Coarse Electrocorticographic Decoding of Ipsilateral Reach in Patients with Brain Lesions

In patients with unilateral upper limb paralysis from strokes and other brain lesions, strategies for functional recovery may eventually include brain-machine interfaces (BMIs) using control signals from residual sensorimotor systems in the damaged hemisphere. When voluntary movements of the contralateral limb are not possible due to brain pathology, initial training of such a BMI may require use of the unaffected ipsilateral limb. We conducted an offline investigation of the feasibility of decoding ipsilateral upper limb movements from electrocorticographic (ECoG) recordings in three patients with different lesions of sensorimotor systems associated with upper limb control. We found that the first principal component (PC) of unconstrained, naturalistic reaching movements of the upper limb could be decoded from ipsilateral ECoG using a linear model. ECoG signal features yielding the best decoding accuracy were different across subjects. Performance saturated with very few input features. Decoding performances of 0.77, 0.73, and 0.66 (median Pearson''s r between the predicted and actual first PC of movement using nine signal features) were achieved in the three subjects. The performance achieved here with small numbers of electrodes and computationally simple decoding algorithms suggests that it may be possible to control a BMI using ECoG recorded from damaged sensorimotor brain systems.     

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

There are several advantages that functional near-infrared spectroscopy (fNIRS) presents in the study of the neural control of human movement. It is relatively flexible with respect to participant positioning and allows for some head movements during tasks. Additionally, it is inexpensive, light weight, and portable, with very few contraindications to its use. This presents a unique opportunity to study functional brain activity during motor tasks in individuals who are typically developing, as well as those with movement disorders, such as cerebral palsy. An additional consideration when studying movement disorders, however, is the quality of actual movements performed and the potential for additional, unintended movements. Therefore, concurrent monitoring of both blood flow changes in the brain and actual movements of the body during testing is required for appropriate interpretation of fNIRS results. Here, we show a protocol for the combination of fNIRS with muscle and kinematic monitoring during motor tasks. We explore gait, a unilateral multi-joint movement (cycling), and two unilateral single-joint movements (isolated ankle dorsiflexion, and isolated hand squeezing). The techniques presented can be useful in studying both typical and atypical motor control, and can be modified to investigate a broad range of tasks and scientific questions.     

Assessing Movement Factors in Upper Limb Kinematics Decoding from EEG Signals

The past decades have seen the rapid development of upper limb kinematics decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Recent studies show that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters. From these studies, it could be concluded that the accuracy of upper limb kinematics decoding depends, at least partially, on the characteristics of the performed movement. In this paper, we have studied upper limb movements with different speeds and trajectories in a controlled environment to analyze the influence of movement variability in the decoding performance. To that end, low frequency components of the EEG signals have been decoded with linear models to obtain the position of the volunteer’s hand during performed trajectories grasping the end effector of a planar manipulandum. The results confirm that it is possible to obtain kinematic information from low frequency EEG signals and show that decoding performance is significantly influenced by movement variability and tracking accuracy as continuous and slower movements improve the accuracy of the decoder. This is a key factor that should be taken into account in future experimental designs.     

Neuron selection by relative importance for neural decoding of dexterous finger prosthesis control application

Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain–machine interfaces for direct neural control of hand prostheses.     

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.     

An Internet-Based Real-Time Audiovisual Link for Dual MEG Recordings

Hyperscanning

Most neuroimaging studies of human social cognition have focused on brain activity of single subjects. More recently, “two-person neuroimaging” has been introduced, with simultaneous recordings of brain signals from two subjects involved in social interaction. These simultaneous “hyperscanning” recordings have already been carried out with a spectrum of neuroimaging modalities, such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS).

Dual MEG Setup

We have recently developed a setup for simultaneous magnetoencephalographic (MEG) recordings of two subjects that communicate in real time over an audio link between two geographically separated MEG laboratories. Here we present an extended version of the setup, where we have added a video connection and replaced the telephone-landline-based link with an Internet connection. Our setup enabled transmission of video and audio streams between the sites with a one-way communication latency of about 130 ms. Our software that allows reproducing the setup is publicly available.

Validation

We demonstrate that the audiovisual Internet-based link can mediate real-time interaction between two subjects who try to mirror each others’ hand movements that they can see via the video link. All the nine pairs were able to synchronize their behavior. In addition to the video, we captured the subjects’ movements with accelerometers attached to their index fingers; we determined from these signals that the average synchronization accuracy was 215 ms. In one subject pair we demonstrate inter-subject coherence patterns of the MEG signals that peak over the sensorimotor areas contralateral to the hand used in the task.     

