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Detecção de potenciais evocados P300 para ativação de uma interface cérebro-máquina.; Brain-computer interface based on P300 event-related potential detection.

Godói, Antônio Carlos Bastos de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 20/07/2010 PT
Relevância na Pesquisa
86.34%
Interfaces cérebro-computador ou Interfaces cérebro-máquina (BCIs/BMIs do inglês Brain-computer interface/Brain-machine interface) são dispositivos que permitem ao usuário interagir com o ambiente ao seu redor sem que seja necessário ativar seus músculos esqueléticos. Estes dispositivos são de extrema valia para indivíduos portadores de deficiências motoras. Esta dissertação ambiciona revisar a literatura acerca de BMIs e expor diferentes técnicas de pré-processamento, extração de características e classificação de sinais neurofisiológicos. Em particular, uma maior ênfase será dada à Máquina de vetor de suporte (SVM do inglês Support-Vector machine), método de classificação baseado no princípio da minimização do risco estrutural. Será apresentado um estudo de caso, que ilustra o funcionamento de uma BMI, a qual permite ao usuário escolher um dentre seis objetos mostrados em uma tela de computador. Esta capacidade da BMI é conseqüência da implementação, através da SVM de um sistema capaz de detectar o potencial evocado P300 nos sinais de eletroencefalograma (EEG). A simulação será realizada em Matlab usando, como sinais de entrada, amostras de EEG de quatro indivíduos saudáveis e quatro deficientes. A análise estatística mostrou que o bom desempenho obtido pela BMI (80...

Bridging present and future of brain-computer interfaces: an assessment of impacts

Velloso, Gabriel Teykal
Fonte: IET Publicador: IET
Tipo: Relatório
Publicado em /09/2012 ENG
Relevância na Pesquisa
66.08%
Based on the report for the “Project III” unit of the PhD programme on Technology Assessment under the supervision of Prof. António B. Moniz. This report was discussed also at the 2nd Winter School on Technology Assessment held at Universidade Nova de Lisboa, Caparica Campus, Portugal on December 2011.; Technology assessment is essentially a systematic method used to investigate technology developments and assess their potential impacts on society. The assessment of emerging technologies, however, requires special attention. To address technologies at early stages of development, Constructive Technology Assessment (CTA) is considered to be one of the best options to bypass the Collingridge dilemma - which fundamentally states that controlling the direction of a technology’s development is very hard. Technologies at early stages of development might appear to be unorganized, chaotic and with high level of uncertainty on future paths to take. Future Oriented Technology Analysis (FTA) represents any systematic process to produce judgments about the characteristics of emerging technologies, its development pathways, and potential future impacts. Technology Assessment is considered to be one of three subjects which form the umbrella concept of FTA. The technology assessed on this project...

Usage of the ACT3D Robot in a Brain Machine Interface for Hand Opening and Closing in Stroke Survivors

Yao, Jun; Sheaff, Clay; Dewald, Julius P. A.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 14/01/2008 EN
Relevância na Pesquisa
66.14%
At six months after brain injury, about 65% of stroke survivors have been shown to be unable to incorporate the affected hand into activities of daily living (ADL). Using a reliable Brain-Machine-Interface (BMI) together with Neural Electronic Stimulation (NES) is a possible solution for the restoration of hand function in severely impaired hemiparetic stroke survivors. However, discoordination, i.e. the abnormal coupling between adjacent joints, causes an expected reduction in the performance of BMI algorithms. In this study, we test whether the active support of an ACT3D robot can increase the performance of two brain-machine-interface (BMI) algorithms in separating the subject’s intention to open or close the impaired hand during reach. Improvement in recognition rate was obtained in 4 chronic hemiparetic stroke subjects when support from the robot was available. Further analysis on one subject suggests that such an improvement is related to quantitative changes in cortical activity. This result suggests that the ACT3D robot can be used to train severely impaired stroke subjects to use a BMI-controlled NES device.

