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brain interfaced controlled arm

Toshihiro Kawase, Takeshi Sakurada, Yasuharu Koike, Kenji Kansaku
OBJECTIVE: Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles. APPROACH: Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model...
January 9, 2017: Journal of Neural Engineering
Ismael Seáñez-González, Camilla Pierella, Ali Farshchiansadegh, Elias B Thorp, Xue Wang, Todd Parrish, Ferdinando A Mussa-Ivaldi
The purpose of this study was to identify rehabilitative effects and changes in white matter microstructure in people with high-level spinal cord injury following bilateral upper-extremity motor skill training. Five subjects with high-level (C5-C6) spinal cord injury (SCI) performed five visuo-spatial motor training tasks over 12 sessions (2-3 sessions per week). Subjects controlled a two-dimensional cursor with bilateral simultaneous movements of the shoulders using a non-invasive inertial measurement unit-based body-machine interface...
December 19, 2016: Brain Sciences
Jianjun Meng, Shuying Zhang, Angeliki Bekyo, Jaron Olsoe, Bryan Baxter, Bin He
Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls...
December 14, 2016: Scientific Reports
Camilo Libedinsky, Rosa So, Zhiming Xu, Toe K Kyar, Duncun Ho, Clement Lim, Louiza Chan, Yuanwei Chua, Lei Yao, Jia Hao Cheong, Jung Hyup Lee, Kulkarni Vinayak Vishal, Yongxin Guo, Zhi Ning Chen, Lay K Lim, Peng Li, Lei Liu, Xiaodan Zou, Kai K Ang, Yuan Gao, Wai Hoe Ng, Boon Siew Han, Keefe Chng, Cuntai Guan, Minkyu Je, Shih-Cheng Yen
Individuals with tetraplegia lack independent mobility, making them highly dependent on others to move from one place to another. Here, we describe how two macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex, and decoded to achieve brain control of the platform...
2016: PloS One
Brittany M Young, Julie M Stamm, Jie Song, Alexander B Remsik, Veena A Nair, Mitchell E Tyler, Dorothy F Edwards, Kristin Caldera, Justin A Sattin, Justin C Williams, Vivek Prabhakaran
Background: Brain-computer interface (BCI) devices are being investigated for their application in stroke rehabilitation, but little is known about how structural changes in the motor system relate to behavioral measures with the use of these systems. Objective: This study examined relationships among diffusion tensor imaging (DTI)-derived metrics and with behavioral changes in stroke patients with and without BCI training. Methods: Stroke patients (n = 19) with upper extremity motor impairment were assessed using Stroke Impact Scale (SIS), Action Research Arm Test (ARAT), Nine-Hole Peg Test (9-HPT), and DTI scans...
2016: Frontiers in Human Neuroscience
H A Agashe, A Y Paek, J L Contreras-Vidal
Upper limb amputation results in a severe reduction in the quality of life of affected individuals due to their inability to easily perform activities of daily living. Brain-machine interfaces (BMIs) that translate grasping intent from the brain's neural activity into prosthetic control may increase the level of natural control currently available in myoelectric prostheses. Current BMI techniques demonstrate accurate arm position and single degree-of-freedom grasp control but are invasive and require daily recalibration...
2016: Progress in Brain Research
Florian Grimm, Armin Walter, Martin Spüler, Georgios Naros, Wolfgang Rosenstiel, Alireza Gharabaghi
Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop...
2016: Frontiers in Neuroscience
Helene G Moorman, Suraj Gowda, Jose M Carmena
Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nervous system. In BMIs with kinematically redundant actuators, the combination of the task goals and the system under neural control can allow for many equally optimal task solutions. The extent to which kinematically redundant degrees of freedom (DOFs) in a BMI system may be under direct neural control is unknown...
July 21, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Matt Higger, Fernando Quivira, Murat Akcakaya, Mohammad Moghadamfalahi, Hooman Nezamfar, Mujdat Cetin, Deniz Erdogmus
Brain Computer Interfaces (BCI) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e...
July 13, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Monzurul Alam, Willyam Rodrigues, Bau Ngoc Pham, Nitish V Thakor
Restoration of motor function is one of the highest priorities in individuals afflicted with spinal cord injury (SCI). The application of brain-machine interfaces (BMIs) to neuroprostheses provides an innovative approach to treat patients with sensorimotor impairments. A BMI decodes motor intent from cortical signals to control external devices such as a computer cursor or a robotic arm. Recent BMI systems can now use these motor intent signals to directly activate paretic muscles or to modulate the spinal cord in a way that reengage dormant neuromuscular systems below the level of injury...
September 1, 2016: Brain Research
Michael Kryger, Brock Wester, Eric A Pohlmeyer, Matthew Rich, Brendan John, James Beaty, Michael McLoughlin, Michael Boninger, Elizabeth C Tyler-Kabara
As Brain-Computer Interface (BCI) systems advance for uses such as robotic arm control it is postulated that the control paradigms could apply to other scenarios, such as control of video games, wheelchair movement or even flight. The purpose of this pilot study was to determine whether our BCI system, which involves decoding the signals of two 96-microelectrode arrays implanted into the motor cortex of a subject, could also be used to control an aircraft in a flight simulator environment. The study involved six sessions in which various parameters were modified in order to achieve the best flight control, including plane type, view, control paradigm, gains, and limits...
