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https://www.readbyqxmd.com/read/30523833/multiscale-modeling-and-decoding-algorithms-for-spike-field-activity
#1
Han-Lin Hsieh, Yan Tat Wong, Bijan Pesaran, Maryam M Shanechi
OBJECTIVE: Behavior is encoded across multiple spatiotemporal scales of brain activity. Modern technology can simultaneously record various scales, from spiking of individual neurons to large neural populations measured with field activity. This capability necessitates developing multiscale modeling and decoding algorithms for spike-field activity, which is challenging because of the fundamental differences in statistical characteristics and time-scales of these signals. Spikes are binary-valued with a millisecond time-scale while fields are continuous-valued with slower time-scales...
October 24, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/30522993/evaluating-the-effectiveness-and-safety-of-the-electroencephalogram-based-brain-machine-interface-rehabilitation-system-for-patients-with-severe-hemiparetic-stroke-protocol-for-a-randomized-controlled-trial-best-brain-trial
#2
Katsuhiro Mizuno, Takayuki Abe, Junichi Ushiba, Michiyuki Kawakami, Tomomi Ohwa, Kazuto Hagimura, Miho Ogura, Kohei Okuyama, Toshiyuki Fujiwara, Meigen Liu
BACKGROUND: We developed a brain-machine interface (BMI) system for poststroke patients with severe hemiplegia to detect event-related desynchronization (ERD) on scalp electroencephalogram (EEG) and to operate a motor-driven hand orthosis combined with neuromuscular electrical stimulation. ERD arises when the excitability of the ipsi-lesional sensorimotor cortex increases. OBJECTIVE: The aim of this study was to evaluate our hypothesis that motor training using this BMI system could improve severe hemiparesis that is resistant to improvement by conventional rehabilitation...
December 6, 2018: JMIR Research Protocols
https://www.readbyqxmd.com/read/30505265/a-new-frontier-the-convergence-of-nanotechnology-brain-machine-interfaces-and-artificial-intelligence
#3
REVIEW
Gabriel A Silva
A confluence of technological capabilities is creating an opportunity for machine learning and artificial intelligence (AI) to enable "smart" nanoengineered brain machine interfaces (BMI). This new generation of technologies will be able to communicate with the brain in ways that support contextual learning and adaptation to changing functional requirements. This applies to both invasive technologies aimed at restoring neurological function, as in the case of neural prosthesis, as well as non-invasive technologies enabled by signals such as electroencephalograph (EEG)...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/30502371/an-automated-behavioral-apparatus-to-combine-parameterized-reaching-and-grasping-movements-in-3d-space
#4
Junjun Chen, Yaoyao Hao, Shaomin Zhang, Guanghao Sun, Kedi Xu, Weidong Chen, Xiaoxiang Zheng
BACKGROUND: The neural principles underlying reaching and grasping movements have been studied extensively in primates for decades. However, few experimental apparatuses have been developed to enable a flexible combination of reaching and grasping in one task in three-dimensional (3D) space. NEW METHOD: By combining a custom turning table with a 3D translational device, we have developed a highly flexible apparatus that enables the subject to reach multiple positions in 3D space, and grasp differently shaped objects with multiple grip types in each position...
November 28, 2018: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/30496189/training-with-brain-machine-interfaces-visuo-tactile-feedback-and-assisted-locomotion-improves-sensorimotor-visceral-and-psychological-signs-in-chronic-paraplegic-patients
#5
Solaiman Shokur, Ana R C Donati, Debora S F Campos, Claudia Gitti, Guillaume Bao, Dora Fischer, Sabrina Almeida, Vania A S Braga, Patricia Augusto, Chris Petty, Eduardo J L Alho, Mikhail Lebedev, Allen W Song, Miguel A L Nicolelis
Spinal cord injury (SCI) induces severe deficiencies in sensory-motor and autonomic functions and has a significant negative impact on patients' quality of life. There is currently no systematic rehabilitation technique assuring recovery of the neurological impairments caused by a complete SCI. Here, we report significant clinical improvement in a group of seven chronic SCI patients (six AIS A, one AIS B) following a 28-month, multi-step protocol that combined training with non-invasive brain-machine interfaces, visuo-tactile feedback and assisted locomotion...
