Decoding hand movement velocity from electroencephalogram signals during a drawing task

<p>Abstract</p> <p>Background</p> <p>Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.</p> <p>Methods</p> <p>Here we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.</p> <p>Results</p> <p>The average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.</p> <p>Conclusions</p> <p>These results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.</p

A hybrid brain-computer interface based on motor intention and visual working memory

Non-invasive electroencephalography (EEG) based brain-computer interface (BCI) is able to provide alternative means for people with disabilities to communicate with and control over external assistive devices. A hybrid BCI is designed and developed for following two types of system (control and monitor). Our first goal is to create a signal decoding strategy that allows people with limited motor control to have more command over potential prosthetic devices. Eight healthy subjects were recruited to perform visual cues directed reaching tasks. Eye and motion artifacts were identified and removed to ensure that the subjects\u27 visual fixation to the target locations would have little or no impact on the final result. We applied a Fisher Linear Discriminate (FLD) analysis for single-trial classification of the EEG to decode the intended arm movement in the left, right, and forward directions (before the onsets of actual movements). The mean EEG signal amplitude near the PPC region 271-310 ms after visual stimulation was found to be the dominant feature for best classification results. A signal scaling factor developed was found to improve the classification accuracy from 60.11% to 93.91% in the two-class (left versus right) scenario. This result demonstrated great promises for BCI neuroprosthetics applications, as motor intention decoding can be served as a prelude to the classification of imagined motor movement to assist in motor disable rehabilitation, such as prosthetic limb or wheelchair control. The second goal is to develop the adaptive training for patients with low visual working memory (VWM) capacity to improve cognitive abilities and healthy individuals who seek to enhance their intellectual performance. VWM plays a critical role in preserving and processing information. It is associated with attention, perception and reasoning, and its capacity can be used as a predictor of cognitive abilities. Recent evidence has suggested that with training, one can enhance the VWM capacity and attention over time. Not only can these studies reveal the characteristics of VWM load and the influences of training, they may also provide effective rehabilitative means for patients with low VWM capacity. However, few studies have investigated VWM over a long period of time, beyond 5-weeks. In this study, a combined behavioral approach and EEG was used to investigate VWM load, gain, and transfer. The results reveal that VWM capacity is directly correlated to the reaction time and contralateral delay amplitude (CDA). The approximate magic number 4 was observed through the event-related potentials (ERPs) waveforms, where the average capacity is 2.8-item from 15 participants. In addition, the findings indicate that VWM capacity can be improved through adaptive training. Furthermore, after training exercises, participants from the training group are able to improve their performance accuracies dramatically compared to the control group. Adaptive training gains on non-trained tasks can also be observed at 12 weeks after training. Therefore, we conclude that all participants can benefit from training gains, and augmented VWM capacity can be sustained over a long period of time. Our results suggest that this form of training can significantly improve cognitive function and may be useful for enhancing the user performance on neuroprosthetics device

Neural Prosthetic Advancement: identification of circuitry in the Posterior Parietal Cortex

There are limited options for rehabilitation following an established Spinal Cord Injury (SCI) resulting in paralysis. For most of the individuals affected, SCI means a lifetime of confinement to a wheelchair and overall reduced independence. Brain-Computer and Brain-Machine Interface (BCI and BMI) techniques may be of aid when used for assistive purposes. However, these techniques are still far from being implemented in daily rehabilitative practice. Existing literature on the use of BCI and BMI techniques in SCI is limited and focuses on the extraction of motor control signals from the primary motor cortex (M1). However, evidence suggests that in long-term established SCI the functional activation of motor and premotor areas tends to decrease over time. In the present project, we explore the possibility of successful implementation of assistive BCI and BMI systems using posterior parietal areas as extraction sites of motor control activity. Firstly, we will investigate the representation of space in the posterior parietal cortex (PPC) and whether evidence of body-centered reference frames can be found in healthy individuals. We will then proceed to extract information regarding the residual level of motor imagery activity in individuals suffering from long-term and high-level SCI. Our aim is to ascertain whether functional activation of motor and posterior areas is comparable to that of matched controls. Finally, we will present work that was done in collaboration with the Netherlands Organisation for Applied Scientific Research that can offer an example of successful application of a BCI technique for rehabilitation purposes

