Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an ...algorithm to determine the IAF from short-term resting-state scalp EEG data. First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using a large-scale dataset of scalp EEG during motor imagery-based BCI use and independent dataset for generalizability confirmation (N = 147). Next, we characterized the relationship between IAF and responsive frequency band of sensorimotor rhythm, which exhibits prominent event-related desynchronization (SMR-ERD) while attempting unilateral and movement. The proposed sequential Bayesian estimation algorithm (Rapid-IAF) determined IAF from less than 26-second resting EEG data among 95% of participants, indicating a clear advance over the conventional methods, which uses 2-15 minutes of data in previous literatures. We confirmed that the determined IAF corresponded to the frequency of SMR, which exhibits the most prominent event-related desynchronization during BCI use (individual SMR-ERD frequency, ISF). Moreover, intraclass correlation revealed that the estimated IAF was more stable than ISF across sessions, suggesting its reliability and utility for robust BCI use without intermittent recalibration. In summary, our method rapidly and reliably determined IAF compared to the conventional method using the spectral power change based on task-related response. The method can be utilized to quick BCI initialization. The demonstration of rapid, task-free parametrization of individual variability of neural responses would be of importance for future BCI systems including neural communication via a cursor, an avatar or robots, and closed-loop neurofeedback training.
There is increasing interest in electroencephalogram (EEG)-based brain-computer interface (BCI) as a tool for rehabilitation of upper limb motor functions in hemiplegic stroke patients. This type of ...BCI often exploits mu and beta oscillations in EEG recorded over the sensorimotor areas, and their event-related desynchronization (ERD) following motor imagery is believed to represent increased sensorimotor cortex excitability. However, it remains unclear whether the sensorimotor cortex excitability is actually correlated with ERD. Thus we assessed the association of ERD with primary motor cortex (M1) excitability during motor imagery of right wrist movement. M1 excitability was tested by motor evoked potentials (MEPs), short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF) with transcranial magnetic stimulation (TMS). Twenty healthy participants were recruited. The participants performed 7 s of rest followed by 5 s of motor imagery and received online visual feedback of the ERD magnitude of the contralateral hand M1 while performing the motor imagery task. TMS was applied to the right hand M1 when ERD exceeded predetermined thresholds during motor imagery. MEP amplitudes, SICI, and ICF were recorded from the agonist muscle of the imagined hand movement. Results showed that the large ERD during wrist motor imagery was associated with significantly increased MEP amplitudes and reduced SICI but no significant changes in ICF. Thus ERD magnitude during wrist motor imagery represents M1 excitability. This study provides electrophysiological evidence that a motor imagery task involving ERD may induce changes in corticospinal excitability similar to changes accompanying actual movements.
Abstract
Neurofeedback training using electroencephalogram (EEG)-based brain–computer interfaces (BCIs) combined with mental rehearsals of motor behavior has demonstrated successful self-regulation ...of motor cortical excitability. However, it remains unclear whether the acquisition of skills to voluntarily control neural excitability is accompanied by structural plasticity boosted by neurofeedback. Here, we sought short-term changes in cortical structures induced by 30 min of BCI-based neurofeedback training, which aimed at the regulation of sensorimotor rhythm (SMR) in scalp EEG. When participants performed kinesthetic motor imagery of right finger movement with online feedback of either event-related desynchronisation (ERD) of SMR magnitude from the contralateral sensorimotor cortex (SM1) or those from other participants (i.e. placebo), the learning rate of SMR-ERD control was significantly different. Although overlapped structural changes in gray matter volumes were found in both groups, significant differences revealed by group-by-group comparison were spatially different; whereas the veritable neurofeedback group exhibited sensorimotor area-specific changes, the placebo exhibited spatially distributed changes. The white matter change indicated a significant decrease in the corpus callosum in the verum group. Furthermore, the learning rate of SMR regulation was correlated with the volume changes in the ipsilateral SM1, suggesting the involvement of interhemispheric motor control circuitries in BCI control tasks.
