Research on Upper and Lower Limb Rehabilitation System based on Brain-computer Interface Technology

Globally, a wide range of mental disorders continues to significantly impact people’s lives, with traditional treatment methods often falling short of providing effective and systematic solutions. Brain-computer interface (BCI) technology, one of the most rapidly advancing fields, offers promising applications across various sectors of human society, including production, healthcare, and media. By combining different BCI technologies, such as EEG monitoring and Deep Brain Stimulation, medical institutions can now offer patients a broader spectrum of effective or potential treatment options. This paper provides a comprehensive review of the current application status of various representative BCI technologies in mental health treatment. It also explores the prospect of constructing an integrated treatment system for mental disorders based on BCI technology, aiming to enhance this paper understanding of BCI applications in mental health care. The study examines how different BCI technologies can be utilized at various stages of disease progression, from early diagnosis to long-term management. Additionally, it discusses the potential of BCI in improving patient outcomes, quality of life, and social integration, while addressing ethical considerations and future research directions in this rapidly evolving field.

The integration of Brain-Computer Interface (BCI) technology into financial decision-making processes is a transformative approach that improves the precision and efficacy of real-time financial strategies. BCI technology is capable of facilitating enhanced and expedited assessments by directly interpreting brain impulses and providing substantial insights into cognitive patterns. This optimisation is achieved by monitoring mental and emotional processes in real time, which leads to a more comprehensive comprehension of financial preferences, decision fatigue, and risk tolerance. This article investigates the potential applications, advantages, and ethical implications of brain-computer interface (BCI) technology in financial decision-making, with a particular emphasis on its revolutionary influence on major financial institutions and ordinary investors.

Finding enjoyable and effective long-term approaches to rehabilitation for improving the upper limb (UL) function of people with multiple sclerosis (MS) is challenging. Virtual reality (VR) with its engagement, interaction, and real time feedback could be a solution, with immersive VR systems having preliminary evidence in stroke to be more effective in improving UL outcomes in comparison to non-immersive VR. However, immersive VR research is limited within the realm of MS rehabilitation. Therefore, the main aim of the PhD was to explore immersive VR as an approach for UL rehabilitation for people with MS (pwMS). This was achieved in four stages: 1) a systematic review of the current evidence for VR in UL rehabilitation in MS; 2) predevelopment focus groups to determine requirements for the development of bespoke immersive VR games; 3) software development of bespoke VR games to potentially improve UL function for pwMS; and 4) a feasibility study of the games and immersive VR for pwMS’ UL rehabilitation. The systematic review initially yielded ten articles with varying study design, VR intervention and UL outcome measures used. There were some statistically significant improvements after VR interventions in outcomes such as in nine-hole peg test (NHPT), hand grip strength and active range of motion. There were however conflicting results regarding if VR was more effective than conventional approaches. Whilst the review demonstrated there were positive results for VR improving UL function, most of the studies had small sample sizes and were inadequately powered, and the level of immersion on effectiveness in MS remains unknown. The predevelopment focus groups recruited ten pwMS and eight specialist clinicians to explore their views on immersive VR as an approach to UL rehabilitation and determine requirements to develop VR games for this purpose. PwMS commonly reported that their UL problems interfered with activities of daily living and resulted in the loss of meaningful hobbies such as writing. Many pwMS neglected UL exercise and found strategies for adapting to their UL impairments. Similarly, clinicians stated UL rehabilitation was neglected within their service and that it was challenging to find interesting treatment strategies. VR was suggested by both participant groups as a potential solution, as it was convenient for pwMS to access and could provide a more engaging and disguised approach to exercise. Although, there were shared concerns about cybersickness and disengagement while using VR approaches. Finally, both groups agreed games should be meaningful and adaptable for users but suggested different VR activities, with clinicians suggesting games directly reflecting activities of daily living and pwMS suggesting more abstract activities. Recommendations were made for the development of VR rehabilitation games which could be personalised and customisable for the varying abilities of pwMS. Using theme-based content analysis and requirement analysis, requirements for the software development of immersive VR games were created and developed using Unity game engine. Three games, “Piano”; “Catch-a-falling-star” and “VR Whack-a-mole” which targeted dexterity and strength for pwMS were developed, with different difficulty levels implemented to adapt to varying ability of pwMS. The final stage of this PhD was a randomised, controlled feasibility study to investigate an intervention of immersive VR involving the three co-produced games for pwMS’ UL rehabilitation. Participants were randomised to either an intervention group consisting of 30 minutes of VR co-produced games, twice/week, for eight weeks, or to a control group of usual care. Participants were recruited from the NHS and third sector organisations. Nineteen pwMS were recruited (intervention n=11, control n=8), with a recruitment rate of 3.2 participants/month. Only minor adverse events were reported (n=9), such as fatigue and UL pain. Adherence was good with 87% (±12%) session completed, and two dropouts were reported in the control group. There were positive trends for improving NHPT and hand grip strength, but there was an observed ceiling effect in the Action Research Arm Test. Participants reported high levels of satisfaction and good usability of the co-produced VR games, as measured by the USE questionnaire. Thematic analysis revealed participants enjoyed the distraction and atmospheres that immersive VR provided, and believed the games were fit for purpose with the majority of participants reporting improvements in their UL function. Participants suggested more variety and progression of exercises for future development. Overall, these studies show that immersive VR has the potential to be beneficial for UL rehabilitation and is feasible and safe for pwMS. Therefore, future work should refine VR games for long-term application and explore the clinical and cost effectiveness of this approach.