Mental imagery of whole-body motion along the sagittal-anteroposterior axis

Whole-body motor imagery is conceptualised as a mental symbolisation directly and indirectly associated with neural oscillations similar to whole-body motor execution. Motor and somatosensory activity, including vestibular activity, is a typical corticocortical substrate of body motion. Yet, it is not clear how this neural substrate is organised when participants are instructed to imagine moving their body forward or backward along the sagittal-anteroposterior axis. It is the aim of the current study to identify the fingerprint of the neural substrate by recording the cortical activity of 39 participants via a 32 electroencephalography (EEG) device. The participants were instructed to imagine moving their body forward or backward from a first-person perspective. Principal Component Analysis (i.e. PCA) applied to the neural activity of whole-body motor imagery revealed neural interconnections mirroring between forward and backward conditions: beta pre-motor and motor oscillations in the left and right hemisphere overshadowed beta parietal oscillations in forward condition, and beta parietal oscillations in the left and right hemisphere overshadowed beta pre-motor and motor oscillations in backward condition. Although functional significance needs to be discerned, beta pre-motor, motor and somatosensory oscillations might represent specific settings within the corticocortical network and provide meaningful information regarding the neural dynamics of continuous whole-body motion. It was concluded that the evoked multimodal fronto-parietal neural activity would correspond to the neural activity that could be expected if the participants were physically enacting movement of the whole-body in sagittal-anteroposterior plane as they would in their everyday environment

Toward More Versatile and Intuitive Cortical Brain–Machine Interfaces

Brain–machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain–machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics

Single Trial Decoding of Movement Intentions Using Functional Ultrasound Neuroimaging

Brain-machine interfaces (BMI) are powerful devices for restoring function to people living with paralysis. Leveraging significant advances in neurorecording technology, computational power, and understanding of the underlying neural signals, BMI have enabled severely paralyzed patients to control external devices, such as computers and robotic limbs. However, high-performance BMI currently require highly invasive recording techniques, and are thus only available to niche populations. Here, we show that a minimally invasive neuroimaging approach based on functional ultrasound (fUS) imaging can be used to detect and decode movement intention signals usable for BMI. We trained non-human primates to perform memory-guided movements while using epidural fUS imaging to record changes in cerebral blood volume from the posterior parietal cortex, a brain area important for spatial perception, multisensory integration, and movement planning. Using hemodynamic signals acquired during movement planning, we classified left-cued vs. right-cued movements, establishing the feasibility of ultrasonic BMI. These results demonstrate the ability of fUS-based neural interfaces to take advantage of the excellent spatiotemporal resolution, sensitivity, and field of view of ultrasound without breaching the dura or physically penetrating brain tissue

Noninvasive neural decoding of overt and covert hand movement

It is generally assumed that the signal-to-noise ratio and information content of neural data acquired noninvasively via magnetoencephalography (MEG) or scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multi-joint movements of the upper limb. If valid, this assumption could severely limit the practical usage of noninvasive signals in brain-computer interface (BCI) systems aimed at continuous complex control of arm-like prostheses for movement impaired persons. Fortunately this dissertation research casts doubt on the veracity of this assumption by extracting continuous hand kinematics from MEG signals collected during a 2D center-out drawing task (Bradberry et al. 2009, NeuroImage, 47:1691-700) and from EEG signals collected during a 3D center-out reaching task (Bradberry et al. 2010, Journal of Neuroscience, 30:3432-7). In both studies, multiple regression was performed to find a matrix that mapped past and current neural data from multiple sensors to current hand kinematic data (velocity). A novel method was subsequently devised that incorporated the weights of the mapping matrix and the standardized low resolution electromagnetic tomography (sLORETA) software to reveal that the brain sources that encoded hand kinematics in the MEG and EEG studies were corroborated by more traditional studies that required averaging across trials and/or subjects. Encouraged by the favorable results of these off-line decoding studies, a BCI system was developed for on-line decoding of covert movement intentions that provided users with real-time visual feedback of the decoder output. Users were asked to use only their thoughts to move a cursor to acquire one of four targets on a computer screen. With only one training session, subjects were able to accomplish this task. The promising results of this dissertation research significantly advance the state-of-the-art in noninvasive BCI systems

Human Ipsilateral Motor Physiology and Neuroprosthetic Applications in Chronic Stroke

