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brain-machine interface

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https://www.readbyqxmd.com/read/28342747/subthalamic-nucleus-beta-and-gamma-activity-is-modulated-depending-on-the-level-of-imagined-grip-force
#1
Petra Fischer, Alek Pogosyan, Binith Cheeran, Alexander L Green, Tipu Z Aziz, Jonathan Hyam, Simon Little, Thomas Foltynie, Patricia Limousin, Ludvic Zrinzo, Marwan Hariz, Michael Samuel, Keyoumars Ashkan, Peter Brown, Huiling Tan
Motor imagery involves cortical networks similar to those activated by real movements, but the extent to which the basal ganglia are recruited is not yet clear. Gamma and beta oscillations in the subthalamic nucleus (STN) vary with the effort of sustained muscle activity. We recorded local field potentials in Parkinson's disease patients and investigated if similar changes can be observed during imagined gripping at three different 'forces'. We found that beta activity decreased significantly only for imagined grips at the two stronger force levels...
March 22, 2017: Experimental Neurology
https://www.readbyqxmd.com/read/28336339/optimal-feature-selection-from-fnirs-signals-using-genetic-algorithms-for-bci
#2
Farzan Majeed Noori, Noman Naseer, Nauman Khalid Qureshi, Hammad Nazeer, Rayyan Azam Khan
In this paper, a novel technique for determination of the optimal feature combinations and, thereby, acquisition of the maximum classification performance for a functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI), is proposed. After obtaining motor-imagery and rest signals from the motor cortex, filtering is applied to remove the physiological noises. Six features (signal slope, signal mean, signal variance, signal peak, signal kurtosis and signal skewness) are then extracted from the oxygenated hemoglobin (HbO)...
March 20, 2017: Neuroscience Letters
https://www.readbyqxmd.com/read/28325008/feasibility-of-an-ultra-low-power-digital-signal-processor-platform-as-a-basis-for-a-fully-implantable-brain-computer-interface-system
#3
Po T Wang, Keulanna Gandasetiawan, Colin M McCrimmon, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do
A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28321973/cortical-and-subcortical-mechanisms-of-brain-machine-interfaces
#4
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...
March 21, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28315750/fun-cube-based-brain-gym-cognitive-function-assessment-system
#5
Tao Zhang, Chung-Chih Lin, Tsang-Chu Yu, Jing Sun, Wen-Chuin Hsu, Alice May-Kuen Wong
The aim of this study is to design and develop a fun cube (FC) based brain gym (BG) cognitive function assessment system using the wireless sensor network and multimedia technologies. The system comprised (1) interaction devices, FCs and a workstation used as interactive tools for collecting and transferring data to the server, (2) a BG information management system responsible for managing the cognitive games and storing test results, and (3) a feedback system used for conducting the analysis of cognitive functions to assist caregivers in screening high risk groups with mild cognitive impairment...
March 3, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28298888/reaching-and-grasping-a-glass-of-water-by-locked-in-als-patients-through-a-bci-controlled-humanoid-robot
#6
Rossella Spataro, Antonio Chella, Brendan Allison, Marcello Giardina, Rosario Sorbello, Salvatore Tramonte, Christoph Guger, Vincenzo La Bella
Locked-in Amyotrophic Lateral Sclerosis (ALS) patients are fully dependent on caregivers for any daily need. At this stage, basic communication and environmental control may not be possible even with commonly used augmentative and alternative communication devices. Brain Computer Interface (BCI) technology allows users to modulate brain activity for communication and control of machines and devices, without requiring a motor control. In the last several years, numerous articles have described how persons with ALS could effectively use BCIs for different goals, usually spelling...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28275048/brain-machine-interfaces-from-basic-science-to-neuroprostheses-and-neurorehabilitation
#7
REVIEW
Mikhail A Lebedev, Miguel A L Nicolelis
Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity...
