Posts Tagged BCI
[Short Review] NCyborg Project – A new stroke rehabilitation pattern based on brain computer interface
NCyborg Project, a new stroke rehabilitation pattern based on brain computer interface (BCI) and brain-inspired intelligent robots, is set up by Tongji Hospital and BrainCo. We will briefly introduce this project in this paper.
NCyborg Project, a new stroke rehabilitation pattern based on brain computer interface (BCI) and brain-inspired intelligent robots, is set up by Tongji Hospital and BrainCo.
In China, stroke is the leading cause of death and disability in the adult population, with 1.96 million deaths each year, and 75% of the surviving patients will lose the ability to move independently. Most patients with physical disabilities cannot take care of themselves, and are difficult to carry out daily activities independently, therefore, requiring long-term functional exercises and rehabilitation.
Traditional stroke rehabilitation equipment only allows patients to passively follow the movements of the equipment. Hence, the rehabilitation effect is poor, and the patient’s willingness to train is also low. It can only be utilized as an auxiliary means for rehabilitation by the practitioners, thus increasing the cost of the rehabilitation treatment.
The department of Neurology in Tongji Hospital, affiliated to Tongji Medical College, Huazhong University of Science and Technologyis in a leading position in China in terms of scientific research and clinical strength. It undertakes 54 national, provincial and municipal research projects. The current research projects include 2 Major Special Projects of the Ministry of Science and Technology, 5 National Key R&D Programs, 1 Major Research Cultivation Program of the National Natural Science Foundation of China, 28 General Programs and Distinguished Young Scholars Programs of National Natural Science Foundation of China, and 13 provincial and municipal research projects. In the past three years, Tongji Hospital has obtained 5 various scientific research achievement awards and 19 Chinese invention patents. It has significantly influenced the clinical and basic research of neurology in China and overseas and has had a profound impact on the related textbooks, books and clinical guidelines. Meanwhile, the number of outpatient clinics in the department of Neurology reached more than 210,000 cases in 2020, the majority of which were stroke patients.
Zhejiang BrainCo, Ltd. (www.brainco.cn), incubated by Harvard Innovation Lab (www.innovationlabs.harvard.edu), is in a leading position in brain-computer interfaces, which is known as the next generation of artificial intelligence technology. BrainCo has more than 100 core patents in the field of brain-computer interface. In the “2019 Artificial Intelligence Development Whitepaper”1 released by the China Academy of Science, BrainCo, as the only brain-computer interface company on the list, was selected as the World’s Top 20 Artificial Intelligence Companies. Its intelligent BrainRobotics Prosthetic Hand based on the brain-computer interface technology was named the “Best Invention of 2019”2 by Time Magazine in the United States.
In the NCyborg Project, Tongji Hospital and BrainCo will cooperate to use brain-computer interface technology and brain-inspired intelligent robot technology to realize the rehabilitation process driven by the patients’ initiative and improve the treatment effect of stroke survivors, see Fig. 1.
The cooperative research will be carried out from the following three aspects:
(1) An algorithm for analyzing the movement intention of stroke patients based on brain-computer interface technology. Stroke survivors often experience damage to the central nervous system after a stroke, so that their movement intention cannot be effectively transmitted to the peripheral nervous system and muscles. Through the brain-computer interface technology, the patients’ active intention can be obtained from the damaged nerve tissue of the patients, thereby bypassing the damaged nerve-muscle pathway and realizing the transmission of the movement intention.
(2) Motion control strategy of rehabilitation robot based on brain-inspired motion perception. The purpose is to allow rehabilitation robots to adapt to complex and changeable activities of daily life (ADLs), to achieve the dual role of function rehabilitation and aids to daily living, to endow rehabilitation robots with the capability of brain-inspired motion perception, to realize the perception of the surrounding environment information, and to reduce users’ attention burden.