Stroke survivors and their families have had to cope with major obstacles, but Brain-Computer Interface (BCI) technology is being touted as an innovation that is a game changer. Traditional modes of rehabilitation offer only minimal help to the severely impaired; however, BCI technology provides a unique ray of hope by enabling direct communication between the brain and devices for therapy and interactive purposes. This means not only restoring movement skills or improving thinking and talking abilities but also transforming the way a person interacts with their surroundings after having a stroke. Despite this potentiality, there are several challenges that must be overcome before BCI technology can be effectively applied in clinics, like signal decoding complexity and universality & accessibility hunt. It seeks to delve into the possibilities of BCI in rehabilitation after stroke, focusing on motor recovery, cognitive and communication restoration as well as environmental interactions. These include demonstrations of innovative uses such as fNIRS-based BCIs for controlling prosthetic limbs, neurofeedback training for cognitive enhancements, and making better use of customizing BCIs to improve mobility and communication. However, it also addresses the obstacles faced in signal acquisition, interpretation, personalization, adaptation, and the training necessary for effective BCI utilization. In order to tackle these issues, forward-looking algorithms, machine learning hybrid BCI systems, and adaptive learning systems were discussed, which mark out how these obstacles will be overcome in this detailed review. The article underscores interdisciplinary collaboration in addition to user-centric designs, ethical considerations regarding access, and continued innovation, which all speak towards a future where BCI technology not only overcomes current limitations but makes rehabilitation different, giving new hope and skills among stroke survivors again.

Here, we present a state-of-the-art review of the research performed on the brain-computer interface (BCI) technologies with a focus on signal processing approaches. BCI can be divided into three main components: signal acquisition, signal processing, and effector device. The signal acquisition component is generally divided into two categories: noninvasive and invasive. For noninvasive, this review focuses on electroencephalogram. For the invasive, the review includes electrocorticography, local field potentials, multiple-unit activity, and single-unit action potentials. Signal processing techniques reviewed are divided into time-frequency methods such as Fourier transform, autoregressive models, wavelets, and Kalman filter and spatiotemporal techniques such as Laplacian filter and common spatial patterns. Additionally, various signal feature classification algorithms are discussed such as linear discriminant analysis, support vector machines, artificial neural networks, and Bayesian classifiers. The article ends with a discussion of challenges facing BCI and concluding remarks on the future of the technology.

In the research of brain-computer interface (BCI) technology, it is difficult to recognize electroencephalogram (EEG) signals induced by different movements of a single joint. In response to this situation, this paper proposes the use of empirical mode decomposition (EMD) to obtain the intrinsic mode function (IMF), combining amplitude-frequency (AF) domain information in the IMF with the common spatial pattern (CSP) to propose the AF-CSP construction shoulder. The eigenvectors of the three types of joints imaginary EEG signals are classified and recognized by the twin support vector machine. The experimental results show that the accuracy rate of the feature vectors constructed by the AF-CSP method proposed in this paper is 89.7 percent recognized by the twin support vector machine, which proves the effectiveness of the method and can be further used in brain-computer interfaces.