High accuracy decoding of movement target direction in non-human primates based on common spatial patterns of local field potentials

Background

The current development of brain-machine interface technology is limited, among other factors, by concerns about the long-term stability of single- and multi-unit neural signals. In addition, the understanding of the relation between potentially more stable neural signals, such as local field potentials, and motor behavior is still in its early stages.

Methodology/Principal Findings

We tested the hypothesis that spatial correlation patterns of neural data can be used to decode movement target direction. In particular, we examined local field potentials (LFP), which are thought to be more stable over time than single unit activity (SUA). Using LFP recordings from chronically implanted electrodes in the dorsal premotor and primary motor cortex of non-human primates trained to make arm movements in different directions, we made the following observations: (i) it is possible to decode movement target direction with high fidelity from the spatial correlation patterns of neural activity in both primary motor (M1) and dorsal premotor cortex (PMd); (ii) the decoding accuracy of LFP was similar to the decoding accuracy obtained with the set of SUA recorded simultaneously; (iii) directional information varied with the LFP frequency sub-band, being greater in low (0.3–4 Hz) and high (48–200 Hz) frequency bands than in intermediate bands; (iv) the amount of directional information was similar in M1 and PMd; (v) reliable decoding was achieved well in advance of movement onset; and (vi) LFP were relatively stable over a period of one week.

Conclusions/Significance

The results demonstrate that the spatial correlation patterns of LFP signals can be used to decode movement target direction. This finding suggests that parameters of movement, such as target direction, have a stable spatial distribution within primary motor and dorsal premotor cortex, which may be used for brain-machine interfaces.     

A review on directional information in neural signals for brain-machine interfaces

Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.     

fMRI responses of the brain during active and passive movements in left-handed subjects

The hemodynamic (magnetic resonance imaging, fMRI, 3T) brain responses were studied in 15 left-handed healthy subjects performing active and passive movements of the dominant and non-dominant hands. Group and individual fMRI responses to the motor load were analyzed. It was found that, during the active movements of dominant and non-dominant hands, the main activation cluster appeared in the preand postcentral gyrus of the contralateral hemisphere and which topographically similar during active and passive movements. The activation cluster of greater volume was identified in these areas; the response was more diffused during the non-dominant hand movements in comparison with the dominant hand. During passive movements, the cortical activation clusters of a smaller volume in comparison with the active movements were found, which was expressed most clearly during the performance of non-dominant hand movements and could reflect the weakening of the control from the cortical structures in these conditions.     

Localizing Movement-Related Primary Sensorimotor Cortices with Multi-Band EEG Frequency Changes and Functional MRI

Electroencephalographic (EEG) oscillations in multiple frequency bands can be observed during functional activity of the cerebral cortex. An important question is whether activity of focal areas of cortex, such as during finger movements, is tracked by focal oscillatory EEG changes. Although a number of studies have compared EEG changes to functional MRI hemodynamic responses, we can find no previous research that relates the fMRI hemodynamic activity to localization of the multiple EEG frequency changes observed in motor tasks. In the present study, five participants performed similar thumb and finger movement tasks in parallel EEG and functional MRI studies. We examined changes in five frequency bands (from 5–120 Hz) and localized them using 256 dense-array EEG (dEEG) recordings and high-resolution individual head models. These localizations were compared with fMRI localizations in the same participants. Results showed that beta-band (14–30 Hz) desynchronizations (power decreases) were the most robust effects, appearing in all individuals, consistently localized to the hand region of the primary motor cortex, and consistently aligned with fMRI localizations.     

How Thoughts Give Rise to Action - Conscious Motor Intention Increases the Excitability of Target-Specific Motor Circuits