Oscillatory electroencephalographic (EEG) activity is associated with the excitability of cortical regions. Visual feedback of EEG-oscillations may promote sensorimotor cortical activation, but its ...spatial specificity is not truly guaranteed due to signal interaction among interhemispheric brain regions. Guiding spatially specific activation is important for facilitating neural rehabilitation processes. Here, we tested whether users could explicitly guide sensorimotor cortical activity to the contralateral or ipsilateral hemisphere using a spatially bivariate EEG-based neurofeedback that monitors bi-hemispheric sensorimotor cortical activities for healthy participants. Two different motor imageries (shoulder and hand MIs) were selected to see how differences in intrinsic corticomuscular projection patterns might influence activity lateralization. We showed sensorimotor cortical activities during shoulder, but not hand MI, can be brought under ipsilateral control with guided EEG-based neurofeedback. These results are compatible with neuroanatomy; shoulder muscles are innervated bihemispherically, whereas hand muscles are mostly innervated contralaterally. We demonstrate the neuroanatomically-inspired approach enables us to investigate potent neural remodeling functions that underlie EEG-based neurofeedback via a BCI.
Because recovery from upper limb paralysis after stroke is challenging, compensatory approaches have been the main focus of upper limb rehabilitation. However, based on fundamental and clinical ...research indicating that the brain has a far greater potential for plastic change than previously thought, functional restorative approaches have become increasingly common. Among such interventions, constraint-induced movement therapy, task-specific training, robotic therapy, neuromuscular electrical stimulation (NMES), mental practice, mirror therapy, and bilateral arm training are recommended in recently published stroke guidelines. For severe upper limb paralysis, however, no effective therapy has yet been established. Against this background, there is growing interest in applying brain–machine interface (BMI) technologies to upper limb rehabilitation. Increasing numbers of randomized controlled trials have demonstrated the effectiveness of BMI neurorehabilitation, and several meta-analyses have shown medium to large effect sizes with BMI therapy. Subgroup analyses indicate higher intervention effects in the subacute group than the chronic group, when using movement attempts as the BMI-training trigger task rather than using motor imagery, and using NMES as the external device compared with using other devices. The Keio BMI team has developed an electroencephalography-based neurorehabilitation system and has published clinical and basic studies demonstrating its effectiveness and neurophysiological mechanisms. For its wider clinical application, the positioning of BMI therapy in upper limb rehabilitation needs to be clarified, BMI needs to be commercialized as an easy-to-use and cost-effective medical device, and training systems for rehabilitation professionals need to be developed. A technological breakthrough enabling selective modulation of neural circuits is also needed.
Optimizing the training regimen depending on neuromuscular fatigue is crucial for the well-being of professionals intensively practicing motor skills, such as athletes and musicians, as persistent ...fatigue can hinder learning and cause neuromuscular injuries. However, accurate assessment of fatigue is challenging because of the dissociation between subjective perception and its impact on motor and cognitive performance. To address this issue, we investigated the interplay between fatigue and learning development in 28 pianists during three hours of auditory-motor training, dividing them into two groups subjected to different resting conditions. Changes in behavior and muscle activity during training were measured to identify potential indicators capable of detecting fatigue before subjective awareness. Our results indicate that motor learning and fatigue development are independent of resting frequency and timing. Learning indices, such as reduction in force and timing errors throughout training, did not differ between the groups. No discernible distinctions emerged in fatigue-related behavioral and physiological indicators between the groups. Regression analysis revealed that several fatigue-related indicators, such as tapping speed variability and electromyogram amplitude per unit force, could explain the learning of timing and force control. Our findings suggest the absence of a universal resting schedule for optimizing auditory-motor learning.
...a comparison of the performance of popular real-time detrending algorithms using both data and simulations finds generally good performance across algorithms, but differential levels of ...vulnerability to different types of artifacts. The experimental results are similar between the two approaches, but only the model-based dynamic causal modeling successfully captures specific network properties. Since to define the model a priori is not always possible, the exploratory data-driven approach might serve as a tool that provides rough clues of target components for post-hoc modeling. ...they suggest a unique hybrid approach combining model-based and data-driven analyses. To explore the feasibility of such training, a study in healthy subjects trained down-regulation of the auditory cortex in the presence of noise and in the context of directed attention strategies, using a yoked-sham control group (Sherwood et al., 2019).