(3) The mechanism of stroke rehabilitation of brain-inspired intelligent robots. Neuroplasticity and motion function reorganization of brain tissue are the neurological principles of stroke rehabilitation. But the rehabilitation involves not only the recovery of the nervous system, but also the rehabilitation of the blood circulatory system and the muscular system. Hence, we need to study the interactive between neuromuscular and perivascular systems from micro-scale, macro-scale and meso-scale perspectives.
In a word, NCyborg Project aims to develop an easy-to-use, reliable and affordable stroke rehabilitation robot to improve the rehabilitation effect of stroke survivors, speed up the rehabilitation process, and reduce the costs. The robot will first start with hand rehabilitation and is expected to realize the recognition of no less than eight hand movement intentions with the recognition accuracy of ≥ 90% and the response time ≤ 300 ms. Additionally, we are looking forward to, within five years, letting millions of stroke patients use this product with their lives improved after stoke.
In NCyborg Project, N means Neural and Cyborg means a system of biological and technical mixed type. In fictional stories, Cyborg is often claimed as icon that is enhanced mentally and/or physically over and above the “norm” with technology. In the real word, we believe that NCyborg Project based on brain computer interface and brain-inspired intelligent robots will set up a brand new stroke rehabilitation pattern which could qualitatively improve the treatment effect of stroke survivors.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
1 Online access website: https://bdk.ucas.ac.cn/index.php/xyxw/2780-20190113.Google Scholar
A brand new stroke rehabilitation pattern that could improve the treatment effect of stroke survivors.
A stroke occurs when the part of the brain loses its blood supply and stops working. Stroke is the leading cause of death and disability in the adult population. 75% of the surviving patients will lose the ability to move independently.
Traditional stroke rehabilitation equipment allows patients to follow the movements of the equipment latently. Henceforth, the rehabilitation effect is poor, and the patient’s willingness to train is likewise low.
Now, Tongji Hospital and BrainCo have introduced NCyborg Project, a new stroke rehabilitation pattern based on brain-computer interface (BCI) and brain-inspired intelligent robots. The project will mainly focus on:
- An algorithm for analyzing the movement intention of stroke patients based on brain-machine interface technology.
- A motion control strategy for a rehabilitation robot based on brain-inspired motion perception.
- The mechanism of stroke rehabilitation using brain-inspired intelligent robots.
The main aim of the NCyborg Project is to develop an easy-to-use, reliable and affordable stroke rehabilitation robot to improve the rehabilitation effect of stroke survivors, speed up the rehabilitation process, and reduce the costs.
The organization will start training the robot to support the rehabilitation of the hand. The robot is expected to recognize no less than eight hand movement intentions with a recognition accuracy of ≥ 90% and a response time ≤ of 300 ms.
Co-corresponding author, Jonh H. Zhang, explains: “The project’s goal is to develop an easy-to-use, reliable and affordable stroke rehabilitation robot that will improve the effect of rehabilitation for stroke survivors, speed up the rehabilitation process, and reduce the costs involved.”
Co-corresponding author, Bicheng Han, adds: “We hope that, within five years, millions of stroke patients will be using this product and see their lives improve.”
Co-corresponding author Zhouping Tang said, “the ‘N’ in the NCyborg Project name stands for ‘neural,’ while in fictional stories ‘cyborg’ is often an icon that is enhanced mentally and/or physically over and above the ‘norm’ with technology. In the real world, we believe that NCyborg Project will set up a brand-new stroke rehabilitation pattern which could qualitatively improve the treatment effect for stroke survivors.”
- Qi Huang et al. NCyborg Project – A new stroke rehabilitation pattern based on brain-computer interface. DOI: 10.1016/j.hest.2021.05.002
With the continuous development of artificial intelligence technology, “brain-computer interfaces” are gradually entering the field of medical rehabilitation. As a result, brain-computer interfaces (BCIs) have been included in many countries’ strategic plans for innovating this field, and subsequently, major funding and talent have been invested in this technology. In neurological rehabilitation for stroke patients, the use of BCIs opens up a new chapter in “top-down” rehabilitation. In our study, we first reviewed the latest BCI technologies, then presented recent research advances and landmark findings in BCI-based neurorehabilitation for stroke patients. Neurorehabilitation was focused on the areas of motor, sensory, speech, cognitive, and environmental interactions. Finally, we summarized the shortcomings of BCI use in the field of stroke neurorehabilitation and the prospects for BCI technology development for rehabilitation.
