Brain-machine Interface Technology Research Articles

A metamaterial unit-cell based patch radiator for brain-machine interface technology

This paper presents a novel approach to the design of a brain implantable antenna tailored for brain-machine interface (BMI) technology. The design is based on a U-shaped unit-cell metamaterial (MTM), introducing innovative features to enhance performance and address specific challenges associated with BMI applications. The motivation behind the use of the unit-cell structure is to elongate the electric path within the antenna patch, diverging from a reliance on the electrical properties of the MTM. Consequently, the unit cell is connected to an inset-fed transmission line and shorted to the ground. This configuration serves the dual purpose of reducing the size of the antenna and enabling resonance at the 2.442 GHz band within a seven-layer brain phantom. The antenna is designed using a FR-4 substrate (εr = 4.3 and tan δ = 0.025) of 1.5 mm thickness, and it is coated with a biocompatible polyamide material (εr = 4.3 and tan δ = 0.004) of 0.05 mm thickness. The proposed antenna achieves a compact dimension of 20 × 20 × 1.6 mm3 (0.338 × 0.338 × 0.027 λg3) and demonstrates a high bandwidth of 974 MHz with its gain of −14.6 dBi in the 2.442 GHz band. It also exhibits a matched impedance of 49.41-j1.32 Ω in the implantable condition, corresponding to a 50 Ω source impedance. In comparison to a selection of relevant research works, the proposed antenna has a low specific absorption rate (SAR) of 218 W/kg and 68 W/kg at 1g and 10g brain tissue standards, respectively. An antenna prototype has been fabricated and measured for return loss in both free space and in-vivo conditions using sheep's brain. The measurement results are found to be in close agreement with the simulation results for both conditions, showing the practical applicability of the proposed antenna for BMI applications.

Pro-myelinating clemastine administration improves recording performance of chronically implanted microelectrodes and nearby neuronal health

Intracortical microelectrodes have become a useful tool in neuroprosthetic applications in the clinic and to understand neurological disorders in basic neurosciences. Many of these brain-machine interface technology applications require successful long-term implantation with high stability and sensitivity. However, the intrinsic tissue reaction caused by implantation remains a major failure mechanism causing loss of recorded signal quality over time. Oligodendrocytes remain an underappreciated intervention target to improve chronic recording performance. These cells can accelerate action potential propagation and provides direct metabolic support for neuronal health and functionality. However, implantation injury causes oligodendrocyte degeneration and leads to progressive demyelination in surrounding brain tissue. Previous work highlighted that healthy oligodendrocytes are necessary for greater electrophysiological recording performance and the prevention of neuronal silencing around implanted microelectrodes over the chronic implantation period. Thus, we hypothesize that enhancing oligodendrocyte activity with a pharmaceutical drug, Clemastine, will prevent the chronic decline of microelectrode recording performance. Electrophysiological evaluation showed that the promyelination Clemastine treatment significantly elevated the signal detectability and quality, rescued the loss of multi-unit activity, and increased functional interlaminar connectivity over 16-weeks of implantation. Additionally, post-mortem immunohistochemistry showed that increased oligodendrocyte density and myelination coincided with increased survival of both excitatory and inhibitory neurons near the implant. Overall, we showed a positive relationship between enhanced oligodendrocyte activity and neuronal health and functionality near the chronically implanted microelectrode. This study shows that therapeutic strategy that enhance oligodendrocyte activity is effective for integrating the functional device interface with brain tissue over chronic implantation period.

Classification of Motor-Imagery Tasks using a Large EEG Dataset by Fusing Classifiers Learning on Wavelet-Scattering Features.