2018: PloS One
https://www.readbyqxmd.com/read/30475577/3d-conductive-scaffolds-as-neural-prostheses-based-on-carbon-nanotubes-and-polypyrrole
#6
Nuria Alegret, Antonio Dominguez-Alfaro, Jose Miguel Gonzalez-Dominguez, Blanca Arnaiz, Unai Cossío, Susanna Bosi, Ester Vázquez, Pedro Ramos-Cabrer, David Mecerreyes, Maurizio Prato
Three-dimensional scaffolds for cellular organization need to enjoy a series of specific properties. On the one hand, the morphology, shape and porosity are critical parameters, and eventually re-lated with the mechanical properties. On the other hand, electrical conductivity is an important asset when dealing with electroactive cells, so it is a desirable property even if the conductivity values are not particularly high. Here, we construct 3D porous and conductive composites, where C8-D1A astrocytic cells were incubated to study their biocompatibility...
November 26, 2018: ACS Applied Materials & Interfaces
https://www.readbyqxmd.com/read/30462665/estimation-of-neuronal-firing-rate-using-bayesian-adaptive-kernel-smoother-baks
#7
Nur Ahmadi, Timothy G Constandinou, Christos-Savvas Bouganis
Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis...
2018: PloS One
https://www.readbyqxmd.com/read/30458838/on-the-design-of-eeg-based-movement-decoders-for-completely-paralyzed-stroke-patients
#8
Martin Spüler, Eduardo López-Larraz, Ander Ramos-Murguialday
BACKGROUND: Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. However, the relationship between the BMI design and its performance in stroke patients is still an open question. METHODS: In this study we compare different methodologies to design a BMI for rehabilitation and evaluate their effects on movement intention decoding performance...
November 20, 2018: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/30455621/cortical-decoding-of-individual-finger-group-motions-using-refit-kalman-filter
#9
Alex K Vaskov, Zachary T Irwin, Samuel R Nason, Philip P Vu, Chrono S Nu, Autumn J Bullard, Mackenna Hill, Naia North, Parag G Patil, Cynthia A Chestek
Objective: To date, many brain-machine interface (BMI) studies have developed decoding algorithms for neuroprostheses that provide users with precise control of upper arm reaches with some limited grasping capabilities. However, comparatively few have focused on quantifying the performance of precise finger control. Here we expand upon this work by investigating online control of individual finger groups. Approach: We have developed a novel training manipulandum for non-human primate (NHP) studies to isolate the movements of two specific finger groups: index and middle-ring-pinkie (MRP) fingers...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/30441732/clustering-based-kernel-reinforcement-learning-for-neural-adaptation-in-brain-machine-interfaces
#10
Xiang Zhang, Jose C Principe, Yiwen Wang
Reinforcement learning (RL) interprets subject's movement intention in Brain Machine Interfaces (BMIs) through trial-and-error with the advantage that it does not need the real limb movements. When the subjects try to control the external devices purely using brain signals without actual movements (brain control), they adjust the neural firing patterns to adapt to device control, which expands the state-action space for the RL decoder to explore. The challenge is to quickly explore the new knowledge in the sizeable state-action space and maintain good performance...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30441189/identifying-multiscale-hidden-states-to-decode-behavior
#11
Hamidreza Abbaspourazad, Yan Wong, Bijan Pesaran, Maryam M Shanechi
A key element needed in a brain-machine interface (BMI) decoder is the encoding model, which relates the neural activity to intended movement. The vast majority of work have used a representational encoding model, which assumes movement parameters are directly encoded in neural activity. Recent work have in turn suggested the existence of neural dynamics that represent behavior. This recent evidence motivates developing dynamical encoding models with hidden states that encode movement. Regardless of their type, encoding models have vastly characterized a single scale of activity, e...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30441039/investigation-of-the-influence-of-ecog-grid-spatial-density-on-decoding-hand-flexion-and-extension
#12
Tianxiao Jiang, Tao Jiang, Taylor Wang, Shanshan Mei, Qingzhu Liu, Yunlin Li, Xiaofei Wang, Sujit Prabhu, Zhiyi Sha, Nuri F Ince
Electrocorticogram (ECoG) has been used as a reliable modality to control a brain machine interface (BMI). Recently, promising results of high-density ECoG have shown that non redundant information can be recorded with finer spatial resolution from the cortical surface. In this study, highdensity ECoG was recorded intraoperatively from two patients during awake brain surgery while performing instructed hand flexion and extension. Event related desynchronization (ERD) were found in the low frequency band (LFB: 8-32 Hz) band while event related synchronization (ERS) were found in the high frequency band (HFB: 60-200 Hz)...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440927/spike-rate-estimation-using-bayesian-adaptive-kernel-smoother-baks-and-its-application-to-brain-machine-interfaces
#13
Nur Ahmadi, Timothy G Constandinou, Christos-Savvas Bouganis
