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https://www.readbyqxmd.com/read/28553694/investigation-of-different-classifiers-and-channel-configurations-of-a-mobile-p300-based-brain-computer-interface
#1
Simone A Ludwig, Jun Kong
Innovative methods and new technologies have significantly improved the quality of our daily life. However, disabled people, for example those that cannot use their arms and legs anymore, often cannot benefit from these developments, since they cannot use their hands to interact with traditional interaction methods (such as mouse or keyboard) to communicate with a computer system. A brain-computer interface (BCI) system allows such a disabled person to control an external device via brain waves. Past research mostly dealt with static interfaces, which limit users to a stationary location...
May 29, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28550098/contralesional-brain-computer-interface-control-of-a-powered-exoskeleton-for-motor-recovery-in-chronic-stroke-survivors
#2
David T Bundy, Lauren Souders, Kelly Baranyai, Laura Leonard, Gerwin Schalk, Robert Coker, Daniel W Moran, Thy Huskey, Eric C Leuthardt
BACKGROUND AND PURPOSE: There are few effective therapies to achieve functional recovery from motor-related disabilities affecting the upper limb after stroke. This feasibility study tested whether a powered exoskeleton driven by a brain-computer interface (BCI), using neural activity from the unaffected cortical hemisphere, could affect motor recovery in chronic hemiparetic stroke survivors. This novel system was designed and configured for a home-based setting to test the feasibility of BCI-driven neurorehabilitation in outpatient environments...
May 26, 2017: Stroke; a Journal of Cerebral Circulation
https://www.readbyqxmd.com/read/28538681/human-thalamic-somatosensory-nucleus-ventral-caudal-vc-as-a-locus-for-stimulation-by-inputs-from-tactile-noxious-and-thermal-sensors-on-an-active-prosthesis
#3
REVIEW
Jui Hong Chien, Anna Korzeniewska, Luana Colloca, Claudia Campbell, Patrick Dougherty, Frederick Lenz
The forebrain somatic sensory locus for input from sensors on the surface of an active prosthesis is an important component of the Brain Machine Interface. We now review the neuronal responses to controlled cutaneous stimuli and the sensations produced by Threshold Stimulation at Microampere current levels (TMIS) in such a locus, the human thalamic Ventral Caudal nucleus (Vc). The responses of these neurons to tactile stimuli mirror those for the corresponding class of tactile mechanoreceptor fiber in the peripheral nerve, and TMIS can evoke sensations like those produced by the stimuli that optimally activate each class...
May 24, 2017: Sensors
https://www.readbyqxmd.com/read/28516901/eeg-source-space-analysis-of-the-supervised-factor-analytic-approach-for-the-classification-of-multi-directional-arm-movement
#4
Vikram Shenoy Handiru, A P Vinod, Cuntai Guan
OBJECTIVE: In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. APPROACH: We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG...
May 18, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28446119/neurobionics-and-the-brain-computer-interface-current-applications-and-future-horizons
#5
REVIEW
Jeffrey V Rosenfeld, Yan Tat Wong
The brain-computer interface (BCI) is an exciting advance in neuroscience and engineering. In a motor BCI, electrical recordings from the motor cortex of paralysed humans are decoded by a computer and used to drive robotic arms or to restore movement in a paralysed hand by stimulating the muscles in the forearm. Simultaneously integrating a BCI with the sensory cortex will further enhance dexterity and fine control. BCIs are also being developed to: provide ambulation for paraplegic patients through controlling robotic exoskeletons; restore vision in people with acquired blindness; detect and control epileptic seizures; and improve control of movement disorders and memory enhancement...
May 1, 2017: Medical Journal of Australia
https://www.readbyqxmd.com/read/28363483/restoration-of-reaching-and-grasping-movements-through-brain-controlled-muscle-stimulation-in-a-person-with-tetraplegia-a-proof-of-concept-demonstration
#6
A Bolu Ajiboye, Francis R Willett, Daniel R Young, William D Memberg, Brian A Murphy, Jonathan P Miller, Benjamin L Walter, Jennifer A Sweet, Harry A Hoyen, Michael W Keith, P Hunter Peckham, John D Simeral, John P Donoghue, Leigh R Hochberg, Robert F Kirsch
BACKGROUND: People with chronic tetraplegia, due to high-cervical spinal cord injury, can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as functional electrical stimulation (FES). Users typically command FES systems through other preserved, but unrelated and limited in number, volitional movements (eg, facial muscle activity, head movements, shoulder shrugs). We report the findings of an individual with traumatic high-cervical spinal cord injury who coordinated reaching and grasping movements using his own paralysed arm and hand, reanimated through implanted FES, and commanded using his own cortical signals through an intracortical brain-computer interface (iBCI)...