Improving the recovery of lost motor function in hemiplegic chronic stroke survivors is a critical need to improve the lives of these patients. Over the last several decades, neuroprosthetic systems have emerged as novel tools with the potential to restore function in a variety of patient populations. While traditional neuroprosthetics have focused on using neural activity contralateral to a moving limb for device control, an alternative control signal may be necessary to develop brain-computer interface (BCI) systems in stroke survivors that suffer damage to the cortical hemisphere contralateral to the affected limb. While movement-related neural activity also occurs in the hemisphere ipsilateral to a moving limb, it is uncertain if these signals can be used within BCI systems. This dissertation examines the motor activity ipsilateral to a moving limb and the potential use of these signals for neuroprosthetic applications in chronic stroke survivors. Patients performed three-dimensional (3D) reaching movements with the arm ipsilateral to an electrocorticography (ECoG) array in order to assess the extent of kinematic information that can be decoded from the cortex ipsilateral to a moving limb. Additionally, patients performed the same task with the arm contralateral to the same ECoG arrays, allowing us to compare the neural representations of contralateral and ipsilateral limb movements. While spectral power changes related to ipsilateral arm movements begin later and are lower in amplitude than power changes related to contralateral arm movements, 3D kinematics from both contralateral and ipsilateral arm trajectories can be decoded with similar accuracies. The ability to decode movement kinematics from the ipsilateral cortical hemisphere demonstrates the potential to use these signals within BCI applications for controlling multiple degrees of freedom. Next we examined the relationship between electrode invasiveness and signal quality. The ability to decode movement kinematics from neural activity was significantly decreased in simulated electroencephalography (EEG) signals relative to ECoG signals, indicating that invasive signals would be necessary to implement BCI systems with multiple degrees of freedom. For ECoG signals, the human dura also causes a significant decrease in signal quality when electrodes with small spatial sizes are used. This tradeoff between signal quality and electrode invasiveness should therefore be taken into account when designing ECoG BCI systems. Finally, chronic stroke survivors used activity associated with affected hand motor intentions, recorded from their unaffected hemisphere using EEG, to control simple BCI systems. This demonstrates that motor signals from the ipsilateral hemisphere are viable for BCI applications, not only in motor-intact patients, but also in chronic stroke survivors. Taken together, these experiments provide initial demonstrations that it is possible to develop BCI systems using the unaffected hemisphere in stroke survivors with multiple degrees of freedom. Further development of these BCI systems may eventually lead to improving function for a significant population of patients

The Role of the Dorsal Premotor and Superior Parietal Cortices in Decoupled Visuomotor Transformations

In order to successfully interact with objects located within our environment, the brain must be capable of combining visual information with the appropriate felt limb position (i.e. proprioception) in order compute an appropriate coordinated muscle plan for accurate motor control. Eye-hand coordination is essential to our independence as a species and relies heavily on the reciprocally-connected regions of the parieto-frontal reach network. The dorsal premotor cortex (PMd) and the superior parietal lobule (SPL) remain prime candidates within this network for controlling the transformations required during visually-guided reaching movements. Our brains are primed to reach directly towards a viewed object, a situation that has been termed a “standard” or coupled reach. Such direct eye-hand coordination is common across species and is crucial for basic survival. Humans, however, have developed the capacity for tool-use and thus have learned to interact indirectly with an object. In such “non-standard” or decoupled situations, the directions of gaze and arm movement have been spatially decoupled and rely on both the implementation of a cognitive rule and on online feedback of the decoupled limb. The studies included within this dissertation were designed to further characterize the role of PMd and SPL during situations in which when a reach requires a spatial transformation between the actions of the eyes and the hand. More specifically, we were interested in examining whether regions within PMd (PMdr, PMdc) and SPL (PEc, MIP) responded differently during coupled versus decoupled visuomotor transformations. To address the relative contribution of these various cortical regions during decoupled reaching movements, we trained two female rhesus macaques on both coupled and decoupled visually-guided reaching tasks. We recorded the neural activity (single units and local field potentials) within each region while the animals performed each condition. We found that two separate networks emerged each contributing in a distinct ways to the performance of coupled versus decoupled eye-hand reaches. While PMdr and PEc showed enhanced activity during decoupled reach conditions, PMdc and MIP were more enhanced during coupled reaches. Taken together, these data presented here provide further evidence for the existence of alternate task-dependent neural pathways for visuomotor integration