Brain Machine Interfaces (BMIs) mostly utilise spike rate as an input feature for decoding a desired motor output as it conveys a useful measure to the underlying neuronal activity. The spike rate is typically estimated by a using non-overlap binning method that yields a coarse estimate. There exist several methods that can produce a smooth estimate which could potentially improve the decoding performance. However, these methods are relatively computationally heavy for real-time BMIs. To address this issue, we propose a new method for estimating spike rate that is able to yield a smooth estimate and also amenable to real-time BMIs...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440925/neural-decoding-forelimb-trajectory-using-evolutionary-neural-networks-with-feedback-error-learning-schemes
#14
Yu-Chieh Lin, Chin Chou, Shin-Hung Yang, Hsin-Yi Lai, Yu-Chun Lo, You-Yin Chen
Changes in the functional mapping between neural activities and kinematic parameters over time poses a challenge to current neural decoder of brain machine interfaces (BMIs). Traditional decoders robust to changes in functional mappings required many day's training data. The decoder may not be robust when it was trained by data from only few days. Therefore, a decoder should be trained to handle a variety of neural-to-kinematic mappings using limited training data. We proposed an evolutionary neural network with error feedback, ECPNN-EF, as a neural decoder, that considered the previous error as an input to the decoder in order to improve the robustness...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440834/novel-tdcs-montage-favors-lower-limb-motor-imagery-detection
#15
M Rodriguez-Ugarte, E Ianez, M Ortiz, J M Azorin
This work studies a novel transcranial direct current stimulation (tDCS) montage to improve a brain-machine interface (BMI) lower limb motor imagery detection. The tDCS montage is composed by two anodes and one cathode. One anode is located over the motor cortex and the other one over the cerebellum. Ten healthy subjects participated in this experiment. They were randomly separated into two groups: sham, which received a fake stimulation, and active tDCS, which received a real stimulation. Each subject was experimented on five consecutive days...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440792/a-hybrid-eeg-emg-bmi-improves-the-detection-of-movement-intention-in-cortical-stroke-patients-with-complete-hand-paralysis
#16
Eduardo Loopez-Larraz, Niels Birbaumer, Ander Ramos-Murguialday
Motor rehabilitation based on brain-machine interfaces (BMI) has been shown as a feasible option for stroke patients with complete paralysis. However, the pathologic EEG activity after a stroke makes the detection of movement intentions in these patients challenging, especially in those with damages involving the motor cortex. Residual electromyographic activity in those patients has been shown to be decodable, even in cases when the movement is not possible. Hybrid BMIs combining EEG and EMG activity have been recently proposed, although there is little evidence about how they work for completely paralyzed stroke patients...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440790/robust-local-field-potential-based-neural-decoding-by-actively-selecting-discriminative-channels
#17
Huijuan Yang, Kai Keng Ang, Camilo Libedinsky, Rosa Q So
Local field potentials (LFPs) have been proposed as a neural decoding signal to compensate for spike signal deterioration in invasive brain-machine interface applications. However, the presence of redundancy among LFP signals at different frequency bands across multiple channels may affect the decoding performance. In order to remove redundant LFP channels, we proposed a novel Fisher-distance ratio-based method to actively batch select discriminative channels to maximize the separation between classes. Experimental evaluation was conducted on 5 non-consecutive days of data from a non-human primate...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440783/hierarchical-graphical-models-for-context-aware-hybrid-brain-machine-interfaces
#18
Ozan Ozdenizci, Sezen Yagmur Gunay, Fernando Quivira, Deniz Erdogmug
We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities. Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses. Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440773/single-trial-eeg-classification-of-similar-errors
#19
Christopher Wirth, Eric Lacey, Paul Dockree, Mahnaz Arvaneh
When humans recognise errors, either committed by themselves or observed, error-related potentials (ErrP) are produced in the brain. Recently, a few studies have shown that it is possible to differentiate between the ErrPs generated for errors of different direction, severity, or type (e.g., response errors, interaction errors). However, in real-world scenarios, errors cannot always be delineated by these metrics. As such, it is important to consider whether errors that are similar in all of the aforementioned aspects can be classified against each other on a single-trial basis...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/30440716/on-muscle-selection-for-emg-based-decoding-of-dexterous-in-hand-manipulation-motions
#20
Yongje Kwon, Anany Dwivedi, Andrew J McDaid, Minas Liarokapis
The field of Brain Machine Interfaces (BMI) has attracted an increased interest due to its multiple applications in the health and entertainment domains. A BMI enables a direct interface between the brain and machines and is capable of translating neuronal information into meaningful actions (e.g., Electromyography based control of a prosthetic hand). One of the biggest challenges in developing a surface Electromyography (sEMG) based interface is the selection of the right muscles for the execution of a desired task...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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