Brain-machine Interface Research Research Articles

A Cyto-silicon Hybrid System with On-chip Closed-loop Modulation.

We introduce a bioelectronic interface between biological electrogenic cells and a mixed-signal CMOS integrated circuit with an array of surface electrodes, where not only is the CMOS electrode array capable of electrophysiological recording and stimulation of the cells with 1,024 recording and stimulation channels, but it can also provide low-latency artificial signal pathways from cells it records to cells it stimulates. This on-chip closed-loop modulation has an intrinsic latency less than 5 μs. To demonstrate the utility of the on-chip closed loop modulation as an artificial feedback pathway between biological cells, we develop a silicon-cardiomyocyte self-sustained oscillator with a tunable frequency to which both the relevant part of the CMOS chip and cells are locked, and also a silicon-neuron interface with a silicon inhibitory connection between neuronal cells. This line of cyto-silicon hybrid system, where the boundary between biological and semiconductor systems is blurred, may find applications in prosthesis, brain-machine interface, and fundamental biology research.

Advances in Brain-Machine Interface Research: 2023 Update

Introduction: There have been many studies proposing new functionalities that would increase the capacity of the brain-machine interface and significantly improve the quality of life of people with reduced mobility, such as the combination with virtual reality, which would provide a safe navigation experience. There are still many topics that have not yet been fully explored in the brain-machine interface. Objectives: List the most recent and main discoveries on the subject and showing the importance of these discoveries. Methodology: This study is a literature review, which used the DeCS/MeSH descriptors "Brain-Computer Interfaces","neural pathways" and "brain mapping" intertwined with the Boolean operators "AND" or "OR", to search the PubMed, ScienceDirect and VHL databases. Results: Brain-machine interfaces have shown great success in decoding intended movements from neural activity recorded in the primary motor cortex and then restoring motor control of their own hand in people with tetraplegia. The big problem lies in the lack of tactile feedback, which undermines this significant progress in neurorehabilitation. Some brain-machine interfaces provide a direct and intuitive means of communication between external devices and people, eliminating the need for external controls. They record brain activity and translate control commands to an output device. Conclusion: The data is still limited, and the information obtained on the Brain-Machine interface is not close to an outcome. There is still a need for more work to have a broader understanding of the subject.

Hippocampal CA1 and CA3 neural recording in the human brain: validation of depth electrode placement through high-resolution imaging and electrophysiology.

Intracranial human brain recordings typically utilize recording systems that do not distinguish individual neuron action potentials. In such cases, individual neurons are not identified by location within functional circuits. In this paper, verified localization of singly recorded hippocampal neurons within the CA3 and CA1 cell fields is demonstrated. Macro-micro depth electrodes were implanted in 23 human patients undergoing invasive monitoring for identification of epileptic seizure foci. Individual neurons were isolated and identified via extracellular action potential waveforms recorded via macro-micro depth electrodes localized within the hippocampus. A morphometric survey was performed using 3T MRI scans of hippocampi from the 23 implanted patients, as well as 46 normal (i.e., nonepileptic) patients and 26 patients with a history of epilepsy but no history of depth electrode placement, which provided average dimensions of the hippocampus along typical implantation tracks. Localization within CA3 and CA1 cell fields was tentatively assigned on the basis of recording electrode site, stereotactic positioning of the depth electrode in comparison with the morphometric survey, and postsurgical MRI. Cells were selected as candidate CA3 and CA1 principal neurons on the basis of waveform and firing rate characteristics and confirmed within the CA3-to-CA1 neural projection pathways via measures of functional connectivity. Cross-correlation analysis confirmed that nearly 80% of putative CA3-to-CA1 cell pairs exhibited positive correlations compatible with feed-forward connection between the cells, while only 2.6% exhibited feedback (inverse) connectivity. Even though synchronous and long-latency correlations were excluded, feed-forward correlation between CA3-CA1 pairs was identified in 1071 (26%) of 4070 total pairs, which favorably compares to reports of 20%-25% feed-forward CA3-CA1 correlation noted in published animal studies. This study demonstrates the ability to record neurons in vivo from specified regions and subfields of the human brain. As brain-machine interface and neural prosthetic research continues to expand, it is necessary to be able to identify recording and stimulation sites within neural circuits of interest.