According to WHO clinical criteria, stroke is defined as “a rapidly developing sign of focal (or global) brain dysfunction lasting more than 24 hours (unless interrupted by death), with no apparent nonvascular cause.” Stroke is the world’s second leading cause of death and third leading cause of injury and can cause severe cognitive, emotional, and sensorimotor impairment in patients . Most stroke victims survive the initial event, and the greatest impact of stroke disease is usually the long-term effect it has on the patient and their family [2, 3]. Unfortunately, there are significant gaps between countries in the quality of stroke research and the effectiveness of medical interventions . Over the last decade, advances in the medical treatment of stroke patients have resulted in a substantial reduction in mortality rates. However, one-third of the 16 million patients worldwide remain disabled each year .
In traditional rehabilitation, the gold standard in care for poststroke recovery is a combination of specialized training and general aerobic exercise. Bimanual arm training (BAT) and constraint-induced movement therapy (CIMT) are two of the most established methods for treating stroke-related sports injuries . These rehabilitation techniques are bottom-up interventions that focus on distal limb modulation to cause subsequent improvements in the neural circuits involved in motor recovery. However, even with intensive task-specific training and physical activity, 15-30% of people who have had a stroke are permanently disabled. As a result, many bottom-up interventions are ineffective in stroke patients who have very limited upper limb mobility (Fugl-Meyer score 20) . We need to explore and develop more effective stroke rehabilitation strategies that supplement or replace traditional rehabilitation training.
The remodeling of neurological function after stroke may facilitate the development of new interventions for poststroke rehabilitation, and recent therapeutic options have shifted to facilitating neural circuit reorganization in order to restore motor function. These top-down approaches to rehabilitation are largely due to the mechanisms of brain plasticity . The advancement of artificial intelligence methods and a better understanding of brain plasticity are also critical for functional movement recovery. The human brain’s ability to adapt to change and environmental stimuli (brain injury, treatment, and experience) by reorganizing its structure, function, and connections is known as brain plasticity . The basic structural reserve and anatomical plasticity of the brain are important parameters for significant motor recovery . Therefore, the key challenge is to figure out how to optimize neuroplasticity during treatment while also reinforcing connections across the infarcted region and promote creation of new connections, thus facilitating long-term functional recovery.
With the advancement of science and technology, artificial intelligence technologies, such as brain-computer interface (BCI), virtual reality (VR), and augmented reality (AR), are rapidly developing and are gradually being applied in the field of medicine. Due to its direct action on the brain, BCI induces brain plasticity and promotes functional reorganization of the brain, proving to be a superior approach in poststroke rehabilitation, especially for improving motor function in stroke patients. The limited neurorehabilitation modalities are no longer adequate to meet increasing rehabilitation needs of patients with central injuries, and BCI has been shown to be effective in improving motor function and enhancing the lives of stroke patients. In this review, we first examined the latest BCI technologies, including how BCIs are acquired, how signals are processed, and how other artificial intelligence technologies are combined with BCIs, such as functional electrical stimulation (FES) technology, virtual reality, exoskeletons, orthotics, and intelligent wheelchairs. We then presented the specific applications, mechanisms of action, and efficacy of BCI in the treatment of poststroke neural remodeling, such as in BCI-based neurorehabilitation of stroke patients in motor, sensory, verbal, cognitive, and environmental interactions. Finally, we summarized our recent research findings and shortcomings, as well as an outlook on the development of BCI technology in the field of rehabilitation.