April 2017: Physiological Reviews
https://www.readbyqxmd.com/read/28269708/combining-a-hybrid-robotic-system-with-a-bain-machine-interface-for-the-rehabilitation-of-reaching-movements-a-case-study-with-a-stroke-patient
#8
F Resquin, J Ibañez, J Gonzalez-Vargas, F Brunetti, I Dimbwadyo, S Alves, L Carrasco, L Torres, Jose Luis Pons
Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user's movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269704/multiscale-brain-machine-interface-decoders
#9
Han-Lin Hsieh, Maryam M Shanechi
Brain-machine interfaces (BMI) have vastly used a single scale of neural activity, e.g., spikes or electrocorticography (ECoG), as their control signal. New technology allows for simultaneous recording of multiple scales of neural activity, from spikes to local field potentials (LFP) and ECoG. These advances introduce the new challenge of modeling and decoding multiple scales of neural activity jointly. Such multi-scale decoding is challenging for two reasons. First, spikes are discrete-valued and ECoG/LFP are continuous-valued, resulting in fundamental differences in statistical characteristics...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269589/high-performance-wearable-two-channel-hybrid-bci-system-with-eye-closure-assist
#10
Yubing Jiang, Hyeonseok Lee, Gang Li, Wan-Young Chung
Generally, eye closure (EC) and eye opening (EO)-based alpha blocking has widely recognized advantages, such as being easy to use, requiring little user training, while motor imagery (MI) is difficult for some users to have concrete feelings. This study presents a hybrid brain-computer interface (BCI) combining MI and EC strategies - such an approach aims to overcome some disadvantages of MI-based BCI, improve the performance and universality of the BCI. The EC/EO is employed to control the machine to switch in different states including forward, stop, changing direction motions, while the MI is used to control the machine to turn left or right for 90° by imagining the hands grasp motions when the system is switched into "changing direction" state...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269585/comparing-eeg-its-time-derivative-and-their-joint-use-as-features-in-a-bci-for-2-d-pointer-control
#11
Dimitrios Andreou, Riccardo Poli
Efficient and accurate classification of event related potentials is a core task in brain-computer interfaces (BCI). This is normally obtained by first extracting features from the voltage amplitudes recorded via EEG at different channels and then feeding them into a classifier. In this paper we evaluate the relative benefits of using the first order temporal derivatives of the EEG signals, not the EEG signals themselves, as inputs to the BCI: an area that has not been thoroughly examined. Specifically, we compare the classification performance of features extracted from the first derivative, with those derived from the amplitude, as well as their combination using data from a P300-based BCI mouse...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269554/adaptive-decoding-using-local-field-potentials-in-a-brain-machine-interface
#12
Rosa So, Camilo Libedinsky, Kai Keng Ang, Wee Chiek Clement Lim, Kyaw Kyar Toe, Cuntai Guan
Brain-machine interface (BMI) systems have the potential to restore function to people who suffer from paralysis due to a spinal cord injury. However, in order to achieve long-term use, BMI systems have to overcome two challenges - signal degeneration over time, and non-stationarity of signals. Effects of loss in spike signals over time can be mitigated by using local field potential (LFP) signals for decoding, and a solution to address the signal non-stationarity is to use adaptive methods for periodic recalibration of the decoding model...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269553/decoding-force-from-deep-brain-electrodes-in-parkinsonian-patients
#13
Syed A Shah, Huiling Tan, Peter Brown
Limitations of many Brain Machine Interface (BMI) systems using invasive electrodes include reliance on single neurons and decoding limited to kinematics only. This study investigates whether force-related information is present in the local field potential (LFP) recorded with deep brain electrodes using data from 14 patients with Parkinson's disease. A classifier based on logistic regression (LR) is developed to classify various force stages, using 10-fold cross validation. Least Absolute and Shrinkage Operator (Lasso) is then employed in order to identify the features with the most predictivity...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268959/modeling-distinct-sources-of-neural-variability-driving-neuroprosthetic-control
#14
Preeya Khanna, Vivek R Athalye, Suraj Gowda, Rui M Costa, Jose M Carmena
Many closed-loop, continuous-control brain-machine interface (BMI) architectures rely on decoding via a linear readout of noisy population neural activity. However, recent work has found that decomposing neural population activity into correlated and uncorrelated variability reveals that improvements in cursor control coincide with the emergence of correlated neural variability. In order to address how correlated and uncorrelated neural variability arises and contributes to BMI cursor control, we simulate a neural population receiving combinations of shared inputs affecting all cells and private inputs affecting only individual cells...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268958/reward-value-is-encoded-in-primary-somatosensory-cortex-and-can-be-decoded-from-neural-activity-during-performance-of-a-psychophysical-task