Towards Intelligent Environments: An Augmented Reality–Brain–Machine Interface Operated with a See-Through Head-Mount Display

Takano, Kouji; Hata, Naoki; Kansaku, Kenji
Fonte: Frontiers Research Foundation Publicador: Frontiers Research Foundation
Tipo: Artigo de Revista Científica
Publicado em 20/04/2011 EN
Relevância na Pesquisa
66.14%
The brain–machine interface (BMI) or brain–computer interface is a new interface technology that uses neurophysiological signals from the brain to control external machines or computers. This technology is expected to support daily activities, especially for persons with disabilities. To expand the range of activities enabled by this type of interface, here, we added augmented reality (AR) to a P300-based BMI. In this new system, we used a see-through head-mount display (HMD) to create control panels with flicker visual stimuli to support the user in areas close to controllable devices. When the attached camera detects an AR marker, the position and orientation of the marker are calculated, and the control panel for the pre-assigned appliance is created by the AR system and superimposed on the HMD. The participants were required to control system-compatible devices, and they successfully operated them without significant training. Online performance with the HMD was not different from that using an LCD monitor. Posterior and lateral (right or left) channel selections contributed to operation of the AR–BMI with both the HMD and LCD monitor. Our results indicate that AR–BMI systems operated with a see-through HMD may be useful in building advanced intelligent environments.

A Chronic Generalized Bi-directional Brain-Machine Interface

Rouse, Adam; Stanslaski, Scott; Cong, Peng; Jensen, Randy; Afshar, Pedram; Ullestad, Dave; Moran, Dan; Denison, Tim
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
66.14%
A bi-directional neural interface (NI) system was designed and built by incorporating a novel neural recording and processing subsystem into a commercially approved neural stimulator. The NI system prototype leverages the system infrastructure from a market-approved neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing approved therapy capabilities, the device adds key elements to facilitate chronic clinical research, such as four channels of ECoG/LFP amplification and spectral analysis, a three axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in-vivo non-human primate model for brain control of a computer cursor (i.e., brain machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson’s). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques...

Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics

Cherian, A.; Krucoff, M. O.; Miller, L. E.
Fonte: American Physiological Society Publicador: American Physiological Society
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
66.12%
During typical movements, signals related to both the kinematics and kinetics of movement are mutually correlated, and each is correlated to some extent with the discharge of neurons in the primary motor cortex (M1). However, it is well known, if not always appreciated, that causality cannot be inferred from correlations. Although these mutual correlations persist, their nature changes with changing postural or dynamical conditions. Under changing conditions, only signals directly controlled by M1 can be expected to maintain a stable relationship with its discharge. If one were to rely on noncausal correlations for a brain-machine interface, its generalization across conditions would likely suffer. We examined this effect, using multielectrode recordings in M1 as input to linear decoders of both end point kinematics (position and velocity) and proximal limb myoelectric signals (EMG) during reaching. We tested these decoders across tasks that altered either the posture of the limb or the end point forces encountered during movement. Within any given task, the accuracy of the kinematic predictions tended to be somewhat better than the EMG predictions. However, when we used the decoders developed under one task condition to predict the signals recorded under different postural or dynamical conditions...

Volitional control of single cortical neurons in a brain-machine interface

Moritz, Chet T.; Fetz, Eberhard E.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
66.07%
Volitional control of cortical activity is relevant for optimizing control signals for neuroprosthetic devices. We explored the control of firing rates of single cortical cells in two M. Nemestrina monkeys by providing visual feedback of neural activity and rewarding changes in cell rates. During ‘brain control’ sessions monkeys modulated the activity of each of 246 cells to acquire targets requiring high or low discharge rates. Cell control improved by more than 2-fold from the beginning of practice to peak performance. Activity of all cells was modulated substantially more during brain control than during wrist movements. When recording stability permitted, monkeys practiced controlling activity of the same cells across multiple days. Performance improved substantially for 27 of 36 cells when practicing brain control across days. Monkeys maintained discharge rates within each target for 1s, but could maintain rates for up to 3s for some cells. Monkeys performed the brain control task equally well using cells recorded from pre-central cortex compared to cells in post-central cortex, and independently of any directional tuning. These findings demonstrate that arbitrary single cortical neurons, regardless of the strength of directional tuning...

