Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia (original) (raw)
Related papers
Journal of Biomedical Science and Engineering, 2017
Patients who suffer from a high spinal cord injury have severe motor disabilities in the lower as well as in the upper extremities. Thus they rely on the help of other people in everyday life. Restoring the function of the upper limbs, especially the grasp function can help them to gain some independence. Using EEG-based neuroprosthetics is a way to help tetraplegic people restore different grasp types as well as moving the arm and the elbow. In this work an overview of non-invasive EEG-based methods for restoring the hand and arm function with the use of neuroprosthetics in individuals with high spinal cord injury is given. Since the Graz BCI group is leading in this area of non-invasive research mainly, the work of this group is represented.
Scientific Reports, 2018
Arm and finger paralysis, e.g. due to brain stem stroke, often results in the inability to perform activities of daily living (ADLs) such as eating and drinking. Recently, it was shown that a hybrid electroencephalography/electrooculography (EEG/EOG) brain/neural hand exoskeleton can restore hand function to quadriplegics, but it was unknown whether such control paradigm can be also used for fluent, reliable and safe operation of a semi-autonomous whole-arm exoskeleton restoring ADLs. To test this, seven abled-bodied participants (seven right-handed males, mean age 30 ± 8 years) were instructed to use an EEG/EOG-controlled whole-arm exoskeleton attached to their right arm to perform a drinking task comprising multiple sub-tasks (reaching, grasping, drinking, moving back and releasing a cup). Fluent and reliable control was defined as average 'time to initialize' (TTI) execution of each sub-task below 3 s with successful initializations of at least 75% of sub-tasks within 5 s. During use of the system, no undesired side effects were reported. All participants were able to fluently and reliably control the vision-guided autonomous whole-arm exoskeleton (average TTI 2.12 ± 0.78 s across modalities with 75% successful initializations reached at 1.9 s for EOG and 4.1 s for EEG control) paving the way for restoring ADLs in severe arm and hand paralysis. Arm and hand paralysis due to lesions of the central or peripheral nervous system is the most common reason for long-term disability in the adulthood 1. Particularly high-cervical spinal cord injuries, stroke or plexus brachialis avulsions resulting in a complete loss of arm and finger function have a substantial impact on the ability to perform various activities of daily living (ADLs), e.g. eating and drinking independently 2,3. Over the last years, various upper-limb robotic systems were developed to mobilize the upper limb and fingers, e.g. in the context of rehabilitation therapies 4-7. Other promising robotic approaches to restore ADLs include gaze-based teleprosthetics 8. While these systems were often immobile and designed to be used in rehabilitation facilities, recent advances in systems integration yielded the development of portable robotic arms with grippers 9,10 or lightweight whole-arm 11 or hand exoskeletons 12,13 that can be used in everyday life environments to assist in ADLs. While assistive robotic arms were mainly designed for individuals with complete tetraplegia and
Stroke, 2017
There are few effective therapies to achieve functional recovery from motor-related disabilities affecting the upper limb after stroke. This feasibility study tested whether a powered exoskeleton driven by a brain-computer interface (BCI), using neural activity from the unaffected cortical hemisphere, could affect motor recovery in chronic hemiparetic stroke survivors. This novel system was designed and configured for a home-based setting to test the feasibility of BCI-driven neurorehabilitation in outpatient environments. Ten chronic hemiparetic stroke survivors with moderate-to-severe upper-limb motor impairment (mean Action Research Arm Test=13.4) used a powered exoskeleton that opened and closed the affected hand using spectral power from electroencephalographic signals from the unaffected hemisphere associated with imagined hand movements of the paretic limb. Patients used the system at home for 12 weeks. Motor function was evaluated before, during, and after the treatment. Acr...