The present study shows evidence for conscious motor intention in motor preparation prior to movement execution. We demonstrate that conscious motor intention of directed movement, combined with minimally supra-threshold transcranial magnetic stimulation (TMS) of the motor cortex, determines the direction and the force of resulting movements, whilst a lack of intention results in weak and omni-directed muscle activation. We investigated changes of consciously intended goal directed movements by analyzing amplitudes of motor-evoked potentials of the forearm muscle, flexor carpi radialis (FCR), and extensor carpi radialis (ECR), induced by transcranial magnetic stimulation over the right motor cortex and their motor outcome. Right-handed subjects were asked to develop a strong intention to move their left wrist (flexion or extension), without any overt motor output at the wrist, prior to brain stimulation. Our analyses of hand acceleration and electromyography showed that during the strong motor intention of wrist flexion movement, it evoked motor potential responses that were significantly larger in the FCR muscle than in the ECR, whilst the opposite was true for an extension movement. The acceleration data on flexion/extension corresponded to this finding. Under no-intention conditions again, which served as a reference for motor evoked potentials, brain stimulation resulted in undirected and minimally simultaneous extension/flexion innervation and virtually no movement. These results indicate that conscious intentions govern motor function, which in turn shows that a neuronal activation representing an “intention network” in the human brain pre-exists, and that it functionally represents target specific motor circuits. Until today, it was unclear whether conscious motor intention exists prior to movement, or whether the brain constructs such an intention after movement initiation. Our study gives evidence that motor intentions become aware before any motor execution.     

Hemodynamic changes in cortical sensorimotor systems following hand and orofacial motor tasks and pulsed pneumotactile stimulation

We performed a functional near-infrared spectroscopy (fNIRS) study of the evoked hemodynamic responses seen in hand and face sensorimotor cortical representations during (1) active motor tasks and (2) pulsed pneumotactile stimulation. Contralateral fNIRS measurements were performed on 22 healthy adult participants using a block paradigm that consisted of repetitive right hand and right oral angle somatosensory stimulation using a pulsed pneumotactile array stimulator, and repetitive right-hand grip compression and bilabial compressions on strain gages. Results revealed significant oxyhemoglobin (HbO) modulation across stimulus conditions in corresponding somatotopic cortical regions. Of the 22 participants, 86% exhibited a decreased HbO response during at least one of the stimulus conditions, which may be indicative of cortical steal, or hypo-oxygenation occurring in channels adjacent to the primary areas of activation. Across all conditions, 56% of participants’ HbO responses were positive and 44% were negative. Hemodynamic responses most likely differed across hand and face motor and somatosensory cortical regions due to differences in regional arterial/venous anatomy, cortical vascular beds, extent and orientation of somatotopy, task dynamics, and mechanoreceptor typing in hand and face. The combination of optical imaging and task conditions allowed for non-invasive examination of hemodynamic changes in somatosensory and motor cortices using natural, pneumatic stimulation of glabrous hand and hairy skin of the lower face and functionally relevant and measurable motor tasks involving the same structures.     

Decoding Individual Finger Movements from One Hand Using Human EEG Signals

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.     

Two Agents in the Brain: Motor Control of Unimanual and Bimanual Reaching Movements

Previous studies have suggested that the left and right hands have different specialties for motor control that can be represented as two agents in the brain. This study examined how coordinated movements are performed during bimanual reaching tasks to highlight differences in the characteristics of the hands. We examined motor movement accuracy, reaction time, and movement time in right-handed subjects performing a three-dimensional motor control task (visually guided reaching). In the no-visual-feedback condition, right-hand movement had lower accuracy and a shorter reaction time than did left-hand movement, whereas bimanual movement had the longest reaction time, but the best accuracy. This suggests that the two hands have different internal models and specialties: closed-loop control for the right hand and open-loop control for the left hand. Consequently, during bimanual movements, both models might be used, creating better control and planning (or prediction), but requiring more computation time compared to the use of one hand only.     

Alien hand syndrome: neural correlates of movements without conscious will

Background

The alien hand syndrome is a striking phenomenon characterized by purposeful and autonomous movements that are not voluntarily initiated. This study aimed to examine neural correlates of this rare neurological disorder in a patient with corticobasal degeneration and alien hand syndrome of the left hand.

Methodology/Principal Findings

We employed functional magnetic resonance imaging to investigate brain responses associated with unwanted movements in a case study. Results revealed that alien hand movements involved a network of brain activations including the primary motor cortex, premotor cortex, precuneus, and right inferior frontal gyrus. Conscious and voluntary movements of the alien hand elicited a similar network of brain responses but lacked an activation of the inferior frontal gyrus. The results demonstrate that alien and unwanted movements may engage similar brain networks than voluntary movements, but also imply different functional contributions of prefrontal areas. Since the inferior frontal gyrus was uniquely activated during alien movements, the results provide further support for a specific role of this brain region in inhibitory control over involuntary motor responses.