Collecting Data and the Status of the Research Subject in Brain-Machine Interface Research in Chronic Stroke Rehabilitation

Brain-Machine Interface (BMI) has moved to the forefront of neuroscientific trials as it not only establishes a direct communication pathway between the brain and an external device but enables the measuring of brain activity in research subjects whereby data is collected. With special emphasis on the somatechnics of BMI, this article analyses the relationship between this data collection process and actual patient care in chronic stroke rehabilitation. In doing so, it focuses on behaviour patterns associated with the use of specific technology that enables the scientist to use certain techniques in order to establish a hierarchy among research participants. In this hierarchy the scientist assumes a superior status to the research subject, an occurrence that is partially supported by using BMI technology and partially initiated by the need to generate data. Based on ethnographic studies of BMI research in hospitals and laboratories, the article outlines how this hierarchical structure in neuroscientific research leads to ethical questions in the quality of actual patient care. It continues to explore how patient care is affected mainly as a result of the changing status of the research subject from an initial bio-technical form that produces data in real time and whose brain is understood as an epistemic object to the patient being considered a ‘perceptible’ or ‘plausible gestalt’ ( Lindemann 2009 ) and therefore a social person. Although focus lies on the status of the research subject as part of actual patient care, scientific data generation remains indispensable for medical advancement; that is curing the patient. The article draws conclusion by addressing this very problem and therewith intends to contribute empirically based insights to a socially relevant discourse on neuroscientific trials. 1

Workshops of the seventh international brain-computer interface meeting: not getting lost in translation

ABSTRACTThe Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.

Development of a Cognitive Brain-Machine Interface Based on a Visual Imagery Method.

In the field of brain-machine interface (BMI) research, the development of cognitive BMI is a hot topic because it may lead to more intuitive and goal-directed findings than existing BMI technology. In this study, we devised a "visual-imagery method," which enables visual imaging of the operation of a target. We also investigated an "inner-speech method," which comprised internal pronunciation of words without emitting sounds, and an "inner-speech + visual-imagery method," which combined the two methods. When only the high $\gamma$ band (60-120 Hz) power in the prefrontal cortex was used, the average accuracy of the 15 participants, with 20-fold crossvalidation, was 81.3% in inner speech, 84.6% in visual imagery, and 83.2% in inner speech + visual imagery. This study also found that the frontal pole was the most useful region in the prefrontal cortex.

S3-4. Independent component analysis derived features for electrocorticographic brain-machine interfaces

Recent progress in Brain Machine Interface (BMI) research using signals from subdural electrocorticographical (ECoG) arrays implanted over primary motor cortex has demonstrated the potential for successful decoding of participant’s arm or hand movement. Concurrent advances in wireless implantable electrode technology are further shortening the gap between existing systems and real functioning rehabilitative neural prosthetics for use by patients with motor system disorders. However the long-term stability and effectiveness of BMI technologies depends crucially on the stability and effectiveness of the feature extraction and decoding algorithms used to translate the neural signals into behavioural commands. Given the limited density and coverage of ECoG channel arrays, it is critical to reliably extract as much of the available information as possible. While some success has been achieved using channel-based features, we have recently shown that improvement may be gained by first applying the Independent Component Analysis (ICA) method to the ECoG channel data to separate and isolate relevant cortical signals. In this talk, we will present some preliminary results of application of ICA to ECoG data for the derivation of features for BMI applications and comparison to standard approaches. We discuss the interpretation of the ICA features and suggest directions for future improvements.

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

The Sixth International Brain–Computer Interface (BCI) Meeting was held 30 May–3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain–machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

Categorical discrimination of human body parts by magnetoencephalography.

Humans recognize body parts in categories. Previous studies have shown that responses in the fusiform body area (FBA) and extrastriate body area (EBA) are evoked by the perception of the human body, when presented either as whole or as isolated parts. These responses occur approximately 190 ms after body images are visualized. The extent to which body-sensitive responses show specificity for different body part categories remains to be largely clarified. We used a decoding method to quantify neural responses associated with the perception of different categories of body parts. Nine subjects underwent measurements of their brain activities by magnetoencephalography (MEG) while viewing 14 images of feet, hands, mouths, and objects. We decoded categories of the presented images from the MEG signals using a support vector machine (SVM) and calculated their accuracy by 10-fold cross-validation. For each subject, a response that appeared to be a body-sensitive response was observed and the MEG signals corresponding to the three types of body categories were classified based on the signals in the occipitotemporal cortex. The accuracy in decoding body-part categories (with a peak at approximately 48%) was above chance (33.3%) and significantly higher than that for random categories. According to the time course and location, the responses are suggested to be body-sensitive and to include information regarding the body-part category. Finally, this non-invasive method can decode category information of a visual object with high temporal and spatial resolution and this result may have a significant impact in the field of brain–machine interface research.