January 2017: Experimental Neurology
Josh Merel, David Carlson, Liam Paninski, John P Cunningham
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available...
May 2016: PLoS Computational Biology
O A Mokienko, R Kh Lyukmanov, L A Chernikova, N A Suponeva, M A Piradov, A A Frolov
Motor imagery is suggested to stimulate the same plastic mechanisms in the brain as a real movement. The brain-computer interface (BCI) controls motor imagery by converting EEG during this process into the commands for an external device. This article presents the results of two-stage study of the clinical use of non-invasive BCI in the rehabilitation of patients with severe hemiparesis caused by focal brain damage. It was found that the ability to control BCI did not depend on the duration of a disease, brain lesion localization and the degree of neurological deficit...
January 2016: Fiziologiia Cheloveka
E V Biryukova, O G Pavlova, M E Kurganskaya, P D Bobrov, L G Turbina, A A Frolov, V I Davydov, A V Sil'tchenko, O A Mokienko
We studied the dynamics of motor function recovery in a patient with severe brain damage in the course of neurorehabilitation using hand exoskeleton controlled by brain-computer interface. For estimating the motor function of paretic arm, we used the biomechanical analysis of movements registered during the course of rehabilitation. After 15 weekly sessions of hand exoskeleton control, the following results were obtained: a) the velocity profile of goal-directed movements of paretic hand became bell-shaped, b) the patient began to extend and abduct the hand which was flexed and adducted in the beginning of rehabilitation, and c) the patient began to supinate the forearm which was pronated in the beginning of rehabilitation...
January 2016: Fiziologiia Cheloveka
Mojgan Tavakolan, Xinyi Yong, Xin Zhang, Carlo Menon
Facilitating independent living of individuals with upper extremity impairment is a compelling goal for our society. The degree of disability of these individuals could potentially be reduced by using robotic devices that assist their movements in activities of daily living. One approach to control such robotic systems is the use of a brain-computer interface, which detects the user's intention. This study proposes a method for estimating the user's intention using electroencephalographic (EEG) signals. The proposed method is capable of discriminating rest from various imagined arm movements, including grasping and elbow flexion...
2016: Journal of Medical and Biological Engineering
Javier M Antelis, Luis Montesano, Ander Ramos-Murguialday, Niels Birbaumer, Javier Minguez
GOAL: Stroke survivors usually require motor rehabilitation therapy as, due to the lesion, they completely or partially loss mobility in the limbs. Brain-computer interface technology offers the possibility of decoding the attempt to move paretic limbs in real time to improve existing motor rehabilitation. However, a major difficulty for the practical application of the BCI to stroke survivors is that the brain rhythms that encode the motor states might be diminished due to the lesion...
January 2017: IEEE Transactions on Bio-medical Engineering
John E Downey, Jeffrey M Weiss, Katharina Muelling, Arun Venkatraman, Jean-Sebastien Valois, Martial Hebert, J Andrew Bagnell, Andrew B Schwartz, Jennifer L Collinger
BACKGROUND: 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...
March 18, 2016: Journal of Neuroengineering and Rehabilitation
David T Bundy, Mrinal Pahwa, Nicholas Szrama, Eric C Leuthardt
OBJECTIVE: Electrocorticography (ECoG) signals have emerged as a potential control signal for brain-computer interface (BCI) applications due to balancing signal quality and implant invasiveness. While there have been numerous demonstrations in which ECoG signals were used to decode motor movements and to develop BCI systems, the extent of information that can be decoded has been uncertain. Therefore, we sought to determine if ECoG signals could be used to decode kinematics (speed, velocity, and position) of arm movements in 3D space...
April 2016: Journal of Neural Engineering
Po T Wang, Christine E King, Colin M McCrimmon, Jack J Lin, Mona Sazgar, Frank P K Hsu, Susan J Shaw, David E Millet, Luis A Chui, Charles Y Liu, An H Do, Zoran Nenadic
OBJECTIVE: Electrocorticography (ECoG)-based brain-computer interface (BCI) is a promising platform for controlling arm prostheses. To restore functional independence, a BCI must be able to control arm prostheses along at least six degrees-of-freedoms (DOFs). Prior studies suggest that standard ECoG grids may be insufficient to decode multi-DOF arm movements. This study compared the ability of standard and high-density (HD) ECoG grids to decode the presence/absence of six elementary arm movements and the type of movement performed...
April 2016: Journal of Neural Engineering
Holger Urbanek, Patrick van der Smagt
Advanced data analysis and visualization methodologies have played an important role in making surface electromyography both a valuable diagnostic methodology of neuromuscular disorders and a robust brain-machine interface, usable as a simple interface for prosthesis control, arm movement analysis, stiffness control, gait analysis, etc. But for diagnostic purposes, as well as for interfaces where the activation of single muscles is of interest, surface EMG suffers from severe crosstalk between deep and superficial muscle activation, making the reliable detection of the source of the signal, as well as reliable quantification of deeper muscle activation, prohibitively difficult...
April 2016: Journal of Electromyography and Kinesiology
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