Conclusions/Significance

We discuss the outcome of this study as providing evidence for a distributed neural network associated with unwanted movements in alien hand syndrome, including brain regions known to be related to movement execution and planning as well as areas that have been linked to inhibition control (inferior frontal gyrus) and experience of agency (precuneus).     

Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks

Non-invasive Brain-Computer Interfaces (BCI) have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) in an asynchronous Sensory Motor rhythm (SMR)-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm—Left-Arm—Right-Hand—Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs) have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.     

Grasp Detection from Human ECoG during Natural Reach-to-Grasp Movements

Various movement parameters of grasping movements, like velocity or type of the grasp, have been successfully decoded from neural activity. However, the question of movement event detection from brain activity, that is, decoding the time at which an event occurred (e.g. movement onset), has been addressed less often. Yet, this may be a topic of key importance, as a brain-machine interface (BMI) that controls a grasping prosthesis could be realized by detecting the time of grasp, together with an optional decoding of which type of grasp to apply. We, therefore, studied the detection of time of grasps from human ECoG recordings during a sequence of natural and continuous reach-to-grasp movements. Using signals recorded from the motor cortex, a detector based on regularized linear discriminant analysis was able to retrieve the time-point of grasp with high reliability and only few false detections. Best performance was achieved using a combination of signal components from time and frequency domains. Sensitivity, measured by the amount of correct detections, and specificity, represented by the amount of false detections, depended strongly on the imposed restrictions on temporal precision of detection and on the delay between event detection and the time the event occurred. Including neural data from after the event into the decoding analysis, slightly increased accuracy, however, reasonable performance could also be obtained when grasping events were detected 125 ms in advance. In summary, our results provide a good basis for using detection of grasping movements from ECoG to control a grasping prosthesis.     

The changes in the hemodynamic activity of the brain during motor imagery training with the use of brain-computer interface

With the use of functional MRI (fMRI), we studied the changes in brain hemodynamic activity of healthy subjects during motor imagery training with the use brain-computer interface (BCI), which is based on the recognition of EEG patterns of imagined movements. ANOVA dispersion analysis showed there are 14 areas of the brain where statistically significant changes were registered. Detailed analysis of the activity in these areas before and after training (Student’s and Mann-Whitney tests) showed that the real amount of such areas is five; these are Brodmann areas 44 and 45, insula, middle frontal gyrus and anterior cingulate gyrus. We suggest that these changed are caused by the formation of memory traces of those brain activity patterns which are most accurately recognized by BCI classifiers as correspondent with limb movements imagery. We also observed a tendency of increase in the activity of motor imagery after training. The hemodynamic activity in all these 14 areas during real movements was either approximately the same or significantly higher than during motor imagery; activity during imagined leg movements was higher than that during imagines arm movements, except for the areas of representation of arms.     

Effects of vibrotactile stimulation on the control of muscle tone and movement facilitation in children with cerebral injury

Afferent signals from the muscle's proprioceptors play important role in the control of muscle tone and in the facilitation of movements. Peripheral afferent pathway enables the restoration of connections with supraspinal structures and so includes mechanism of synaptic inhibition in the performance of normal movement. Different sensory stimuli, as vibrotactile stimulation, excite muscle's proprioceptors which then send sensorimotor information via spinal cord. In this way afferent signals promote cortical control and modulation of movements. The goal of this study is to evaluate the effects of vibrotactile stimulation on the spasticity and motor performance in children with cerebral injury. Subjects included in this study were 13 children who were developing the classification of spastic cerebral palsy. For all children perinatal brain damage was documented by medical reports and neonatal brain ultrasound scan. At the mean age of 3 years and 6 months subject underwent the assessment of motor development by Gross Motor Function Measurement (GMFM-88). Gross Motor Classification System (GMFCS) has been used to classify functions of lower extremities. Therapeutic intervention was conducted once a week during 3 months. All subjects were stimulated with vibrotactile stimuli of 40Hz in duration of 20 minutes in order to reduce spasticity. After the ending of the treatment subjects underwent second assessment of motor performance and the classification of lower extremities functions. The results have shown that there was a significant improvement in motor performance, what has been seen in the facilitation of rotations, better postural trunk stability and head control and in greater selectivity of movements. Further randomized, control trial investigations with bigger sample and included spasm scale are needed to gain better insight in the role of vibrotactile stimulation in the facilitation of normal movements.     

Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses

Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants'' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent “negative” feedback (participants'' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive movements. To summarize, we demonstrated that the use of contingent positive proprioceptive feedback BCI enhanced SMR desynchronization during motor tasks.