2. BCI Technology
The word “brain-computer interface” was first formally identified as “a communication device that does not depend on the usual output pathways of the peripheral nerves and muscles of the brain” at the First International Conference on Brain-Computer Interface Technology in June 1999 . The brain-computer interface (BCI) is a new technology that enables interaction with one’s environment through brain signals. This technology takes physiological measurements of mental states directly from the brain and converts them into control signals that can be used to control external devices or computers . The BCI recognizes a set of patterns in brain signals by going through four successive stages: signal acquisition, feature extraction, feature transformation, and device output  (Figure 1).[…]
[Systematic Review] Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation – Full Text
In recent years, various studies have demonstrated the potential of electroencephalographic (EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of human limbs. This article is a systematic review of the state of the art and opportunities in the development of BCIs for the rehabilitation of upper and lower limbs of the human body. The systematic review was conducted in databases considering using EEG signals, interface proposals to rehabilitate upper/lower limbs using motor intention or movement assistance and utilizing virtual environments in feedback. Studies that did not specify which processing system was used were excluded. Analyses of the design processing or reviews were excluded as well. It was identified that 11 corresponded to applications to rehabilitate upper limbs, six to lower limbs, and one to both. Likewise, six combined visual/auditory feedback, two haptic/visual, and two visual/auditory/haptic. In addition, four had fully immersive virtual reality (VR), three semi-immersive VR, and 11 non-immersive VR. In summary, the studies have demonstrated that using EEG signals, and user feedback offer benefits including cost, effectiveness, better training, user motivation and there is a need to continue developing interfaces that are accessible to users, and that integrate feedback techniques.
Brain-computer interfaces (BCIs) represent a broad field of research and development from the last few decades. Scientists from all over the world have worked to acquire a deep understanding of BCIs, resulting in rapid and considerable progress in systems, development, and brainwave processing techniques including non-invasive methods such as electroencephalography (EEG) . Most relevantly, useful and novel applications developed in this domain have contributed to the evolution of technology in healthcare ; it is clearly evidenced that using a BCI system plays an efficient and “natural” role in the attempt to provide assistance and preventive care to people with neurological disorders [3,4].
The fields where BCIs can be applied are quite promising and diverse. In fact, it is becoming an innovative neurological technology that successfully allows the restoration and improvement of people’s motor and communication abilities [5,6]. According to [7,8,9,10,11,12], its application fields can be divided into communication and control, medical applications, training and education, games and entertainment, monitoring, prevention, detection and diagnosis, intelligent environments, neuromarketing, advertising, security, and authentication. However, this review focuses specifically on examining the applications proposed exclusively as support in rehabilitation processes of the upper and lower limbs of the human body.
For people with partial or total limitation of movement in the upper and lower limbs, using a BCI and classifying an EEG recorded over the sensorimotor cortex in real time gives the possibility of understanding psychological and motor parameters and intentions that allow the reestablishment of communication with the environment [13,14,15]. In short, BCI technology provides direct communication between the brain and an external device, which can assist with numerous diseases  such as epilepsy , Alzheimer’s disease , traumatic brain injuries , strokes [20,21], neurological diseases , multiple sclerosis , and Parkinson’s disease . Indeed, this problem demands innovative advances that counteract the varied impacts that humanity has on a social level, with the economic situation and life of each patient and his or her family coming into play .
With the goal of identifying advances and opportunities for improvement in the rehabilitation of upper and lower limbs in the human body, this review includes studies published between 2011 and 2020. In line with the goal of the review, for the included proposals, the devices used for EEG signal acquisition are described, as well as the processing methods within the system, and the BCI applications that support rehabilitation processes in the upper and lower limbs. On the other hand, it was observed that most studies involved BCI systems focused on the upper limbs, and the use of EEG signals and user feedback showed improvements in BCI systems. The objective of this review is to identify the contributions that have been made so far as well as describing opportunities for improvement and limitations that should be taken into account in order to guide future proposals focused on supporting rehabilitation processes of limbs of the human body based on the treatment of EEG signals and user feedback.[…]
[WEB] NCyborg Program: Can AI Robots Assist in Stroke Rehabilitation? Neuroscientists Develop Brain-Computer Interface to Find Out – Video
Stroke is a disease that usually results in disability and is the leading cause of death in the older adults community. A stroke happens when the supply of blood on the human brain is reduced or decreased from the normal flow. Stroke is a devastating disease that does not stop the agony even if the patient recovers from it.