Brain-computer interface (BCI) technology is an innovative human-computer interaction technology that does not rely on the peripheral nerve transmission pathway and muscle tissues, and establishes the connection between the human brain and the external machine. BCI system includes three categories: active, reactive and passive, with the motor imagery brain-computer interface (MI-BCI) is the most common active BCI system. MI-BCI controls external devices by imagining movements in the brain, without actually having to perform the movement. In order to bring more immersion to patients, the introduction of augmented reality (AR) technology can increase the interest of patients and improve the concentration on rehabilitation training. This study reviews the overview of BCI technology, the application of MI-BCI technology in the rehabilitation of nervous system diseases, and the limitations and prospects applications of MI-BCI technology in the rehabilitation of nervous system diseases, so as to provide reference for the application of MI-BCI technology in the diagnosis and rehabilitation of nervous system diseases. Specifically, the overview of BCI technology mainly introduces BCI technology, MI-BCI technology and AR-MI-BCI rehabilitation training system (the process and overall structure of the AR-MI-BCI rehabilitation training system). MI-BCI system has many applications in neurological diseases such as stroke, drug addiction and depression, which can not only effectively assist the diagnosis of neurological diseases, but also activate specific brain regions to promote brain function rehabilitation. MI-BCI system can identify the motor imagery intention of stroke patients and guide them to actively imagine body movements, which is helpful for active rehabilitation of patients. To address the poor generalization issues in traditional machine learning algorithms at the individual level of stroke patients, MI-BCI rehabilitation training system is built on the transfer learning technology. The rehabilitation training system based on AR-MI-BCI can assist drug users to reduce drug addiction and the difficult withdrawal of mental dependence and physical dependence caused by drug abuse, and make drug users have a connection between drugs and resistance emotion. EEG recognition scheme based on MI-BCI multi-frequency brain network can assist in the diagnosis of depression, but MI-BCI technology has some shortcomings in terms of technical development, device cost, patient privacy and clinical application.

The development of brain-computer interface (BCI) technology has enabled animals to execute movements in accordance with human intent. The rat robot represents a novel robotic system based on BCI technology. However, due to limitations in electrode fabrication techniques and the use of simplistic control strategies, current rat robots are restricted to limited movement patterns, which hinder their applicability in real-world scenarios. To address these challenges, we have developed a portable wireless neural stimulator and a novel 3D integrated stimulating electrode. By refining the locomotion control strategy, we aim to achieve complex, high-degree-of-freedom movement in rat robot systems. 3D integrated electrodes were implanted into the rats' head, with no reward-based training required. By utilizing a wearable wireless stimulation backpack to connect the electrodes and deliver electrical stimulation to multiple brain regions, thereby enabling the rat to perform forward movement, turning, and stopping behaviors. The experimental results demonstrate that under optimized stimulation parameters, the forward speed of the rat robot can be controlled to achieve 31.06 ± 1.21 m/min, the turning angle can reach up to 150 ± 1.22°, and the stopping duration can be flexibly adjusted. Furthermore, we presented a practical scenario in which the rat robot successfully executed a predefined navigation task in a real-world environment, thereby validating its high degree of movement flexibility and control precision. This study achieved high-degree-of-freedom motion control of rat robots without the need for reward-based training, which was previously unattainable. This research establishes a crucial foundation and provides valuable technical references for the application of animal robots in fields such as information reconnaissance and wreckage search and rescue operations.

