Neuroprosthetics of the upper extremity--clinical application in spinal cord injury and challenges for the future

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Grasping is one of the most indispensable functions of humans. Decoding reach-and-grasp actions from electroencephalograms (EEGs) is of great significance for the realization of intuitive and natural neuroprosthesis control, and the recovery or reconstruction of hand functions of patients with motor disorders. In this paper, we investigated decoding five different reach-and-grasp movements closely related to daily life using movement-related cortical potentials (MRCPs). In the experiment, nine healthy subjects were asked to naturally execute five different reach-and-grasp movements on the designed experimental platform, namely palmar, pinch, push, twist, and plug grasp. A total of 480 trials per subject (80 trials per condition) were recorded. The MRCPs amplitude from low-frequency (0.3-3 Hz) EEG signals were used as decoding features for further offline analysis. Average binary classification accuracy for grasping vs. the no-movement condition peaked at 75.06 ± 6.8%. Peak average accuracy for grasping vs. grasping conditions of 64.95 ± 7.4% could be reached. Grand average peak accuracy of multiclassification for five grasping conditions reached 36.7 ± 6.8% at 1.45 s after the movement onset. The analysis of MRCPs indicated that all the grasping conditions are more pronounced than the no-movement condition, and there are also significant differences between the grasping conditions. These findings clearly proved the feasibility of decoding multiple reach-and-grasp actions from noninvasive EEG signals. This work is significant for the natural and intuitive BCI application, particularly for neuroprosthesis control or developing an active human-machine interaction system, such as rehabilitation robot.

Applying intuitive EEG-controlled grasp neuroprostheses in individuals with spinal cord injury: Preliminary results from the MoreGrasp clinical feasibility study

The aim of the MoreGrasp project is to develop a non-invasive, multimodal user interface including a brain-computer interface (BCI) for control of a grasp neuroprostheses in individuals with high spinal cord injury (SCI). The first results of the ongoing MoreGrasp clinical feasibility study involving end users with SCI are presented. This includes BCI screening sessions, in which we investigate the electroencephalography (EEG) patterns associated with single, natural movements of the upper limb. These patterns will later be used to control the neuroprosthesis. Additionally, the MoreGrasp grasp neuroprosthesis consisting of electrode arrays embedded in an individualized textile forearm sleeve is presented. The general feasibility of this electrode array in terms of corrections of misalignments during donning is shown together with the functional results in end users of the electrode forearm sleeve.

Decoding natural reach-and-grasp actions from human EEG

OBJECTIVE

Despite the high number of degrees of freedom of the human hand, most actions of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study we attempt to discriminate these three different executed reach-and-grasp actions utilizing their EEG neural correlates.

APPROACH

In a cue-guided experiment, 15 healthy individuals were asked to perform these actions using daily life objects. We recorded 72 trials for each reach-and-grasp condition and from a no-movement condition.

MAIN RESULTS

Using low-frequency time domain features from 0.3 to 3 Hz, we achieved binary classification accuracies of 72.4%, STD  ±  5.8% between grasp types, for grasps versus no-movement condition peak performances of 93.5%, STD  ±  4.6% could be reached. In an offline multiclass classification scenario which incorporated not only all reach-and-grasp actions but also the no-movement condition, the highest performance could be reached using a window of 1000 ms for feature extraction. Classification performance peaked at 65.9%, STD  ±  8.1%. Underlying neural correlates of the reach-and-grasp actions, investigated over the primary motor cortex, showed significant differences starting from approximately 800 ms to 1200 ms after the movement onset which is also the same time frame where classification performance reached its maximum.

SIGNIFICANCE

We could show that it is possible to discriminate three executed reach-and-grasp actions prominent in people's everyday use from non-invasive EEG. Underlying neural correlates showed significant differences between all tested conditions. These findings will eventually contribute to our attempt of controlling a neuroprosthesis in a natural and intuitive way, which could ultimately benefit motor impaired end users in their daily life actions.

Feasibility of predicting improvements in motor function following SCI using the SCAR outcome measure: a retrospective study

STUDY DESIGN

A retrospective study.

OBJECTIVES

To assess improvement in volitional motor function after SCI, using The Spinal Cord Ability Ruler (SCAR) as a metric and investigate participant characteristics and recovery of motor functioning.