Brain–machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm

Lee, Jong-Hwan; Ryu, Jeongwon; Jolesz, Ferenc A.; Cho, Zang-Hee; Yoo, Seung-Schik
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
66.09%
Real-time functional MRI (rtfMRI) has been used as a basis for brain–computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain–machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects’ thought processes was possible using the rtfMRI-based BMI trials.

A Non-Adhesive Solid-Gel Electrode for a Non-Invasive Brain–Machine Interface

Toyama, Shigeru; Takano, Kouji; Kansaku, Kenji
Fonte: Frontiers Research Foundation Publicador: Frontiers Research Foundation
Tipo: Artigo de Revista Científica
Publicado em 18/07/2012 EN
Relevância na Pesquisa
66.09%
A non-invasive brain–machine interface (BMI) or brain–computer interface is a technology for helping individuals with disabilities and utilizes neurophysiological signals from the brain to control external machines or computers without requiring surgery. However, when applying electroencephalography (EEG) methodology, users must place EEG electrodes on the scalp each time, and the development of easy-to-use electrodes for clinical use is required. In this study, we developed a conductive non-adhesive solid-gel electrode for practical non-invasive BMIs. We performed basic material testing, including examining the volume resistivity, viscoelasticity, and moisture-retention properties of the solid-gel. Then, we compared the performance of the solid-gel, a conventional paste, and an in-house metal-pin-based electrode using impedance measurements and P300-BMI testing. The solid-gel was observed to be conductive (volume resistivity 13.2 Ωcm) and soft (complex modulus 105.4 kPa), and it remained wet for a prolonged period (>10 h) in a dry environment. Impedance measurements revealed that the impedance of the solid-gel-based and conventional paste-based electrodes was superior to that of the pin-based electrode. The EEG measurement suggested that the signals obtained with the solid-gel electrode were comparable to those with the conventional paste-based electrode. Moreover...

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface

Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 06/02/2014 EN
Relevância na Pesquisa
66.11%
In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies...

Towards the Implementation of Non-Invasive Brain Machine Interface Control on a Rehabilitative Robotic Upper Limb Exoskeleton

French, James Andrew
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Thesis; Text Formato: application/pdf
ENG
Relevância na Pesquisa
96.12%
This thesis presents an upper limb robotic exoskeleton and a method for actively engaging stroke patients during robotic rehabilitation by incorporating the patient's intentions into the robot's control scheme. Robotic rehabilitation is effective for sensorimotor training of patients who have some residual movement in their impaired limbs, but those with severe impairment benefit less due to the difficulty in determining movement intent. Intent recognition is necessary for providing an appropriate level of robotic assistance. Through implementation of a non-invasive brain-machine interface (BMI) using electroencephalography (EEG), the patient's movement intent is transmitted to the MAHI Exo-II, an upper extremity robotic exoskeleton. This thesis first validates the exoskeleton as the proper choice for clinical implementation by assessing its dynamics and performance characteristics as compared to other state-of-the-art designs. Then, results of pre-clinical trials with the BMI are described, laying the foundation for improved robotic rehabilitation and a better understanding of neural plasticity.

Future developments in brain-machine interface research

Lebedev, Mikhail A; Tate, Andrew J; Hanson, Timothy L; Li, Zheng; O'Doherty, Joseph E; Winans, Jesse A; Ifft, Peter J; Zhuang, Katie Z; Fitzsimmons, Nathan A; Schwarz, David A; Fuller, Andrew M; An, Je Hi; Nicolelis, Miguel A L
Fonte: Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo Publicador: Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo
Tipo: Artigo de Revista Científica
Publicado em /06/2011 EN
Relevância na Pesquisa
66.3%
Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Future developments in brain-machine interface research

Lebedev,Mikhail A.; Tate,Andrew J.; Hanson,Timothy L.; Li,Zheng; O'Doherty,Joseph E.; Winans,Jesse A.; Ifft,Peter J.; Zhuang,Katie Z.; Fitzsimmons,Nathan A.; Schwarz,David A.; Fuller,Andrew M.; An,Je Hi; Nicolelis,Miguel A. L.
Fonte: Faculdade de Medicina / USP Publicador: Faculdade de Medicina / USP
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2011 EN
Relevância na Pesquisa
96.33%
Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Detection of movement intention using EEG in a human-robot interaction environment

