Collaborative approach in the development of high-performance brain-computer interfaces for a neuroprosthetic arm: translation from animal models to human control.
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ABSTRACT:
SUBMITTER: Collinger JL
PROVIDER: S-EPMC3929226 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of driving these movements reliably. Vision-guided robotic assistance is one possible way to improve BMI performance. We describe a method of shared control where the user controls a prosthetic arm using a BMI and receives assistance with positioning the hand when it approaches an object.Two human subjects with tetraplegia used a robotic arm to complete object transport tasks with and without shared control. The shared control system was designed to provide a balance between BMI-derived intention and computer assistance. An autonomous robotic grasping system identified and tracked objects and defined stable grasp positions for these objects. The system identified when the user intended to interact with an object based on the BMI-controlled movements of the robotic arm. Using shared control, BMI controlled movements and autonomous grasping commands were blended to ensure secure grasps.Both subjects were more successful on object transfer tasks when using shared control compared to BMI control alone. Movements made using shared control were more accurate, more efficient, and less difficult. One participant attempted a task with multiple objects and successfully lifted one of two closely spaced objects in 92 % of trials, demonstrating the potential for users to accurately execute their intention while using shared control.Integration of BMI control with vision-guided robotic assistance led to improved performance on object transfer tasks. Providing assistance while maintaining generalizability will make BMI systems more attractive to potential users.NCT01364480 and NCT01894802 .
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Project description:Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients' own paralyzed limbs. To date, human studies have demonstrated an "all-or-none" type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate wrist flexion states that are intermediate to training levels. These innovations provide the groundwork for enabling enhanced and more natural fine motor control of paralyzed limbs by BCI-FES neuroprosthetics.
| S-EPMC5567199 | biostudies-literature
Project description:The incidence of pain in cancer patients during diagnosis and treatment is exceedingly high. Although advances in cancer detection and therapy have improved patient prognosis, cancer and its treatment-associated pain have gained clinical prominence. The biological mechanisms involved in cancer-related pain are multifactorial; different processes for pain may be responsible depending on the type and anatomic location of cancer. Animal models of cancer-related pain have provided mechanistic insights into the development and process of pain under a dynamic molecular environment. However, while cancer-evoked nociceptive responses in animals reflect some of the patients' symptoms, the current models have failed to address the complexity of interactions within the natural disease state. Although there has been a recent convergence of the investigation of carcinogenesis and pain neurobiology, identification of new targets for novel therapies to treat cancer-related pain requires standardization of methodologies within the cancer pain field as well as across disciplines. Limited success of translation from preclinical studies to the clinic may be due to our poor understanding of the crosstalk between cancer cells and their microenvironment (e.g., sensory neurons, infiltrating immune cells, stromal cells etc.). This relatively new line of inquiry also highlights the broader limitations in translatability and interpretation of basic cancer pain research. The goal of this review is to summarize recent findings in cancer pain based on preclinical animal models, discuss the translational benefit of these discoveries, and propose considerations for future translational models of cancer pain.
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Project description:Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.
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Project description:BackgroundParalysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface.MethodsWe implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480.FindingsThe participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported.InterpretationWith continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living.FundingDefense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute.
| S-EPMC3641862 | biostudies-literature
Project description:ObjectiveTo identify Common Data Elements (CDEs) in eligibility criteria of multiple clinical trials studying the same disease using a human-computer collaborative approach.DesignA set of free-text eligibility criteria from clinical trials on two representative diseases, breast cancer and cardiovascular diseases, was sampled to identify disease-specific eligibility criteria CDEs. In this proposed approach, a semantic annotator is used to recognize Unified Medical Language Systems (UMLSs) terms within the eligibility criteria text. The Apriori algorithm is applied to mine frequent disease-specific UMLS terms, which are then filtered by a list of preferred UMLS semantic types, grouped by similarity based on the Dice coefficient, and, finally, manually reviewed.MeasurementsStandard precision, recall, and F-score of the CDEs recommended by the proposed approach were measured with respect to manually identified CDEs.ResultsAverage precision and recall of the recommended CDEs for the two diseases were 0.823 and 0.797, respectively, leading to an average F-score of 0.810. In addition, the machine-powered CDEs covered 80% of the cardiovascular CDEs published by The American Heart Association and assigned by human experts.ConclusionIt is feasible and effort saving to use a human-computer collaborative approach to augment domain experts for identifying disease-specific CDEs from free-text clinical trial eligibility criteria.
| S-EPMC3524400 | biostudies-literature