 5 (L5) neurons and generates I-waves in response to TMS. Oscillatory input to the L2/3 neurons induces rhythmic fluctuations in activity of L5 neurons. TMS pulses are simulated at different phases and amplitudes of the ongoing rhythm. ResultsThe model shows a robust dependence of I-wave properties on phase and power of ongoing rhythms, with the strongest response occurring for TMS at maximal L5 depolarization. The amount of phase-modulation depends on stimulation intensity, with stronger modulation for lower intensity. ConclusionThe model predicts that responses to TMS are highly variable for low stimulation intensities if ongoing brain rhythms are not taken into account. Closed-loop TMS-EEG holds promise for obtaining more reliable TMS effects.

Towards a Hybrid BCI Gaming Paradigm Based on Motor Imagery and SSVEP

ABSTRACT Brain-computer interfaces (BCIs) not only can allow individuals to voluntarily control external devices, helping to restore lost motor functions of the disabled, but can also be used by healthy users for entertainment and gaming applications. In this study, we proposed a hybrid BCI paradigm to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. In this paradigm, we combined motor imagery (MI) and steady-state visually evoked potentials (SSVEPs) to generate multiple commands. A classic game, Tetris, was chosen as the control object. The novelty of this study includes the effective usage of a “dwell time” approach and fusion rules to design BCI games. To demonstrate the feasibility of the proposed hybrid paradigm, ten subjects were chosen to participate in online control experiments. The experimental results showed that all subjects successfully completed the predefined tasks with high accuracy. This proposed hybrid BCI paradigm could potentially provide those who suffer disability or paralysis with additional entertainment options, such as brain-actuated games, that could improve their happiness and quality of life. Abbreviations: BCI: brain-computer interface; EEG: electroencephalogram; MI: motor imagery; SSVEP: steady-state visually evoked potential; ERP: event-related potential; SMR: sensorimotor rhythm; VEP: visual evoked potential; TCP/IP: transmission control protocol/internet protocol; GUI: graphical user interface; ERD/ERS: event-related desynchronization/synchronization; CIC: control intention classifier; LRC: left/right classifier; CSP: common spatial pattern; LDA: linear discriminant analysis; ROC: receiver operating characteristic; TPR: true positive rate; FPR: false positive rate; CCA: canonical correlation analysis.

Single-session tDCS over the dominant hemisphere affects contralateral spectral EEG power, but does not enhance neurofeedback-guided event-related desynchronization of the non-dominant hemisphere's sensorimotor rhythm.

Background and objectiveTranscranial direct current stimulation (tDCS) and neurofeedback-guided motor imagery (MI) have attracted considerable interest in neurorehabilitation, given their ability to influence neuroplasticity. As tDCS has been shown to modulate event-related desynchronization (ERD), the neural signature of motor imagery detected for neurofeedback, a combination of the techniques was recently proposed. One limitation of this approach is that the area targeted for stimulation is the same from which the signal for neurofeedback is acquired. As tDCS may interfere with proximal electroencephalographic (EEG) electrodes, in this study our aim was to test whether contralateral tDCS could have interhemispheric effects on the spectral power of the unstimulated hemisphere, possibly mediated by transcallosal connection, and whether such effects could be used to enhance ERD magnitudes. A contralateral stimulation approach would indeed facilitate co-registration, as the stimulation electrode would be far from the recording sites.MethodsTwenty right-handed healthy volunteers (aged 21 to 32) participated in the study: ten assigned to cathodal, ten to anodal versus sham stimulation. We applied stimulation over the dominant (left) hemisphere, and assessed ERD and spectral power over the non-dominant (right) hemisphere. The effect of tDCS was evaluated over time. Spectral power was assessed in theta, alpha and beta bands, under both rest and MI conditions, while ERD was evaluated in alpha and beta bands.ResultsTwo main findings emerged: (1) contralateral alpha-ERD was reduced after anodal (p = 0.0147), but not enhanced after cathodal tDCS; (2) both stimulations had remote effects on the spectral power of the contralateral hemisphere, particularly in theta and alpha (significant differences in the topographical t-value maps).ConclusionThe absence of contralateral cathodal ERD enhancement suggests that the protocol is not applicable in the context of MI training. Nevertheless, ERD results of anodal and spectral power results of both stimulations complement recent findings on the distant tDCS effects between functionally related areas.