Brain-computer or brain-machine interface technology allows humans to control machines using their thoughts via brain signals. In particular, these interfaces can assist people with neurological diseases for speech understanding or physical disabilities for operating devices such as wheelchairs. Motor-imagery tasks play a basic role in brain-computer interfaces. This study introduces an approach for classifying motor-imagery tasks in a brain-computer interface environment, which remains a challenge for rehabilitation technology using electroencephalogram sensors. Methods used and developed for addressing the classification include wavelet time and image scattering networks, fuzzy recurrence plots, support vector machines, and classifier fusion. The rationale for combining outputs from two classifiers learning on wavelet-time and wavelet-image scattering features of brain signals, respectively, is that they are complementary and can be effectively fused using a novel fuzzy rule-based system. A large-scale challenging electroencephalogram dataset of motor imagery-based brain-computer interface was used to test the efficacy of the proposed approach. Experimental results obtained from within-session classification show the potential application of the new model that achieves an improvement of 7% in classification accuracy over the best existing classifier using state-of-the-art artificial intelligence (76% versus 69%, respectively). For the cross-session experiment, which imposes a more challenging and practical classification task, the proposed fusion model improves the accuracy by 11% (54% versus 65%). The technical novelty presented herein and its further exploration are promising for developing a reliable sensor-based intervention for assisting people with neurodisability to improve their quality of life.

C2SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction.

Recent developments in brain-machine inter-face technology have rendered seizure prediction possible. However, the transmission of a large volume of electro-physiological signals between sensors and processing apparatuses and the related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computational resources, especially for power-critical wearable and implantable medical devices. Although many data compression methods can be adopted to compress the signals to reduce communication bandwidth requirement, they require complex compression and reconstruction procedures before the signal can be used for seizure prediction. In this paper, we propose C2SP-Net, a framework to jointly solve compression, prediction, and reconstruction without extra computation overhead. The framework consists of a plug-and-play in-sensor compression matrix to reduce transmission bandwidth requirements. The compressed signal can be utilized for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Compression and classification overhead from the energy consumption perspective, prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated using various compression ratios. The experimental results illustrate that our proposed framework is energy efficient and outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.6% in prediction accuracy with a compression ratio ranging from 1/2 to 1/16.

Learning to control a BMI-driven wheelchair for people with severe tetraplegia.

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.

Patent landscape of brain-machine interface technology.

Patent landscape of brain-machine interface technology.

Emerging Neuralink Brain Machine Interface Technology: An Oversight Proposal to Address the Ethical, Legal, and Social Implications

Humanity is heading towards a new era with the advancements being made in artificial intelligence (AI) and neuroscience. Amongst these advances is the development of brain machine interface (BMI) t...

Clinical Diagnosis of Psychiatry Based on Electroencephalography

At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.

Non-Invasive Brain-Computer Interface Technology (BCI) Modalities and Implementation: A Review Article and Assimilation of BCI Models

Brain-computer interface (BCI) technology or brain-machine interface (BMI) technology has become the most attractive field for researchers in various disciplines and has occupied an important place in many scientific and even recreational applications. This review first highlights the different and most frequently used methods for implementing brain-computer interface (BCI) systems with a focus on non-invasive BCI models. Secondly, it analyzes the different stages of building a BCI system (input stage, signal processing stage, and output stage). Then it compares the different methods in terms of the algorithms used and the pros and cons. The aim of the study is to find the most adequate and price method to record the EEG by means of electrodes placed on the scalp. Then some features will be extracted from the EEG and sent to a classifier, whose response is translated features into some action whose execution provides feedback to the user.

Use of Brain-Machine Interface technology in the rehabilitation of patients

In the last few decades, there have been advances in the field of innovative technologies used for the rehabilitation of people with a motor disability. A great example is the Brain-Machine Interface (BMI) technologies, which allow the control of machines through the brain activity of individuals and contributes to a reorganization of their motor and sensory systems. Thus, several evidences have suggested the use of technologies in the rehabilitation of these patients. The aim of this study was to perform a literature review on the use of technologies applied to motor rehabilitation. To carry out this study, a search for scientific articles was performed in the Pubmed, Scielo and Lilacs databases, in addition to the dissertations and theses found on the CAPES database. There were a total of 24 references, published between 2002 and 2020. According to the literature studied, there is an increase in resources that use technologies as therapeutic options. Many of the conventional interventions are being replaced or associated with these innovative technologies. With the advent of BMI technology and its use in human beings, a technological revolution can be observed in several biomedical areas, thus allowing a multidisciplinary application in the rehabilitation of motor, sensory or cognitive functions in patients. Despite the advances, this subject still shows controversies and before a broad recommendation, more randomized studies and a greater ethical recommendation on the subject will be needed.