Chapter 24 - Ethics and the emergence of brain-computer interface medicine

Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose significant threats to human mortality, morbidity, and functional independence. Brain-Computer Interface (BCI) technology, which facilitates direct communication between the brain and external devices, emerges as an innovative key to unlocking neurological conditions, demonstrating significant promise in this context. This comprehensive review uniquely synthesizes the latest advancements in BCI research across multiple neurological disorders, offering an interdisciplinary perspective on both clinical applications and emerging technologies. We explore the progress in BCI research and its applications in addressing various neurological conditions, with a particular focus on recent clinical studies and prospective developments. Initially, the review provides an up-to-date overview of BCI technology, encompassing its classification, operational principles, and prevalent paradigms. It then critically examines specific BCI applications in movement disorders, disorders of consciousness, cognitive and mental disorders, as well as sensory disorders, highlighting novel approaches and their potential impact on patient care. This review reveals emerging trends in BCI applications, such as the integration of artificial intelligence and the development of closed-loop systems, which represent significant advancements over previous technologies. The review concludes by discussing the prospects and directions of BCI technology, underscoring the need for interdisciplinary collaboration and ethical considerations. It emphasizes the importance of prioritizing bidirectional and high-performance BCIs, areas that have been underexplored in previous reviews. Additionally, we identify crucial gaps in current research, particularly in long-term clinical efficacy and the need for standardized protocols. The role of neurosurgery in spearheading the clinical translation of BCI research is highlighted. Our comprehensive analysis presents BCI technology as an innovative key to unlocking neurological disorders, offering a transformative approach to diagnosing, treating, and rehabilitating neurological conditions, with substantial potential to enhance patients' quality of life and advance the field of neurotechnology.

Brain-computer interface (BCI) technologies, such as deep brain stimulation and transcranial magnetic stimulation, have clear medical benefits and are largely familiar to the medical community. Meanwhile, companies are developing BCIs for personal use, to promote cognitive performance and assist with everyday tasks. On a recent episode of the ANA Investigates podcast, Dr Karen Rommelfanger, professor at Emory University and founder of the Institute of Neuroethics, discusses the neuroethics of medical and commercial applications of BCI. BCI encompasses both sensing technologies (which monitor brain activity) and stimulating devices (which alter brain pathways). Dr Rommelfanger begins the podcast by distinguishing between the two. She emphasizes that many commercially available devices tend to be wearable sensing technologies as an effort to be more easily accessible, affordable, and less invasive than clinical technology.1 At the present moment, she says we should not fall prey to popular media and sci-fi tropes about so-called “mind reading” or “mind control.” However, Dr Rommelfanger says that there are legitimate concerns around privacy with existing consumer-ready devices, both around how companies use the data they collect and whether that data are adequately secured as there are gaps in regulations, particularly around potential inferences that might be made about individuals' current or future brain function. In the podcast, Dr Rommelfanger discusses how devices especially in the clinical research realm that can sense, but also stimulate are the most advanced of BCIs; for example, brain mapping to enable communication and speech in those who have lost the ability to do so and affecting aspects of personality and emotional range with implantable electrodes for intractable depression. She makes the point later in the podcast that neuroethics can guide the development in these clinical and consumer devices as well, such as considering the inclusiveness of different ethnicities and hair types when developing the standard electroencephalogram (EEG) leads.2 She also addresses the neuroethics related to brain organoids, tiny 3-dimensional aggregates of brain cells currently used in research and development of novel treatments, some of which have been observed to produce spontaneous electrical activity that resembles the activity of brains of premature human embryos. She outlines how questions about the moral status of brain organoids may influence how organoids are handled and how tissue donors are consented for research. Dr Rommelfanger concludes the podcast by discussing the clinicians' role in helping patients understand the utility and limits of commercially available BCI technologies and advocating for greater clinician involvement in BCI development.3 She suggests that clinicians familiarize themselves with information from the Institute of Neuroethics Think and Do Tank (see Institute of Neuroethics Guidance), International Neuroethics Society, and the Dana Foundation to educate patients and fellow clinicians attempting to navigate this field in applied and practical neuroethics and related policy.4, 5 On a societal level, she points to a role for physicians in promoting access and medical reimbursement of BCI technologies. She also emphasizes the need to promote equity in the development of BCI, both to improve user experiences across diverse groups and to prevent misuse of technologies. R.D.S. conducted the primary podcast interview and wrote the first draft of the manuscript. R.D.S., A.J.C., K.S.R., and A.L.G. participated in the podcast interview and edited the manuscript. R.D.S., A.J.C., A.L.G. have nothing to disclose. K.S.R. offers consulting for policy entities, foundations, and companies related to neurotechnology and ethics strategy.