Towards a brain computer interface driven exoskeleton for upper extremity rehabilitation
5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 2014
Stroke impairs individuals to perform activities of daily living. Intense rehabilitation programs offer hope for recovery, but are labor intensive and costly. Robotic rehabilitation technology plays a key role to solve such a problem. Current robotic systems along with brain computer interface (BCI) allow patients to participate in rehabilitation exercises, which require their own mental inputs. Studies have shown such active rehabilitation exercise can induce neuroplasticity and help towards recovery. However, even though BCI-driven robotic systems do exist, they are large complex systems and expensive to set up. These drawbacks limit a wide distribution of these technologies. Currently, the BCI robotic systems only used in large hospitals or research settings, not community level facilities. To facilitate the accessibility of stroke patients to such technologies, we propose a novel BCI-driven exoskeleton rehabilitation system. The exoskeleton has four degrees of freedom (DOF) for assisting the movement of the upper extremities. It is integrated with an affordable and wireless EEG headset for enabling the patients to control the movement of the exoskeleton with their brain activity. The developed exoskeleton is portable and easy to set up. A sequential control scheme is proposed to allow the user to control one movement at a time. An experiment was designed to assess if a healthy individual was able to control the movement of the exoskeleton correctly under a predefined sequence. One volunteer participated in the exploratory study and the volunteer was able to correctly control the exoskeleton in each step.
Restoring cortical control of functional movement in a human with quadriplegia
Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements 1–11. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles 12,13. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5–C6) to the seventh cervical to first thoracic (C7–T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis. The study participant was a 24-year-old male with stable, non-spastic C5/C6 quadriplegia from cervical spinal cord injury (SCI) sustained in a diving accident 4 years previously. He underwent implantation of a Utah microelectrode array (Blackrock Microsystems) in his left primary motor cortex. As shown in Fig. 1a, the hand area of the primary motor cortex was identified preopera-tively by performing functional magnetic resonance imaging (fMRI) while the participant attempted to mirror videos of hand movements. The final array implantation location was chosen during surgery, targeting the hand area while avoiding sulci and injury to large cor-tical vessels. The implant location was confirmed by co-registration of postoperative computed tomography imaging with preoperative fMRI (Fig. 1a) and is consistent with the 'knob' region of the primary motor cortex 5,14. The participant attended up to three sessions weekly for 15 months after implantation to use the neural bypass system (NBS). In each session, he was trained to utilize his motor cortical neuronal activity to control a custom-built high-resolution neuromuscular electrical stim-ulator (NMES). The NMES delivered electrical stimulation to his para-lysed right forearm muscles using an array of 130 electrodes embedded in a custom-made flexible sleeve wrapped around the arm (Fig. 1b). The participant was positioned in front of a computer monitor, and a stereo camera was placed overhead to track and record hand movements (Fig. 1c). During the study, up to 50 single units could be isolated in a given session. Near the end of the study, 33 units could be isolated with a mean signal-to-noise ratio of 3.05 ± 0.81 (mean ± s.d.) including units that responded to imagined or performed wrist movements (Fig. 1d). (See Extended Data Fig. 1 for additional unit activity.) Wavelet decomposition of the multiunit activity recorded from 96 microelec-trodes was used to produce mean wavelet power (MWP) features for decoding (Fig. 1e) (see Methods). To assess the ability of the NBS to restore individual movements, we focused on six wrist and hand movements that were all impaired by the participant's injury and reactivated by stimulation of forearm muscles (see Supplementary Video 1 showing the participant attempting the six movements without the use of the NBS). Each session began with recalibration of the NMES to map electrode stimulation patterns to evoked movements (see Methods). Cortical activity was continuously decoded as the participant attempted the six selected movements inter-leaved with rest periods, as cued by an animated virtual hand on the computer monitor. Changes in the MWP patterns for each movement were captured during the test. These patterns were then processed by multiple simultaneous neural decoders, one for each trained movement , using a nonlinear kernel method with a non-smooth support vector machine 15. The decoders were trained in successive blocks and, once trained, their outputs were continuously compared using the highest decoder output to control the corresponding NMES movement stimulation