Brain-Computer Program Potential Treatment for Stroke Survivors
75 percent of the stroke survivors are recorded with difficulties in conducting simple, daily activities. These people cannot move independently without supervision and heavily relies on long-term care and rehabilitation programs. However, using traditional equipment and long-term exercises in stroke rehabilitation have limited effect on the patients. The desire of patients to participate in programs listed under a rehabilitation process is also affected by poor motivation due to lack of equipment, reports EurekAlert.
Huazhong University of Science and Technology is recognized as one of the most prestigious clinical research institutes in China. The university oversees Tongji Medical College and the Department of Neurology in the Tongji Hospital. The Chinese research institute collaborated with the brain-computer interface top producer Zheijang BrainCo, Ltd to conduct a study regarding the utilization of robots for stroke patients.
The institutes aim to produce brain-computer interface technology and brain-inspired artificial intelligence for the betterment of equipment used in stroke rehabilitation. Together, they have developed NCyborg Project, a program that could help patients who were diagnosed with stroke get back their motor skills and have the independence to carry out their daily movements.
NCyborg Project’s stroke rehabilitation development and studies were published in the journal Brain Hemorrhages entitled “NCyborg Project – A new stroke rehabilitation pattern based on brain-computer interface.” The project contains several brain-computer programs that are beneficial for stroke survivors that undergo rehabilitation.
AI Robot Pattern for Improvement of Stroke Rehabilitation
The NCyborg Project includes analysis and prediction of the patient’s movement through the algorithm of the brain-computer interface technology. Along with the movement prediction, NCyborg also has a motion control strategy based on brain-inspired motion perception. According to the study, the rehabilitation process begins with the robotic support of the left hand of the stroke patient, as the specified body part is usually paralyzed and has limited movement capacity after a stroke occurs.
Loma Linda Univerisity School of Medicine’s neurosurgery expert and co-author of the study John H. Zhang said that the key interest of the NCyborg development is to have a user-friendly, reliable, and affordable stroke rehabilitation assistant. Zhang added that through the help of the NCyborg rehab robots, the improvement rates of stroke survivors are expected to increase. The brain-computer AI robots can also speed up rehabilitation procedures in a simple, cheaper way.
Zhejiang Qiangnao Technology Co., Ltd expert and co-author of the study Bicheng Han said that the team estimates million of patients will benefit from NCyborg. Han expresses that the improvement of stroke survivors is expected to be significant in just five years of utilizing the brain-computer interface rehabilitation pattern. If completed, the NCyborg Project will be among the greatest contributions to the cardiology and neurology fields, improving the treatment process for stroke survivors, Medical Xpress reports.
[Abstract] Combined action observation- and motor imagery-based brain computer interface (BCI) for stroke rehabilitation: a case report – Full text PDF
Upper extremity impairment is a problem usually found in poststroke patients, and it is seldom completely improved even following conventional physical therapy. Motor imagery (MI) and action observation (AO) therapy are mental practices that may regain motor function in poststroke patients, especially when integrating them with brain-computer interface (BCI) technology. However, previous studies have always investigated the effects of an MI- or AO-based BCI for stroke rehabilitation separately. Therefore, in this study, we aimed to propose the effectiveness of a combined AO and MI (AOMI)-based BCI with functional electrical stimulation (FES) feedback to improve upper limb functions and alter brain activity patterns in chronic stroke patients. Case presentation: A 53-year-old male who was 12 years post stroke was left hemiparesis and unable to produce any wrist and finger extension. Intervention: The participant was given an AOMI-based BCI with FES feedback 3 sessions per week for 4 consecutive weeks, and he did not receive any conventional physical therapy during the intervention. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) and active range of motion (AROM) of wrist extension were used as clinical assessments, and the laterality coefficient (LC) value was applied to explore the altered brain activity patterns affected by the intervention. Outcomes: The FMA-UE score improved from 34 to 46 points, and the AROM of wrist extension was increased from 0 degrees to 20 degrees. LC values in the alpha band tended to be positive whereas LC values in the beta band seemed to be slightly negative after the intervention.