This paper discusses the implications of incorporating Brain-Computer Interface (BCI) technology into vulnerable populations with Intellectual Disabilities (ID). Although this technology is not currently widespread, the paper postulates that there could be a rapid increase in these surgeries in the future. Based on this assumption, the content is entirely scenario-based and offers various possibilities. The author is encouraging a preventative discussion surrounding the topic of elective BCI implantation in people with ID. The paper provides several central concerns about this technology. The first and most important is informed consent: many individuals in this population may lack the cognitive capacity to fully comprehend the profound, long-term implications of this surgical procedure. The paper also addresses that the decision to undergo a neurological modification—a procedure popularized by companies like Neuralink—will often be made by parents or guardians and not youth with extreme intellectual disabilities, and the paper argues against this. Another contested area surrounding this presumed elective surgery is the ownership of the human soul and the concern that unnecessary surgical intervention could usurp its divine ownership. Other considerations in this document include the widespread adoption of BCI, which could lead to a surge in demand and a surgical waiting list, despite a lack of thorough analysis and critique of its risks. This paper aims to spur a critical conversation about why implementing BCI technology in vulnerable populations warrants extreme caution. By exploring these moral dimensions, this writing contends that BCI technology poses profound, long-term consequences that must be fully considered to prevent widespread implementation in the intellectually vulnerable populations of the world.

Machine learning-based feature combination analysis for odor-dependent hemodynamic responses of rat olfactory bulb

The virtual rehabilitation system combining virtual rehabilitation environment and upper limb rehabilitation technology is interactive and interesting, which can improve the enthusiasm and initiative of patients for rehabilitation training, improve the efficiency of rehabilitation training and improve the effect of rehabilitation treatment. This paper firstly conducts in-depth research and analysis on the research progress of the upper limb rehabilitation robot system, and deeply studies the principle and rehabilitation principle of the stroke caused by hemiplegic dyskinesia, and summarizes the goals and methods of the upper limb rehabilitation system design. Secondly, the hand motion tracking is realized by Kinect’s bone tracking, and the optimal tracking distance is determined experimentally, which verifies the stability and robustness of the tracking. Static gesture recognition adopts two gesture recognition schemes based on Kinect depth image and color space model respectively. Finally, using the rehabilitation robot and Kinect sensor as the hardware platform, the virtual rehabilitation training system experimental platform is constructed, and the horizontal rehabilitation exercise and the three-dimensional space rehabilitation exercise are respectively studied experimentally, and the exercise data obtained by using healthy subjects as the experimental object is analyzed. Based on this, the validity and feasibility of the Kinect-based upper limb exercise rehabilitation interactive system were verified.

Dynamic evaluation mechanisms of the human upper limb are of great value for research and applications in upper limb rehabilitation, especially for the development of robotic upper limb rehabilitation systems. This paper proposes a muscle force prediction method based on the Hill muscle model. The proposed approach, which combines sEMG signals and kinematic data, provides a deep understanding of the dynamic motion mechanisms and parameters that characterize the upper limbs of the human body. The study provides a theoretical benchmark for the evaluation of rehabilitation training practices and for improved designs of upper limb rehabilitation robots that are used for upper limb neuro-rehabilitation. Specifically, the system collected motion data and sEMG signals from the upper limbs of the human body through a high-speed infrared motion capture system and skin sEMG sensors. By applying human kinematics and dynamics theories, real-time joint angle and torque information was obtained and imported into OpenSim. This platform can simulate the real-time muscle force values produced by the upper limbs during movements. The myoelectric signals were first filtered to remove noise, and an exponential model was then used to obtain the muscle activation. These data were then entered into the Hill-type prediction model to determine an individual’s muscle forces. In this paper, grasping movements commonly used in everyday situations were taken as a testing case. The results of the experiments showed that an individual’s muscle forces can be predicted using a Hill-type model. The results are consistent with those from simulated muscle force models and can reflect the real forces experienced during upper limb exercises.KeywordsMuscle forceUpper limb movementHuman–robot interactionBiomechanics analysissEMG signals