Deafferented controllers: a fundamental failure mechanism in cortical neuroprosthetic systems.

Brain-machine interface (BMI) research assumes that patients with disconnected neural pathways could naturally control a prosthetic device by volitionally modulating sensorimotor cortical activity usually responsible for movement coordination. However, computational approaches to motor control challenge this view. This article examines the predictions of optimal feedback control (OFC) theory on the effects that loss of motor output and sensory feedback have on the normal generation of motor commands. Example simulations of unimpaired, totally disconnected, and deafferented controllers illustrate that by neglecting the dynamic interplay between motor commands, state estimation, feedback and behavior, current BMI systems face translational challenges rooted in a debatable assumption and experimental models of limited validity.

Creating the brain and interacting with the brain: an integrated approach to understanding the brain.

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the 'understanding the brain by creating the brain' approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain-machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.

Degraded EEG decoding of wrist movements in absence of kinaesthetic feedback.

A major assumption of brain–machine interface research is that patients with disconnected neural pathways can still volitionally recall precise motor commands that could be decoded for naturalistic prosthetic control. However, the disconnected condition of these patients also blocks kinaesthetic feedback from the periphery, which has been shown to regulate centrally generated output responsible for accurate motor control. Here, we tested how well motor commands are generated in the absence of kinaesthetic feedback by decoding hand movements from human scalp electroencephalography in three conditions: unimpaired movement, imagined movement, and movement attempted during temporary disconnection of peripheral afferent and efferent nerves by ischemic nerve block. Our results suggest that the recall of cortical motor commands is impoverished in the absence of kinaesthetic feedback, challenging the possibility of precise naturalistic cortical prosthetic control. Hum Brain Mapp 36:643–654, 2015. © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

Brain-machine interfaces can accelerate clarification of the principal mysteries and real plasticity of the brain.

This perspective emphasizes that the brain-machine interface (BMI) research has the potential to clarify major mysteries of the brain and that such clarification of the mysteries by neuroscience is needed to develop BMIs. I enumerate five principal mysteries. The first is “how is information encoded in the brain?” This is the fundamental question for understanding what our minds are and is related to the verification of Hebb’s cell assembly theory. The second is “how is information distributed in the brain?” This is also a reconsideration of the functional localization of the brain. The third is “what is the function of the ongoing activity of the brain?” This is the problem of how the brain is active during no-task periods and what meaning such spontaneous activity has. The fourth is “how does the bodily behavior affect the brain function?” This is the problem of brain-body interaction, and obtaining a new “body” by a BMI leads to a possibility of changes in the owner’s brain. The last is “to what extent can the brain induce plasticity?” Most BMIs require changes in the brain’s neuronal activity to realize higher performance, and the neuronal operant conditioning inherent in the BMIs further enhances changes in the activity.

Concurrent stable and unstable cortical correlates of human wrist movements

Cortical activity has been shown to correlate with different parameters of movement. However, the dynamic properties of cortico-motor mappings still remain unexplored in humans. Here, we show that during the repetition of simple stereotyped wrist movements both stable and unstable correlates simultaneously emerge in human sensorimotor cortex. Using visual feedback of wrist movement target inferred online from MEG, we assessed the dynamics of the tuning properties of two neuronal signals: the MEG signal below 1.6 Hz and within the 4 to 6 Hz range. We found that both components are modulated by wrist movement allowing for closed-loop inference of movement targets. Interestingly, while tuning of 4 to 6 Hz signals remained stable over time leading to stable inference of movement target using a static classifier, the tuning of cortical signals below 1.6 Hz significantly changed resulting in steadily decreasing inference accuracy. Our findings demonstrate that non-invasive neuronal population signals in human sensorimotor cortex can reflect a stable correlate of voluntary movements. Hence, we provide first evidence for a stable control signal in non-invasive human brain-machine interface research. However, as not all neuronal signals initially tuned to movement were stable across days, a careful selection of features for real-life applications seems to be mandatory.

Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future

The Fifth International Brain-Computer Interface (BCI) Meeting met on 3–7 June 2013 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues for future research and development.

A pelvic implant orthosis in rodents, for spinal cord injury rehabilitation, and for brain machine interface research: Construction, surgical implantation and validation

BackgroundRodents are important model systems used to explore spinal cord injury (SCI) and rehabilitation, and brain machine interfaces (BMI). We present a new method to provide mechanical interaction for BMI and rehabilitation in rat models of SCI. New methodWe present the design and implantation procedures for a pelvic orthosis that allows direct force application to the skeleton in brain machine interface and robot rehabilitation applications in rodents. We detail the materials, construction, machining, surgery and validation of the device. ResultsWe describe the statistical validation of the implant procedures by comparing stepping parameters of 8 rats prior to and after implantation and surgical recovery. An ANOVA showed no effects of the implantation on stepping. Paired tests in the individual rats also showed no effect in 7/8 rats and minor effects in the last rat, within the group's variance. Comparison with existing methodsOur method allows interaction with rats at the pelvis without any perturbation of normal stepping in the intact rat. The method bypasses slings, and cuffs, avoiding cuff or slings squeezing the abdomen, or other altered sensory feedback. Our implant osseointegrates, and thus allows an efficient high bandwidth mechanical coupling to a robot. The implants support quadrupedal training and are readily integrated into either treadmill or overground contexts. ConclusionsOur novel device and procedures support a range of novel experimental designs and motor tests for rehabilitative and augmentation devices in intact and SCI model rats, with the advantage of allowing direct force application at the pelvic bones.

Direct Brain Control and Communication in Paralysis

Despite considerable growth in the field of brain-computer or brain-machine interface (BCI/BMI) research reflected in several hundred publications each year, little progress was made to enable patients in complete locked-in state (CLIS) to reliably communicate using their brain activity. Independent of the invasiveness of the BCI systems tested, no sustained direct brain control and communication was demonstrated in a patient in CLIS so far. This suggested a more fundamental theoretical problem of learning and attention in brain communication with BCI/BMI, formulated in the extinction-of-thought hypothesis. While operant conditioning and goal-directed thinking seems impaired in complete paralysis, classical conditioning of brain responses might represent the only alternative. First experimental studies in CLIS using semantic conditioning support this assumption. Evidence that quality-of-life in locked-in-state is not as limited and poor as generally believed draise doubts that "patient wills" or "advanced directives"signed long-before the locked-in-state are useful. On the contrary, they might be used as an excuse to shorten anticipated long periods of care for these patients avoiding associated financial and social burdens. Current state and availability of BCI/BMI systems urge a broader societal discourse on the pressing ethical challenges associated with the advancements in neurotechnology and BCI/BMI research.

Decoding Intra-Limb and Inter-Limb Kinematics During Treadmill Walking From Scalp Electroencephalographic (EEG) Signals

Brain-machine interface (BMI) research has largely been focused on the upper limb. Although restoration of gait function has been a long-standing focus of rehabilitation research, surprisingly very little has been done to decode the cortical neural networks involved in the guidance and control of bipedal locomotion. A notable exception is the work by Nicolelis' group at Duke University that decoded gait kinematics from chronic recordings from ensembles of neurons in primary sensorimotor areas in rhesus monkeys. Recently, we showed that gait kinematics from the ankle, knee, and hip joints during human treadmill walking can be inferred from the electroencephalogram (EEG) with decoding accuracies comparable to those using intracortical recordings. Here we show that both intra- and inter-limb kinematics from human treadmill walking can be achieved with high accuracy from as few as 12 electrodes using scalp EEG. Interestingly, forward and backward predictors from EEG signals lagging or leading the kinematics, respectively, showed different spatial distributions suggesting distinct neural networks for feedforward and feedback control of gait. Of interest is that average decoding accuracy across subjects and decoding modes was ~0.68±0.08, supporting the feasibility of EEG-based BMI systems for restoration of walking in patients with paralysis.

BrainLiner: A Platform for Neurophysiological Data Sharing and Manipulation

BrainLiner: A Platform for Neurophysiological Data Sharing and Manipulation

Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

This paper presents “Craniux,” an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.