Android Feedback-Based Training Modulates Sensorimotor Rhythms During Motor Imagery.

EEG-based brain computer interface (BCI) systems have demonstrated potential to assist patients with devastating motor paralysis conditions. However, there is great interest in shifting the BCI trend toward applications aimed at healthy users. Although BCI operation depends on technological factors (i.e., EEG pattern classification algorithm) and human factors (i.e., how well the person can generate good quality EEG patterns), it is the latter that is least investigated. In order to control a motor imagery-based BCI, users need to learn to modulate their sensorimotor brain rhythms by practicing motor imagery using a classical training protocol with an abstract visual feedback. In this paper, we investigate a different BCI training protocol using a human-like android robot (Geminoid HI-2) to provide realistic visual feedback. The proposed training protocol addresses deficiencies of the classical approach and takes the advantage of body-abled user capabilities. Experimental results suggest that android feedback-based BCI training improves the modulation of sensorimotor rhythms during motor imagery task. Moreover, we discuss how the influence of body ownership transfer illusion toward the android might have an effect on the modulation of event-related desynchronization/synchronization activity.

EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought

BackgroundThe Brain Computer Interfaces (BCI) are devices allowing direct communication between the brain of a user and a machine. This technology can be used by disabled people in order to improve their independence and maximize their capabilities such as finding an object in the environment. Such devices can be realized by the non-invasive measurement of information from the cortex by electroencephalography (EEG). MethodsOur work proposes a novel BCI system that consists of controlling a robot arm based on the user's thought. Four subjects (1 female and 3 males) aged between 20 and 29 years have participated to our experiment. They have been instructed to imagine the execution of movements of the right hand, the left hand, both right and left hands or the movement of the feet depending on the protocol established.EMOTIV EPOC headset was used to record neuronal electrical activities from the subject's scalp, these activities were then sent to the computer for analysis. Feature extraction was performed using the Principal Component Analysis (PCA) method combined with the Fast Fourier transform (FFT) spectrum within the frequency band responsible for sensorimotor rhythms (8 Hz–22 Hz).These features were then fed into a Support Vector Machine (SVM) classifier based on a Radial Base Function (RBF) whose outputs were translated into commands to control the robot arm. ResultsThe proposed BCI enabled the control of the robot arm in the four directions: right, left, up and down, achieving an averaged accuracy of 85.45% across all the subjects. ConclusionThe results obtained would encourage, with further developments, the use of the proposed BCI to perform more complex tasks such as execution of successive movements or stopping the execution once a searched object is detected. This would provide a useful assistance means for people with motor impairment.

The Way You Make Me Feel: Psychological and cerebral responses to music during real-life physical activity

BackgroundThe brain mechanisms that underlie the psychological effects of auditory stimuli during physical activity are hitherto under-researched; particularly so in ecologically valid settings. The objective of the present experiment was to investigate the effects of two contrasting auditory stimuli conditions on psychological responses and brain activity during an outdoor walking task. MethodsTwenty-four participants were required to walk 400 m at a pace of their choosing and report perceptual (state attention and perceived exertion) and affective (valence, arousal, and perceived enjoyment) outcomes immediately after each exercise bout. Three conditions were administered in a randomised and fully counterbalanced order (control, podcast, and music). State-of-the-art, portable EEG technology was used to facilitate measurement during the walking task. Fast Fourier Transform was used to decompose the brain's electrical activity into different band waves (lower-alpha, upper-alpha, sensorimotor rhythm, and beta). ResultsThe results indicated that music up-regulated beta waves, led to more dissociative thoughts, induced more positive affective responses, up-regulated arousal, and enhanced perceived enjoyment to a greater degree when compared to control and podcast. ConclusionsRearrangement of beta frequencies in the brain appears to elicit a more positive emotional state wherein participants are more likely to dissociate from internal sensory signals and focus on task-irrelevant factors. The portable EEG system used in the present study appears to accurately measure electrical activity in the brain during light-intensity physical activities and is effective in reducing electrical artefacts caused by body and cable movements.

EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century.

People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain–computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future.