SETTING

A highly-specialized SCI rehabilitation unit (Spinal Cord Injury Centre of Western Denmark, SCIWDK).

METHODS

Retrospectively, data on all SCI patients admitted to SCIWDK between 1 January 1997 and 1 November 2018 were extracted from a database. The SCAR score (range: 0-100) was calculated by combining items from ISNCSCI and SCIM.

RESULTS

Mean (95%CI) improvement in volitional motor function was of 17.2 (CI: 14.5-19.9) equal to an improvement of 43% from baseline after median 155 days in-hospital rehabilitation. Individuals with tetraplegia exerted larger improvement (mean difference of 8.9 (CI: 3.6-14.2) points) as compared to paraplegia. Male gender predicted better improvement (p < 0.03), as did no need for mechanical ventilation with a gain of 8.5 (CI: 1.8-15.3) points as compared to those in need.

CONCLUSIONS

Overall mean improvement of 43% in volitional motor function was found in 84 in-hospitalized patients using SCAR as a metric at a highly-specialized SCI unit. Following factors; level-of-injury, gender, age, need of ventilation support predicted improvement in volitional motor function after a rehabilitation period. Results should be cautiously interpreted as a majority of hospitalized patients did not fulfill criteria for SCAR scoring. Prospectively designed studies with better internal validation and external validations are needed to confirm these findings.

Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury

We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).

Upper limb movements can be decoded from the time-domain of low-frequency EEG

How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.

Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury

Brain computer interfaces (BCIs) are devices that measure brain activities and translate them into control signals used for a variety of applications. Among them are systems for communication, environmental control, neuroprostheses, exoskeletons, or restorative therapies. Over the last years the technology of BCIs has reached a level of matureness allowing them to be used not only in research experiments supervised by scientists, but also in clinical routine with patients with neurological impairments supervised by clinical personnel or caregivers. However, clinicians and patients face many challenges in the application of BCIs. This particularly applies to high spinal cord injured patients, in whom artificial ventilation, autonomic dysfunctions, neuropathic pain, or the inability to achieve a sufficient level of control during a short-term training may limit the successful use of a BCI. Additionally, spasmolytic medication and the acute stress reaction with associated episodes of depression may have a negative influence on the modulation of brain waves and therefore the ability to concentrate over an extended period of time. Although BCIs seem to be a promising assistive technology for individuals with high spinal cord injury systematic investigations are highly needed to obtain realistic estimates of the percentage of users that for any reason may not be able to operate a BCI in a clinical setting.

Feedback control of arm movements using Neuro-Muscular Electrical Stimulation (NMES) combined with a lockable, passive exoskeleton for gravity compensation

Within the European project MUNDUS, an assistive framework was developed for the support of arm and hand functions during daily life activities in severely impaired people. This contribution aims at designing a feedback control system for Neuro-Muscular Electrical Stimulation (NMES) to enable reaching functions in people with no residual voluntary control of the arm and shoulder due to high level spinal cord injury. NMES is applied to the deltoids and the biceps muscles and integrated with a three degrees of freedom (DoFs) passive exoskeleton, which partially compensates gravitational forces and allows to lock each DOF. The user is able to choose the target hand position and to trigger actions using an eyetracker system. The target position is selected by using the eyetracker and determined by a marker-based tracking system using Microsoft Kinect. A central controller, i.e., a finite state machine, issues a sequence of basic movement commands to the real-time arm controller. The NMES control algorithm sequentially controls each joint angle while locking the other DoFs. Daily activities, such as drinking, brushing hair, pushing an alarm button, etc., can be supported by the system. The robust and easily tunable control approach was evaluated with five healthy subjects during a drinking task. Subjects were asked to remain passive and to allow NMES to induce the movements. In all of them, the controller was able to perform the task, and a mean hand positioning error of less than five centimeters was achieved. The average total time duration for moving the hand from a rest position to a drinking cup, for moving the cup to the mouth and back, and for finally returning the arm to the rest position was 71 s.

Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals

The extent to which humans can interact with machines significantly enhanced through inclusion of speech, gestures, and eye movements. However, these communication channels depend on a functional motor system. As many people suffer from severe damage of the motor system resulting in paralysis and inability to communicate, the development of brain-machine interfaces (BMI) that translate electric or metabolic brain activity into control signals of external devices promises to overcome this dependence. People with complete paralysis can learn to use their brain waves to control prosthetic devices or exoskeletons. However, information transfer rates of currently available noninvasive BMI systems are still very limited and do not allow versatile control and interaction with assistive machines. Thus, using brain waves in combination with other biosignals might significantly enhance the ability of people with a compromised motor system to interact with assistive machines. Here, we give an overview of the current state of assistive, noninvasive BMI research and propose to integrate brain waves and other biosignals for improved control and applicability of assistive machines in paralysis. Beside introducing an example of such a system, potential future developments are being discussed.

Neural interfaces for the brain and spinal cord--restoring motor function

Regaining motor function is of high priority to patients with spinal cord injury (SCI). A variety of electronic devices that interface with the brain or spinal cord, which have applications in neural prosthetics and neurorehabilitation, are in development. Owing to our advancing understanding of activity-dependent synaptic plasticity, new technologies to monitor, decode and manipulate neural activity are being translated to patient populations, and have demonstrated clinical efficacy. Brain-machine interfaces that decode motor intentions from cortical signals are enabling patient-driven control of assistive devices such as computers and robotic prostheses, whereas electrical stimulation of the spinal cord and muscles can aid in retraining of motor circuits and improve residual capabilities in patients with SCI. Next-generation interfaces that combine recording and stimulating capabilities in so-called closed-loop devices will further extend the potential for neuroelectronic augmentation of injured motor circuits. Emerging evidence suggests that integration of closed-loop interfaces into intentional motor behaviours has therapeutic benefits that outlast the use of these devices as prostheses. In this Review, we summarize this evidence and propose that several known plasticity mechanisms, operating in a complementary manner, might underlie the therapeutic effects that are achieved by closing the loop between electronic devices and the nervous system.

Early intensive hand rehabilitation after spinal cord injury ("Hands On"): a protocol for a randomised controlled trial

BACKGROUND

Loss of hand function is one of the most devastating consequences of spinal cord injury. Intensive hand training provided on an instrumented exercise workstation in conjunction with functional electrical stimulation may enhance neural recovery and hand function. The aim of this trial is to compare usual care with an 8-week program of intensive hand training and functional electrical stimulation.

METHODS/DESIGN

A multicentre randomised controlled trial will be undertaken. Seventy-eight participants with recent tetraplegia (C2 to T1 motor complete or incomplete) undergoing inpatient rehabilitation will be recruited from seven spinal cord injury units in Australia and New Zealand and will be randomised to a control or experimental group. Control participants will receive usual care. Experimental participants will receive usual care and an 8-week program of intensive unilateral hand training using an instrumented exercise workstation and functional electrical stimulation. Participants will drive the functional electrical stimulation of their target hands via a behind-the-ear bluetooth device, which is sensitive to tooth clicks. The bluetooth device will enable the use of various manipulanda to practice functional activities embedded within computer-based games and activities. Training will be provided for one hour, 5 days per week, during the 8-week intervention period. The primary outcome is the Action Research Arm Test. Secondary outcomes include measurements of strength, sensation, function, quality of life and cost effectiveness. All outcomes will be taken at baseline, 8 weeks, 6 months and 12 months by assessors blinded to group allocation. Recruitment commenced in December 2009.

DISCUSSION

The results of this trial will determine the effectiveness of an 8-week program of intensive hand training with functional electrical stimulation.

TRIAL REGISTRATION

NCT01086930 (12th March 2010)ACTRN12609000695202 (12th August 2009).

A real-time, 3-D musculoskeletal model for dynamic simulation of arm movements

Neuroprostheses can be used to restore movement of the upper limb in individuals with high-level spinal cord injury. Development and evaluation of command and control schemes for such devices typically require real-time, "patient-in-the-loop" experimentation. A real-time, 3-D, musculoskeletal model of the upper limb has been developed for use in a simulation environment to allow such testing to be carried out noninvasively. The model provides real-time feedback of human arm dynamics that can be displayed to the user in a virtual reality environment. The model has a 3-DOF glenohumeral joint as well as elbow flexion/extension and pronation/supination and contains 22 muscles of the shoulder and elbow divided into multiple elements. The model is able to run in real time on modest desktop hardware and demonstrates that a large-scale, 3-D model can be made to run in real time. This is a prerequisite for a real-time, whole-arm model that will form part of a dynamic arm simulator for use in the development, testing, and user training of neural prosthesis systems.