An AOMI-based BCI with FES feedback training may be a promising strategy that could improve motor function in poststroke patients; however, its efficacy should be studied in a larger population and compared to that of other therapeutic methods.
Posted by Debbie Overman
The U.S. Food and Drug Administration has authorized the marketing of the IpsiHand, a new device indicated for use in patients 18 and older undergoing stroke rehabilitation to facilitate muscle re-education and for maintaining or increasing range of motion.
The IpsiHand Upper Extremity Rehabilitation System (IpsiHand System), from Neurolutions Inc, is a Brain-Computer-Interface (BCI) device that assists in rehabilitation for stroke patients with upper extremity—or hand, wrist and arm—disability.
“Thousands of stroke survivors require rehabilitation each year. Today’s authorization offers certain chronic stroke patients undergoing stroke rehabilitation an additional treatment option to help them move their hands and arms again and fills an unmet need for patients who may not have access to home-based stroke rehabilitation technologies.”
— Christopher M. Loftus, MD, acting director of the Office of Neurological and Physical Medicine Devices in the FDA’s Center for Devices and Radiological Health
Designed for Post-Stroke Rehab
Although stroke is a brain disease, it can affect the entire body and sometimes causes long-term disability such as complete paralysis of one side of the body (hemiplegia) or one-sided weakness (hemiparesis) of the body. Stroke survivors may have problems with the simplest of daily activities, including speaking, walking, dressing, eating and using the bathroom.
Post-stroke rehabilitation helps individuals overcome disabilities that result from stroke damage. The IpsiHand System uses non-invasive electroencephalography (EEG) electrodes instead of an implanted electrode or other invasive feature to record brain activity. The EEG data is then wirelessly conveyed to a tablet for analysis of the intended muscle movement (intended motor function) and a signal is sent to a wireless electronic hand brace, which in turn moves the patient’s hand. The device aims to help stroke patients improve grasping. The device is prescription-only and may be used as part of rehabilitation therapy.
The FDA assessed the safety and effectiveness of the IpsiHand System device through clinical data submitted by the company, including an unblinded study of 40 patients over a 12-week trial. All participants demonstrated motor function improvement with the device over the trial. Adverse events reported included minor fatigue and discomfort and temporary skin redness.
The IpsiHand System device should not be used by patients with severe spasticity or rigid contractures in the wrist and/or fingers that would prevent the electronic hand brace from being properly fit or positioned for use or those with skull defects due to craniotomy or craniectomy.
The IpsiHand System device was granted Breakthrough Device designation, which is a process designed to expedite the development and review of devices that may provide for more effective treatment or diagnosis of life-threatening or irreversibly debilitating diseases or conditions.
The FDA reviewed the IpsiHand System device through the De Novo premarket review pathway, a regulatory pathway for low- to moderate-risk devices of a new type. Along with this authorization, the FDA is establishing special controls for devices of this type, including requirements related to labeling and performance testing. When met, the special controls, along with general controls, provide reasonable assurance of safety and effectiveness for devices of this type.
This action creates a new regulatory classification, which means that subsequent devices of the same type with the same intended use may go through the FDA’s 510(k) premarket process, whereby devices can obtain clearance by demonstrating substantial equivalence to a predicate device.
The FDA granted marketing authorization of the Neurolutions IpsiHand Upper Extremity Rehabilitation System to Neurolutions Inc.
[Source(s): US Food and Drug Administration, PR Newswire]
Paralysis of an arm and/or leg is one of the most common effects of a stroke. But thanks to research carried out by scientists at the Defitech Foundation Chair in Brain-Machine Interface and collaborators, stroke victims may soon be able to recover greater use of their paralyzed limbs. The scientists’ pioneering approach brings together two known types of therapies—a brain-computer interface (BCI) and functional electrical stimulation (FES) – and has been published in Nature Communications.