Brain-Machine Interfaces: A Tale of Two Learners

Brain-machine interface (BMI) technology has rapidly matured over the last two decades, mainly thanks to the introduction of artificial intelligence (AI) methods, in particular, machine-learning algorithms. Yet, the need for subjects to learn to modulate their brain activity is a key component of successful BMI control. Blending machine and subject learning, or mutual learning, is widely acknowledged in the BMI field. Nevertheless, we posit that current research trends are heavily biased toward the machine-learning side of BMI training. In this article, we take a critical view of the relevant literature, and our own previous work, to identify the key issues for more effective mutual-learning schemes in translational BMIs that are specifically tailored to promote subject learning. We identify the main caveats in the literature on subject learning in BMI, in particular, the lack of longitudinal studies involving end users and shortcomings in quantifying subject learning, and pinpoint critical improvements for future experimental designs.

Chronic neural interfacing with cerebral cortex using single-walled carbon nanotube-polymer grids

Objective. The development of electrode arrays able to reliably record brain electrical activity is a critical issue in brain machine interface (BMI) technology. In the present study we undertook a comprehensive physico-chemical, physiological, histological and immunohistochemical characterization of new single-walled carbon nanotubes (SWCNT)-based electrode arrays grafted onto medium-density polyethylene (MD-PE) films. Approach. The long-term electrical stability, flexibility, and biocompatibility of the SWCNT arrays were investigated in vivo in laboratory rats by two-months recording and analysis of subdural electrocorticogram (ECoG). Ex-vivo characterization of a thin flexible and single probe SWCNT/polymer electrode is also provided. Main results. The SWCNT arrays were able to capture high quality and very stable ECoG signals across 8 weeks. The histological and immunohistochemical analyses demonstrated that SWCNT arrays show promising biocompatibility properties and may be used in chronic conditions. The SWCNT-based arrays are flexible and stretchable, providing low electrode-tissue impedance, and, therefore, high compliance with the irregular topography of the cortical surface. Finally, reliable evoked synaptic local field potentials in rat brain slices were recorded using a special SWCNT-polymer-based flexible electrode. Significance. The results demonstrate that the SWCNT arrays grafted in MD-PE are suitable for manufacturing flexible devices for subdural ECoG recording and might represent promising candidates for long-term neural implants for epilepsy monitoring or neuroprosthetic BMI.

A state-based probabilistic method for decoding hand position during movement from ECoG signals in non-human primate

Objective. In this study, we proposed a state-based probabilistic method for decoding hand positions during unilateral and bilateral movements using the ECoG signals recorded from the brain of Rhesus monkey. Approach. A customized electrode array was implanted subdurally in the right hemisphere of the brain covering from the primary motor cortex to the frontal cortex. Three different experimental paradigms were considered: ipsilateral, contralateral, and bilateral movements. During unilateral movement, the monkey was trained to get food with one hand, while during bilateral movement, the monkey used its left and right hands alternately to get food. To estimate the hand positions, a state-based probabilistic method was introduced which was based on the conditional probability of the hand movement state (i.e. idle, right hand movement, and left hand movement) and the conditional expectation of the hand position for each state. Moreover, a hybrid feature extraction method based on linear discriminant analysis and partial least squares (PLS) was introduced. Main results. The proposed method could successfully decode the hand positions during ipsilateral, contralateral, and bilateral movements and significantly improved the decoding performance compared to the conventional Kalman and PLS regression methods . The proposed hybrid feature extraction method was found to outperform both the PLS and PCA methods . Investigating the kinematic information of each frequency band shows that more informative frequency bands were (15–30 Hz) and (50–100 Hz) for ipsilateral and and (100–200 Hz) for contralateral movements. It is observed that ipsilateral movement was decoded better than contralateral movement for (5–15 Hz) and bands, while contralateral movements was decoded better for (30–200 Hz) and hfECoG (200–400 Hz) bands. Significance. Accurate decoding the bilateral movement using the ECoG recorded from one brain hemisphere is an important issue toward real-life applications of the brain-machine interface technologies.