Concomitant with the development of deep learning, brain-computer interface (BCI) decoding technology has been rapidly evolving. Convolutional neural networks (CNNs), which are generally used as ...electroencephalography (EEG) classification models, are often deployed in BCI prototypes to improve the estimation accuracy of a participant's brain activity. However, because most BCI models are trained, validated, and tested
within-subject cross-validation and there is no corresponding generalization model, their applicability to unknown participants is not guaranteed. In this study, to facilitate the generalization of BCI model performance to unknown participants, we trained a model comprising multiple layers of residual CNNs and visualized the reasons for BCI classification to reveal the location and timing of neural activities that contribute to classification. Specifically, to develop a BCI that can distinguish between rest, left-hand movement, and right-hand movement tasks with high accuracy, we created multilayers of CNNs, inserted residual networks into the multilayers, and used a larger dataset than in previous studies. The constructed model was analyzed with gradient-class activation mapping (Grad-CAM). We evaluated the developed model
subject cross-validation and found that it achieved significantly improved accuracy (85.69 ± 1.10%) compared with conventional models or without residual networks. Grad-CAM analysis of the classification of cases in which our model produced correct answers showed localized activity near the premotor cortex. These results confirm the effectiveness of inserting residual networks into CNNs for tuning BCI. Further, they suggest that recording EEG signals over the premotor cortex and some other areas contributes to high classification accuracy.
In the field of psychology, it has been well established that there are two types of motor imagery such as kinesthetic motor imagery (KMI) and visual motor imagery (VMI), and the subjective ...evaluation for vividness of motor imagery each differs across individuals. This study aimed to examine how the motor imagery ability assessed by the psychological scores is associated with the physiological measure using electroencephalogram (EEG) sensorimotor rhythm during KMI task. First, 20 healthy young individuals evaluated subjectively how vividly they can perform each of KMI and VMI by using the Kinesthetic and Visual Imagery Questionnaire (KVIQ). We assessed their motor imagery abilities by summing each of KMI and VMI scores in KVIQ (KMI
and VMI
). Second, in physiological experiments, they repeated two strengths (10 and 40% of maximal effort) of isometric voluntary wrist-dorsiflexion. Right after each contraction, they also performed its KMI. The scalp EEGs over the sensorimotor cortex were recorded during the tasks. The EEG power is known to decrease in the alpha-and-beta band (7-35 Hz) from resting state to performing state of voluntary contraction (VC) or motor imagery. This phenomenon is referred to as event-related desynchronization (ERD). For each strength of the tasks, we calculated the maximal peak of ERD during VC, and that during its KMI, and measured the degree of similarity (ERD
) between them. The results showed significant negative correlations between KMI
and ERD
for both strengths (
< 0.05) (i.e., the higher the KMI
, the smaller the ERD
). These findings suggest that in healthy individuals with higher motor imagery ability from a first-person perspective, KMI efficiently engages the shared cortical circuits corresponding with motor execution, including the sensorimotor cortex, with high compliance.
A variety of neural substrates are implicated in the initiation, coordination, and stabilization of voluntary movements underpinned by adaptive contraction and relaxation of agonist and antagonist ...muscles. To achieve such flexible and purposeful control of the human body, brain systems exhibit extensive modulation during the transition from resting state to motor execution and to maintain proper joint impedance. However, the neural structures contributing to such sensorimotor control under unconstrained and naturalistic conditions are not fully characterized. To elucidate which brain regions are implicated in generating and coordinating voluntary movements, we employed a physiologically inspired, two-stage method to decode relaxation and three patterns of contraction in unilateral finger muscles (i.e., extension, flexion, and co-contraction) from high-density scalp electroencephalograms (EEG). The decoder consisted of two parts employed in series. The first discriminated between relaxation and contraction. If the EEG data were discriminated as contraction, the second stage then discriminated among the three contraction patterns. Despite the difficulty in dissociating detailed contraction patterns of muscles within a limb from scalp EEG signals, the decoder performance was higher than chance-level by 2-fold in the four-class classification. Moreover, weighted features in the trained decoders revealed EEG features differentially contributing to decoding performance. During the first stage, consistent with previous reports, weighted features were localized around sensorimotor cortex (SM1) contralateral to the activated fingers, while those during the second stage were localized around ipsilateral SM1. The loci of these weighted features suggested that the coordination of unilateral finger muscles induced different signaling patterns in ipsilateral SM1 contributing to motor control. Weighted EEG features enabled a deeper understanding of human sensorimotor processing as well as of a more naturalistic control of brain-computer interfaces.
PDF