“The key is to stimulate the nerves of the paralyzed arm precisely when the stroke-affected part of the brain activates to move the limb, even if the patient can’t actually carry out the movement. That helps reestablish the link between the two nerve pathways where the signal comes in and goes out,” says José del R. Millán, who holds the Defitech Chair at EPFL.
Twenty-seven patients aged 36 to 76 took part in the clinical trial. All had a similar lesion that resulted in moderate to severe arm paralysis following a stroke occurring at least ten months earlier. Half of the patients were treated with the scientists‘ dual-therapy approach and reported clinically significant improvements. The other half were treated only with FES and served as a control group.
For the first group, the scientists used a BCI system to link the patients’ brains to computers using electrodes. That let the scientists pinpoint exactly where the electrical activity occurred in the brain tissue when the patients tried to reach out their hands. Every time that the electrical activity was identified, the system immediately stimulated the arm muscle controlling the corresponding wrist and finger movements. The patients in the second group also had their arm muscles stimulated, but at random times. This control group enabled the scientists to determine how much of the additional motor-function improvement could be attributed to the BCI system.
The scientists noted a significant improvement in arm mobility among patients in the first group after just ten one-hour sessions. When the full round of treatment was completed, some of the first-group patients’ scores on the Fugl-Meyer Assessment—a test used to evaluate motor recovery among patients with post-stroke hemiplegia—were over twice as high as those of the second group.
“Patients who received the BCI treatment showed more activity in the neural tissue surrounding the affected area. Due to their plasticity, they could help make up for the functioning of the damaged tissue,” says Millán.
Electroencephalographies (EEGs) of the patients clearly showed an increase in the number of connections among the motor cortex regions of their damaged brain hemisphere, which corresponded with the increased ease in carrying out the associated movements. What’s more, the enhanced motor function didn’t seem to diminish with time. Evaluated again 6-12 months later, the patients hadn’t lost any of their recovered mobility.
More information: A. Biasiucci et al, Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke, Nature Communications (2018). DOI: 10.1038/s41467-018-04673-z
Journal information: Nature Communications
Provided by Ecole Polytechnique Federale de Lausanne
A brain-computer interface (BCI) provides a direct connection between cortical activity and external devices. BCIs may use non-invasive methods such as the Electroencephalogram (EEG) or invasive methods such as the Electrocorticogram (ECoG) or neural spike recordings (Homer et al., 2013; Guger et al., 2015, 2018). In the last decades, many BCI approaches have been developed, based on slow waves, evoked potentials (EPs), steady-state evoked potentials (SSEPs), code-based EPs or motor imagery (MI) paradigms, with the aim of bringing medical applications that help people to the market. The first BCI systems were used to spell, control prosthetic devices, or move cursors on a computer screen (Guger et al., 2015; Allison et al., 2020). Early BCI work focused on locked-in or completely locked-in patients. Nowadays, many more clinical applications of BCIs technology are being developed.
Several neurological disorders impair voluntary movements and communication, despite intact cognitive functioning. The spectrum of BCI usage for control is extremely wide and includes neural prostheses, wheelchairs (Fernández-Rodríguez et al.), home environments, humanoid robots, and much more (Fukuma et al.). Another exciting clinical application of BCIs focuses on facilitating the recovery of motor function after a stroke or spinal cord injury (Thompson et al.). BCIs for rehabilitation integrate BCIs with conventional methods and devices for rehabilitation like functional electrical stimulation (FES)-based neuroprostheses (Colachis et al.; Remsik et al.), transcranial direct current stimulation (tDCS) (Rodriguez-Ugarte et al.) etc. to enhance the brain’s reorganization of corticospinal and cortico-muscular connections after acute, sub-acute, or chronic lesions.