pattern (see Methods). During movement, a large portion of the stimulation artefact that occurred during a stimulation pulse was removed, but stimulation effects still remained (see Methods). To test the system's performance, test blocks were performed consisting of five trials of each of the six trained movements presented in random order. At the beginning of each trial, the participant was visually cued by the virtual hand demonstrating a target movement. Representative data, including modulation of MWP (before and after stimulation begins), decoder outputs, and corresponding movement state are shown in Fig. 2. MWP increases by a factor of 2–8 after stimulation begins because of residual stimulation artefact (see Methods and Extended Data Fig. 2). However, since the neural decoders were trained with MWP from before and during stimulation, they were able
2021
The project is aimed at investigating efficacy of a BCI-controlled palm exoskeleton as a tool for motor function recovery in post-stroke patients. The idea of using the system is grounded on vast amount of data supported by physiologic literature and our own findings in healthy subjects, suggesting that kinesthetic motor imagery (MI) requires activation of the brain areas involved in motion planning, execution and control. Thus, the common idea of using a MI-based BCI for neurorehabilitation is to reinforce motor imagery of intention to move with visual, proprioceptive and\or tactile feedback. Results of a four-year multi-center randomized controlled study of post-stroke motor rehabilitation procedure with BCI-controlled hand exoskeleton complex are presented. The study has the largest number of participants so far. Statistical analysis of different clinical scales used to assess motor function recovery show that incorporating the BCI+exoskeleton procedure into rehabilitation significantly improves its outcome. The analysis also revealed non-monotonical dependency of motor function recovery rate on initial motor and sensory function status, as well as on age, and BCI control accuracy. Hopefully, the reported data combined with the results obtained by other groups in the world, would provide solid evidence supporting inclusion of the BCI-based systems into rehabilitation practice.
Sensors
Millions of individuals suffer from upper extremity paralysis caused by neurological disorders including stroke, traumatic brain injury, or spinal cord injury. Robotic hand exoskeletons can substitute the missing motor control and help restore the functions in daily operations. However, most of the hand exoskeletons are bulky, stationary, and cumbersome to use. We have modified a recent existing design (Tenoexo) to prototype a motorized, lightweight, fully wearable rehabilitative hand exoskeleton by combining rigid parts with a soft mechanism capable of producing various grasps needed for the execution of daily tasks. Mechanical evaluation of our exoskeleton showed that it can produce fingertip force up to 8 N and can cover 91.5° of range of motion in just 3 s. We further tested the performance of the developed robotic exoskeleton in two quadriplegics with chronic hand paralysis and observed immediate success on independent grasping of different daily objects. The results suggested ...
Noninvasive brain-computer interface driven hand orthosis
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Neurological conditions, such as stroke, can leave the affected individual with hand motor impairment despite intensive treatments. Novel technologies, such as brain-computer interface (BCI), may be able to restore or augment impaired motor behaviors by engaging relevant cortical areas. Here, we developed and tested an electroencephalogram (EEG) based BCI system for control of hand orthosis. An able-bodied subject performed contralateral hand grasping to achieve continuous online control of the hand orthosis, suggesting that the integration of a noninvasive BCI with a hand orthosis is feasible. The adoption of this technology to stroke survivors may provide a novel neurorehabilitation therapy for hand motor impairment in this population. 978-1-4244-4122-8/11/$26.00 ©2011 IEEE 5786 33rd Annual International Conference of the IEEE EMBS
Wearable Technologies
We have developed a one-of-a-kind hand exoskeleton, called Maestro, which can power finger movements of those surviving severe disabilities to complete daily tasks using compliant joints. In this paper, we present results from an electromyography (EMG) control strategy conducted with spinal cord injury (SCI) patients (C5, C6, and C7) in which the subjects completed daily tasks controlling Maestro with EMG signals from their forearm muscles. With its compliant actuation and its degrees of freedom that match the natural finger movements, Maestro is capable of helping the subjects grasp and manipulate a variety of daily objects (more than 15 from a standardized set). To generate control commands for Maestro, an artificial neural network algorithm was implemented along with a probabilistic control approach to classify and deliver four hand poses robustly with three EMG signals measured from the forearm and palm. Increase in the scores of a standardized test, called the Sollerman hand fu...