S8-2. Brain mapping using brain-machine interface technology based on magnetoencephalographic and electrocorticographic signals

S8-2. Brain mapping using brain-machine interface technology based on magnetoencephalographic and electrocorticographic signals

Ultracompact Multielectrode Array for Neurological Monitoring.

Patients with paralysis, spinal cord injury, or amputated limbs could benefit from using brain–machine interface technology for communication and neurorehabilitation. In this study, a 32-channel three-dimensional (3D) multielectrode probe array was developed for the neural interface system of a brain–machine interface to monitor neural activity. A novel microassembly technique involving lead transfer was used to prevent misalignment in the bonding plane during the orthogonal assembly of the 3D multielectrode probe array. Standard microassembly and biopackaging processes were utilized to implement the proposed lead transfer technique. The maximum profile of the integrated 3D neural device was set to 0.50 mm above the pia mater to reduce trauma to brain cells. Benchtop tests characterized the electrical impedance of the neural device. A characterization test revealed that the impedance of the 3D multielectrode probe array was on average approximately 0.55 MΩ at a frequency of 1 KHz. Moreover, in vitro cytotoxicity tests verified the biocompatibility of the device. Subsequently, 3D multielectrode probe arrays were implanted in rats and exhibited the capability to record local field potentials and spike signals.

Decoding of muscle activity from the sensorimotor cortex in freely behaving monkeys

Remarkable advances have recently been made in the development of Brain-Machine Interface (BMI) technologies for restoring or enhancing motor function. However, the application of these technologies may be limited to patients in static conditions, as these developments have been largely based on studies of animals (e.g., non-human primates) in constrained movement conditions. The ultimate goal of BMI technology is to enable individuals to move their bodies naturally or control external devices without physical constraints. Here, we demonstrate accurate decoding of muscle activity from electrocorticogram (ECoG) signals in unrestrained, freely behaving monkeys. We recorded ECoG signals from the sensorimotor cortex as well as electromyogram signals from multiple muscles in the upper arm while monkeys performed two types of movements with no physical restraints, as follows: forced forelimb movement (lever-pull task) and natural whole-body movement (free movement within the cage). As in previous reports using restrained monkeys, we confirmed that muscle activity during forced forelimb movement was accurately predicted from simultaneously recorded ECoG data. More importantly, we demonstrated that accurate prediction of muscle activity from ECoG data was possible in monkeys performing natural whole-body movement. We found that high-gamma activity in the primary motor cortex primarily contributed to the prediction of muscle activity during natural whole-body movement as well as forced forelimb movement. In contrast, the contribution of high-gamma activity in the premotor and primary somatosensory cortices was significantly larger during natural whole-body movement. Thus, activity in a larger area of the sensorimotor cortex was needed to predict muscle activity during natural whole-body movement. Furthermore, decoding models obtained from forced forelimb movement could not be generalized to natural whole-body movement, which suggests that decoders should be built individually and according to different behavior types. These results contribute to the future application of BMI systems in unrestrained individuals.

Plugging Electronics Into Minds: Recent Trends and Advances in Neural Interface Microsystems

Despite rapid advancements in our electronics industry, connecting our minds to machines (e.g., robots and computers) through brain-machine interface (BMI) technologies remains an unfulfilled human ambition. Voice-free interaction with unplugged holographic displays, controlling a computer mouse hands free, downloading our minds onto artificial intelligence chips, and uploading knowledge or skills into our brain still reside in the realm of research or imagination.

A systematic review of the psychological factors that influence neurofeedback learning outcomes