Beside motor deficits, BCI-induced brain plasticity might contribute to the treatment of high-order cortical dysfunctions, such as improving social and emotional behaviors in autism spectrum disorder (Amaral et al.), training inhibitory control and working memory in ADHD, as well as contributing to the rehabilitation of cognitive deficits related to dementia. Moreover, BCI-based brain training can help preserve cognitive performance in healthy older adults, promoting successful aging and reducing the social burden of the population’s increasing aging. BCIs are also used to establish closed-loop control of brain sensing and stimulation technology to improve, for example, tremor, or to provide sensation. Another new challenge described in this Research Topic refers to the inner speech detection, defined as the ability to generate internal speech representations, in the absence of any external speech stimulation or self-generated overt speech (Martin et al.).
Finally, BCIs may increase the diagnostic accuracy of brain disorders. For instance, BCIs could be used to detect neural signatures of cognitive processes in persons diagnosed with disorders of consciousness (DOC) (Annen et al.; Guger et al.; Heilinger et al.), provide real-time functional brain mapping for neurosurgery (Jiang et al.), improve visual function assessment in glaucoma, detect the intraoperative awareness during general anesthesia (Rimbert et al.), screening for cognitive function in complete immobility (Lulé et al.), etc.
The articles here present different BCI approaches that could enter mainstream clinical practice, improving the assessment, rehabilitation, and management of several neurological diseases. All presented papers use elaborate, task-specific experiment setups with both invasive and non-invasive BCIs. Future research can build on these pioneering works and bring new standardized BCI applications in medicine.
Allison, B. Z., Kübler, A., and Jin, J. (2020). 30+ years of P300 brain-computer interfaces. Psychophysiology 57:e13569. doi: 10.1111/psyp.13569
Guger, C., Kapeller, C., Ortner, R., and Kamada, K. (2015). “Motor imagery with brain-computer interface neurotechnology,” in Motor Imagery, ed B. M. Garcia (Hauppauge, NY: Nova Science Publishers), 61–79.
Guger, C., Kapeller, C., Ogawa, H., Prückl, R., Grünwald, J., and Kamada, K. (2018). “Electrocorticogram based brain-computer interfaces,” in Smart Wheelchairs and Brain-Computer Interfaces, ed P. Diez (Amsterdam: Elsevier), 197–227.
Homer, M. L., Nurmikko, A. V., Donoghue, J., and Hochberg, L. (2013). Sensors and decoding for intracortical brain computer interfaces. Annu. Rev. Biomed. Eng. 15, 383–405. doi: 10.1146/annurev-bioeng-071910-124640
THIS ARTICLE IS PART OF THE RESEARCH TOPIC Breakthrough BCI Applications in Medicine View all 22 Articles >
[Abstract + References] Use of a Brain–Computer Interface + Exoskeleton Technology in Complex Multimodal Stimulation in the Rehabilitation of Stroke Patients
Introduction. Studies of the potentials of brain–computer neural interface technology with an arm exoskeleton (BCNI) in training to motor imagery in the recovery of higher mental functions in patients constitute an interesting task. The effectiveness of multimodal stimulation including diverse information channels needs to be assessed, as this approach should promote stimulation of neuroplasticity and improvement to interhemisphere interactions.
Objectives. To study the influences of multimodal stimulation using BCNI technologies on the restoration of cognitive functions in stroke patients.
Materials and methods. A total of 44 stroke patients were studied and treated at periods of two months to two years after onset. Patients were divided into two groups with comparable main parameters: a study group (22 patients) and a reference group (22 patients). Patients of the study group underwent a program of complex multimodal stimulation including procedures using BCNI technologies, cognitive training, use of a stabilometric platform with biological feedback for the support reaction, and vibrotherapy. Patients of the reference group received only BCNI.
Results. After treatment, statistically significant improvements in therapeutic results were obtained in the form of improvements in memory, attention, and visuospatial skills in patients of the study group as compared with those of the reference group.
Conclusions. Questions of cognitive training using BCNI technologies are currently a relatively new direction in neurorehabilitation; the promising results obtained here provide evidence of the potential of this direction.
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