Lana,Ernesto Pablo; Adorno,Bruno Vilhena; Tierra-Criollo,Carlos Julio
Fonte: Sociedade Brasileira de Engenharia Biomédica Publicador: Sociedade Brasileira de Engenharia Biomédica
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2015 EN
Relevância na Pesquisa
66.22%
Introduction: This paper presents a detection method for upper limb movement intention as part of a brain-machine interface using EEG signals, whose final goal is to assist disabled or vulnerable people with activities of daily living. Methods EEG signals were recorded from six naïve healthy volunteers while performing a motor task. Every volunteer remained in an acoustically isolated recording room. The robot was placed in front of the volunteers such that it seemed to be a mirror of their right arm, emulating a Brain Machine Interface environment. The volunteers were seated in an armchair throughout the experiment, outside the reaching area of the robot to guarantee safety. Three conditions are studied: observation, execution, and imagery of right arm’s flexion and extension movements paced by an anthropomorphic manipulator robot. The detector of movement intention uses the spectral F test for discrimination of conditions and uses as feature the desynchronization patterns found on the volunteers. Using a detector provides an objective method to acknowledge for the occurrence of movement intention. Results When using four realizations of the task, detection rates ranging from 53 to 97% were found in five of the volunteers when the movement was executed...

Future developments in brain-machine interface research

Lebedev, Mikhail A.; Tate, Andrew J.; Hanson, Timothy L.; Li, Zheng; O'Doherty, Joseph E.; Winans, Jesse A.; Ifft, Peter J.; Zhuang, Katie Z.; Fitzsimmons, Nathan A.; Schwarz, David A.; Fuller, Andrew M.; An, Je Hi; Nicolelis, Miguel A. L.
Fonte: Universidade de São Paulo. Faculdade de Medicina Publicador: Universidade de São Paulo. Faculdade de Medicina
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; ; ; ; Formato: application/pdf
Publicado em 01/01/2011 ENG
Relevância na Pesquisa
96.33%
Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Brain-Machine Interface for Reaching: Accounting for Target Size, Multiple Motor Plans, and Bimanual Coordination

Ifft, Peter James
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação
Publicado em //2014
Relevância na Pesquisa
86.14%

Brain-machine interfaces (BMIs) offer the potential to assist millions of people worldwide suffering from immobility due to loss of limbs, paralysis, and neurodegenerative diseases. BMIs function by decoding neural activity from intact cortical brain regions in order to control external devices in real-time. While there has been exciting progress in the field over the past 15 years, the vast majority of the work has focused on restoring of motor function of a single limb. In the work presented in this thesis, I first investigate the expanded role of primary sensory (S1) and motor (M1) cortex during reaching movements. By varying target size during reaching movements, I discovered the cortical correlates of the speed-accuracy tradeoff known as Fitts' law. Similarly, I analyzed cortical motor processing during tasks where the motor plan is quickly reprogrammed. In each study, I found that parameters relevant to the reach, such as target size or alternative movement plans, could be extracted by neural decoders in addition to simple kinematic parameters such as velocity and position. As such, future BMI functionality could expand to account for relevant sensory information and reliably decode intended reach trajectories, even amidst transiently considered alternatives.

The second portion of my thesis work was the successful development of the first bimanual brain-machine interface. To reach this goal...

Brain-Machine-Brain Interface

O'Doherty, Joseph Emmanuel
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação
Publicado em //2011
Relevância na Pesquisa
76.31%

Brain-machine interfaces (BMIs) use neuronal activity to control external actuators. As such, they show great promise for restoring motor and communication abilities in persons with paralysis or debilitating neurological disorders.

While BMIs aim to enact normal sensorimotor functions, so far they have lacked afferent feedback in the form of somatic sensation. This deficiency limits the utility of current BMI designs and may hinder the translation of future clinical BMIs, which will need a means of delivering sensory signals from prosthetic devices back to the user.

This dissertation describes the development of brain-machine-brain interfaces (BMBIs) capable of bidirectional communication with the brain. The interfaces consisted of efferent and afferent modules. The efferent modules decoded motor intentions from the activity of populations of cortical neurons recorded with chronic multielectrode recording arrays. The activity of these ensembles was used to drive the movements of a computer cursor and a realistic upper-limb avatar. The afferent modules encoded tactile feedback about the interactions of the avatar with virtual objects through patterns of intracortical microstimulation (ICMS).