Real-time functional magnetic resonance imaging (fMRI)-based neurofeedback represents the latest applied behavioural neuroscience methodology developed to train participants in the self-regulation of brain regions or networks. However, as with previous biofeedback approaches which rely on electroencephalography (EEG) or related approaches such as brain-machine interface technology (BCI), individual success rates vary significantly, and some participants never learn to control their brain responses at all. Given that these approaches are often being developed for eventual use in a clinical setting (albeit there is also significant interest in using NF for neuro-enhancement in typical populations), this represents a significant hurdle which requires more research. Here we present the findings of a systematic review which focused on how psychological variables contribute to learning outcomes in fMRI-based neurofeedback. However, as this is a relatively new methodology, we also considered findings from EEG-based neurofeedback and BCI. 271 papers were found and screened through PsycINFO, psycARTICLES, Psychological and Behavioural Sciences Collection, ISI Web of Science and Medline and 21 were found to contribute towards the aim of this survey. Several main categories emerged: Attentional variables appear to be of importance to both performance and learning, motivational factors and mood have been implicated as moderate predictors of success, while personality factors have mixed findings. We conclude that future research will need to systematically manipulate psychological variables such as motivation or mood, and to define clear thresholds for a successful neurofeedback effect. Non-responders need to be targeted for interventions and tested with different neurofeedback setups to understand whether their non-response is specific or general. Also, there is a need for qualitative evidence to understand how psychological variables influence participants throughout their training. This will help us to understand the subtleties of psychological effects over time. This research will allow interventions to be developed for non-responders and better selection procedures in future to improve the efficacy of neurofeedback.

Stepwise intervention for proximal and distal upper extremity motor function in patients with chronic stroke: A report of 2 cases

Upper extremity (UE) dysfunction is a common problem in patients with stroke. Recently, several neurorehabilitation approaches have been developed to improve the UE motor function (Langphorene P. Lancet Neurol , 2009). However, most approaches target either proximal or distal UE motor function, although interventions targeting both portions are necessary to restore functional UE in actual daily life. Therefore, we conducted stepwise intervention for both shoulder and finger function to improve UE motor function. Two stroke patients with severe hemiparetic stroke participated in the study. Firstly, we attempted to improve shoulder function using brain machine interface (BMI) technology for 7 days ( Fig. 1 ). After the shoulder BMI training, a combination of motor imagery and electrical stimulation (MI + ES) was conducted to improve finger function for 10 days ( Fig. 2 ). Motor function of the affected UE was assessed with motor items of the Fugl–Meyer assessment UE motor score (FMA-UE). FMA-UE consists of 4 categories (A: Shoulder/Elbow/Forearm; B: Wrist; C: Hand; D: Coordination) and has a maximum score of 66. The FMA-UE score increased remarkably after the stepwise intervention (Patient A: 17→33; Patient B: 11→25). The effect of shoulder BMI training is shown in Fig. 3 . It became possible for patients to raise their arms above the shoulder position after the shoulder BMI training. Patients’ UE motor function was also further improved by the subsequent MI + ES intervention. The improvement in UE motor function with our stepwise intervention for proximal and distal motor functions greatly exceeded the minimal clinically important difference in the subjects of this study.

Inside the Mind's Eye: An International Perspective on Data Privacy Law in the Age of Brain-Machine Interfaces

Brain machine interface (BMI) technologies extract activity and translate that activity into commands that control external devices. BMIs have been used by patients with physical disabilities to allow for thought-controlled use of computers, prosthetic limbs, and robotic arms. In the commercial context, recreational BMIs are joining the internet of things through applications in video gaming and beyond. BMI systems collect and interpret large amounts of data obtained directly from the brain. This brain data' contains highly intimate information about individual level intentions, cognition, and states of health and disease. Public and private sector entities will have access and control over data, raising concerns over personal data privacy and protection. This paper examines personal data privacy regimes in relation to data across three jurisdictions: Canada, the United States, and the European Union. In relation to the governance of personal data in the private sector, the United States adopts a self-regulatory mechanism, while the European Union assumes a rights-based approach, with Canada's position being intermediate to the two extremes. All three jurisdictions seem to fall short when considering data privacy. The narrow coverage of the United States privacy legislation will exclude most data, while aspects of the EU's rights-based framework will overcompensate and hamper fruitful implementations of BMI technology. Canada's middle-ground approach seems to leave certain gaps in protection for data. Pertaining to government agencies, all three jurisdictions set a low threshold, providing for unfettered governmental access in the name of law enforcement and national security interests. The paper concludes with policy recommendations that recognize the need for stronger personal data privacy protection in the age of BMIs, while balancing the need to foster continued innovation in the BMI technology space. Note: In press at: Albany Law Journal of Science and Technology.