#15
David B McNiel, John S Choi, John P Hessburg, Joseph T Francis
Encoding of reward valence has been shown in various brain regions, including deep structures such as the substantia nigra as well as cortical structures such as the orbitofrontal cortex. While the correlation between these signals and reward valence have been shown in aggregated data comprised of many trials, little work has been done investigating the feasibility of decoding reward valence on a single trial basis. Towards this goal, one non-human primate (macaca radiata) was trained to grip and hold a target level of force in order to earn zero, one, two, or three juice rewards...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268956/maximum-correntropy-based-attention-gated-reinforcement-learning-designed-for-brain-machine-interface
#16
Hongbao Li, Fang Wang, Qiaosheng Zhang, Shaomin Zhang, Yiwen Wang, Xiaoxiang Zheng, Jose C Principe
Reinforcement learning is an effective algorithm for brain machine interfaces (BMIs) which interprets the mapping between neural activities with plasticity and the kinematics. Exploring large state-action space is difficulty when the complicated BMIs needs to assign credits over both time and space. For BMIs attention gated reinforcement learning (AGREL) has been developed to classify multi-actions for spatial credit assignment task with better efficiency. However, the outliers existing in the neural signals still make interpret the neural-action mapping difficult...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268955/a-non-linear-mapping-algorithm-shaping-the-control-policy-of-a-bidirectional-brain-machine-interface
#17
Fabio Boi, Marianna Semprini, Alessandro Vato
Motor brain-machine interfaces (BMIs) transform neural activities recorded directly from the brain into motor commands to control the movements of an external object by establishing an interface between the central nervous system (CNS) and the device. Bidirectional BMIs are closed-loop systems that add a sensory channel to provide the brain with an artificial feedback signal produced by the interaction between the device and the external world. Taking inspiration from the functioning of the spinal cord in mammalians, in our previous works we designed and developed a bidirectional BMI that uses the neural signals recorded form rats' motor cortex to control the movement of an external object...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268901/a-flexible-parylene-probe-for-in-vivo-recordings-from-multiple-subregions-of-the-rat-hippocampus
#18
Huijing Xu, Ahuva Weltman, Min-Chi Hsiao, Kee Scholten, Ellis Meng, Theodore W Berger, Dong Song
The hippocampus is crucial to the formation of long-term memory and declarative memory. It is divided into three sub-fields the CA1, the CA3 and the DG. To understand the neuronal circuitry within the hippocampus and to study the role of the hippocampus in memory function requires the collection of neural activities from multiple subregions of the hippocampus simultaneously. Micro-wire electrode arrays are commonly used as an interface with neural systems. However, recording from multiple deep brain regions with curved anatomical structures such as the thin cell body layers of the hippocampus requires the micro-wires to be arranged into a highly accurate, complex layout that is difficult to fabricated manually...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268894/mutual-information-based-feature-selection-for-low-cost-bcis-based-on-motor-imagery
#19
L Schiatti, L Faes, J Tessadori, G Barresi, L Mattos
In the present study a feature selection algorithm based on mutual information (MI) was applied to electro-encephalographic (EEG) data acquired during three different motor imagery tasks from two dataset: Dataset I from BCI Competition IV including full scalp recordings from four subjects, and new data recorded from three subjects using the popular low-cost Emotiv EPOC EEG headset. The aim was to evaluate optimal channels and band-power (BP) features for motor imagery tasks discrimination, in order to assess the feasibility of a portable low-cost motor imagery based Brain-Computer Interface (BCI) system...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268892/eeg-based-single-trial-detection-of-errors-from-multiple-error-related-brain-activity
#20
Guofa Shou, Lei Ding
A key ability of the human brain is to monitor erroneous events and adjust behaviors accordingly. Electrophysiological and neuroimaging studies have demonstrated different brain activities related to errors. Meanwhile, the recognition of error-related brain activity as one aspect of performance monitoring has been reported for potential applications in clinical neuroscience and brain-machine interface, where single-trial analysis and classification would provide novel insights on dynamic brain responses to errors...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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