I first show that a direct intracortical signal can be used to instruct rhesus monkeys about the direction of a reach to make with a BMI. Rhesus monkeys placed an actuator over an instruction target and obtained...

Non-Linear Adaptive Bayesian Filtering for Brain Machine Interfaces

Li, Zheng
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação Formato: 35719025 bytes; application/pdf
Publicado em //2010 EN_US
Relevância na Pesquisa
76.12%

Brain-machine interfaces (BMI) are systems which connect brains directly to machines or computers for communication. BMI-controlled prosthetic devices use algorithms to decode neuronal recordings into movement commands. These algorithms operate using models of how recorded neuronal signals relate to desired movements, called models of tuning. Models of tuning have typically been linear in prior work, due to the simplicity and speed of the algorithms used with them. Neuronal tuning has been shown to slowly change over time, but most prior work do not adapt tuning models to these changes. Furthermore, extracellular electrical recordings of neurons' action potentials slowly change over time, impairing the preprocessing step of spike-sorting, during which the neurons responsible for recorded action potentials are identified.

This dissertation presents a non-linear adaptive Bayesian filter and an adaptive spike-sorting method for BMI decoding. The adaptive filter consists of the n-th order unscented Kalman filter and Bayesian regression self-training updates. The unscented Kalman filter estimates desired prosthetic movements using a non-linear model of tuning as its observation model. The model is quadratic with terms for position...

A Study of Extracting Information from Neuronal Ensemble Activity and Sending Information to the Brain Using Microstimulation in Two Experimental Models: Bipedal Locomotion in Rhesus Macaques and Instructed Reaching Movements in Owl Monkeys

Fitzsimmons, Nathan Andrew
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação Formato: 23979157 bytes; application/pdf
Publicado em //2009 EN_US
Relevância na Pesquisa
76.13%

The loss of the ability to walk as the result of neurological injury or disease critically impacts the mobility and everyday lifestyle of millions. The World Heath Organization (WHO) estimates that approximately 1% of the world's population needs the use of a wheelchair to assist their personal mobility. Advances in the field of brain-machine interfaces (BMIs) have recently demonstrated the feasibility of using neuroprosthetics to extract motor information from cortical ensembles for more effective control of upper-limb replacements. However, the promise of BMIs has not yet been brought to bear on the challenge of restoring the ability to walk. A future neuroprosthesis designed to restore walking would need two streams of information flowing between the user's brain and the device. First, the motor control signals would have to be extracted from the brain, allowing the robotic prosthesis to behave in the manner intended by the user. Second, and equally important would be the flow of sensory and proprioceptive information back to the user from the neuroprosthesis. Here, I contribute to the foundation of such a bi-directional brain machine interface for the restoration of walking in a series of experiments in two animal models...

Technology for Brain-Machine Interfaces

Hanson, Timothy Lars
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação
Publicado em //2012
Relevância na Pesquisa
76.09%

Brain-machine interfaces (BMIs) use recordings from the nervous system to extract volitional and motor parameters for controlling external actuators, such as prosthetics, thereby bypassing or replacing injured tissue. As such, they show enormous promise for restoring mobility, dexterity, or communication in paralyzed patients or amputees. Recent advancements to the BMI paradigm have made the brain -- machine communication channel bidirectional, enabling the prosthetic to inform the user about touch, temperature, strain, or other sensory information; these devices are hence called brain-machine-brain interfaces (BMBIs).

In the first chapter an intraoperative BMI is investigated in human patients undergoing surgery for implantation of a deep brain stimulation (DBS) treatment electrodes. While the BMI was marginally effective, we found high levels of behavioral and tremor tuning among cells recorded from the surgical targets, the subthalamic nucleus (STN) and ventral intermediate nucleus (VIM) of the thalamus. Notably, this tremor or behavior tuning was not mutually exclusive with oscillatory behavior, suggesting that physiological tuning persists even in the face of pathological oscillations. We then used nonlinear means for extracting tremor tuning...