May 6, 2017: Lancet
https://www.readbyqxmd.com/read/28361947/mapping-ecog-channel-contributions-to-trajectory-and-muscle-activity-prediction-in-human-sensorimotor-cortex
#7
Yasuhiko Nakanishi, Takufumi Yanagisawa, Duk Shin, Hiroyuki Kambara, Natsue Yoshimura, Masataka Tanaka, Ryohei Fukuma, Haruhiko Kishima, Masayuki Hirata, Yasuharu Koike
Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies have also identified distributions of "extrinsic-like" and "intrinsic-like" neurons in the premotor (PM) and primary motor (M1) cortices. Here, we investigated whether trajectories and muscle activity predicted from ECoG were obtained based on signals derived from extrinsic-like or intrinsic-like neurons. Three participants carried objects of three different masses along the same counterclockwise path on a table...
March 31, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28321973/cortical-and-subcortical-mechanisms-of-brain-machine-interfaces
#8
Silvia Marchesotti, Roberto Martuzzi, Aaron Schurger, Maria Laura Blefari, José R Del Millán, Hannes Bleuler, Olaf Blanke
Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level...
June 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28268630/decoding-movement-direction-using-phase-space-analysis-of-hemodynamic-responses-to-arm-movements-based-on-functional-near-infrared-spectroscopy
#9
Nicoladie Tam, Luca Pollonini, George Zouridakis
In this study we applied phase-space analysis on the hemodynamic signals recorded from the motor cortex of human subjects using functional near infrared spectroscopy (fNIRS) to decode the direction of intentional hand movements. Our goal is to develop a brain-computer-interface (BCI) based on optical imaging that can control a wheelchair. To establish the relationship between the hemodynamic response and movement direction, participants were asked to perform repetitive arm movements in two orthogonal directions (right-left and front-back) on a horizontal plane, while the time course of the oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) responses were recorded...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226810/decoding-movement-direction-using-phase-space-analysis-of-hemodynamic-responses-to-arm-movements-based-on-functional-near-infrared-spectroscopy
#10
Nicoladie Tam, Luca Pollonini, George Zouridakis, Nicoladie Tam, Luca Pollonini, George Zouridakis, Luca Pollonini, Nicoladie Tam, George Zouridakis
In this study we applied phase-space analysis on the hemodynamic signals recorded from the motor cortex of human subjects using functional near infrared spectroscopy (fNIRS) to decode the direction of intentional hand movements. Our goal is to develop a brain-computer-interface (BCI) based on optical imaging that can control a wheelchair. To establish the relationship between the hemodynamic response and movement direction, participants were asked to perform repetitive arm movements in two orthogonal directions (right-left and front-back) on a horizontal plane, while the time course of the oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) responses were recorded...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28193497/i-act-therefore-i-err-eeg-correlates-of-success-and-failure-in-a-virtual-throwing-game
#11
Boris Yazmir, Miriam Reiner
What are the neural responses to success and failure in a throwing task? To answer this question, we compared Event Related Potentials (ERPs) correlated with success and failure during a highly-ecological-virtual game. Participants played a tennis-like game in an immersive 3D virtual world, against a computer player, by controlling a virtual tennis racket with a force feedback robotic arm. Results showed that success, i.e. hitting the target, and failure, by missing the target, evoked ERP's that differ by peak, latencies, scalp signal distributions, sLORETA source estimation, and time-frequency patterns...
February 11, 2017: International Journal of Psychophysiology
https://www.readbyqxmd.com/read/28143603/classification-of-upper-limb-center-out-reaching-tasks-by-means-of-eeg-based-continuous-decoding-techniques
#12
Andrés Úbeda, José M Azorín, Ricardo Chavarriaga, José Del R Millán
BACKGROUND: One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements...
February 1, 2017: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/28068293/a-hybrid-bmi-based-exoskeleton-for-paresis-emg-control-for-assisting-arm-movements
#13
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
https://www.readbyqxmd.com/read/27999362/body-machine-interfaces-after-spinal-cord-injury-rehabilitation-and-brain-plasticity
#14
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
https://www.readbyqxmd.com/read/27966546/noninvasive-electroencephalogram-based-control-of-a-robotic-arm-for-reach-and-grasp-tasks
#15
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
https://www.readbyqxmd.com/read/27802344/independent-mobility-achieved-through-a-wireless-brain-machine-interface
#16
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
https://www.readbyqxmd.com/read/27695404/brain-computer-interface-training-after-stroke-affects-patterns-of-brain-behavior-relationships-in-corticospinal-motor-fibers
#17
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
https://www.readbyqxmd.com/read/27590967/multisession-noninvasive-closed-loop-neuroprosthetic-control-of-grasping-by-upper-limb-amputees
#18
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
https://www.readbyqxmd.com/read/27555805/hybrid-neuroprosthesis-for-the-upper-limb-combining-brain-controlled-neuromuscular-stimulation-with-a-multi-joint-arm-exoskeleton
#19
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
https://www.readbyqxmd.com/read/27455526/control-of-redundant-kinematic-degrees-of-freedom-in-a-closed-loop-brain-machine-interface
#20
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
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