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Brain machine interface

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https://www.readbyqxmd.com/read/29682000/an-adaptive-calibration-framework-for-mvep-based-brain-computer-interface
#1
Teng Ma, Fali Li, Peiyang Li, Dezhong Yao, Yangsong Zhang, Peng Xu
Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set...
2018: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/29674949/effect-of-different-movement-speed-modes-on-human-action-observation-an-eeg-study
#2
Tian-Jian Luo, Jitu Lv, Fei Chao, Changle Zhou
Action observation (AO) generates event-related desynchronization (ERD) suppressions in the human brain by activating partial regions of the human mirror neuron system (hMNS). The activation of the hMNS response to AO remains controversial for several reasons. Therefore, this study investigated the activation of the hMNS response to a speed factor of AO by controlling the movement speed modes of a humanoid robot's arm movements. Since hMNS activation is reflected by ERD suppressions, electroencephalography (EEG) with BCI analysis methods for ERD suppressions were used as the recording and analysis modalities...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29666460/cortical-classification-with-rhythm-entropy-for-error-processing-in-cocktail-party-environment-based-on-scalp-eeg-recording
#3
Yin Tian, Wei Xu, Li Yang
Using single-trial cortical signals calculated by weighted minimum norm solution estimation (WMNE), the present study explored a feature extraction method based on rhythm entropy to classify the scalp electroencephalography (EEG) signals of error response from that of correct response during performing auditory-track tasks in cocktail party environment. The classification rate achieved 89.7% with single-trial (≈700 ms) when using support vector machine(SVM) with the leave-one-out-cross-validation (LOOCV)...
April 17, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29660675/improvements-in-event-related-desynchronization-and-classification-performance-of-motor-imagery-using-instructive-dynamic-guidance-and-complex-tasks
#4
Yan Bian, Hongzhi Qi, Li Zhao, Dong Ming, Tong Guo, Xing Fu
BACKGROUND AND OBJECTIVE: The motor-imagery based brain-computer interface supplies a potential approach for motor-impaired patients, not only to control rehabilitation facilities but also to promote recovery from motor dysfunctions. To improve event-related desynchronization during motor imagery and obtain improved brain-computer interface classification accuracy, we introduce dynamic video guidance and complex motor tasks to the motor imagery paradigm. METHODS: Eleven participants were included in the experiment; 64-channel electroencephalographic data were collected and analyzed during four motor imagery tasks with different guidance...
March 30, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29623905/compact-standalone-platform-for-neural-recording-with-real-time-spike-sorting-and-data-logging
#5
Song Luan, Ian Williams, Michal Maslik, Yan Liu, Felipe De Carvalho, Andrew Jackson, Rodrigo Quian Quiroga, Timothy Constandinou
OBJECTIVE: Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective Brain Machine Interfaces (BMIs). These recordings generate enormous amounts of data for transmission & storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: 1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); 2) producing real-time, low-latency, spike sorted data; and 3) long term untethered operation...
April 6, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29623902/a-continuous-time-resolved-measure-decoded-from-eeg-oscillatory-activity-predicts-working-memory-task-performance
#6
Elaine Astrand
OBJECTIVE: Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved...
April 6, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29616982/a-fast-intracortical-brain-machine-interface-with-patterned-optogenetic-feedback
#7
Aamir Abbasi, Dorian Goueytes, Daniel E Shulz, Valerie Ego-Stengel, Luc Estebanez
OBJECTIVE: The development of brain-machine interfaces (BMIs) brings a new perspective to patients with a loss of autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis...
April 4, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29596978/fast-and-robust-block-sparse-bayesian-learning-for-eeg-source-imaging
#8
Alejandro Ojeda, Kenneth Kreutz-Delgado, Tim Mullen
We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of noisy measurements. Current implementations of the SBL framework are computationally expensive and typically handle fluctuations in the measurement noise using different heuristics that are unsuitable for real-time imaging applications. We address these shortcomings by decoupling the estimation of the sensor noise covariance and the sparsity profile of the sources, thereby yielding an efficient two-stage algorithm...
March 26, 2018: NeuroImage
https://www.readbyqxmd.com/read/29563891/how-our-cognition-shapes-and-is-shaped-by-technology-a-common-framework-for-understanding-human-tool-use-interactions-in-the-past-present-and-future
#9
REVIEW
François Osiurak, Jordan Navarro, Emanuelle Reynaud
Over the evolution, humans have constantly developed and improved their technologies. This evolution began with the use of physical tools, those tools that increase our sensorimotor abilities (e.g., first stone tools, modern knives, hammers, pencils). Although we still use some of these tools, we also employ in daily life more sophisticated tools for which we do not systematically understand the underlying physical principles (e.g., computers, cars). Current research is also turned toward the development of brain-computer interfaces directly linking our brain activity to machines (i...
2018: Frontiers in Psychology
https://www.readbyqxmd.com/read/29557196/the-future-of-the-provision-process-for-mobility-assistive-technology-a-survey-of-providers
#10
Brad E Dicianno, James Joseph, Stacy Eckstein, Christina K Zigler, Eleanor J Quinby, Mark R Schmeler, Richard M Schein, Jon Pearlman, Rory A Cooper
PURPOSE: The purpose of this study was to evaluate the opinions of providers of mobility assistive technologies to help inform a research agenda and set priorities. MATERIALS AND METHODS: This survey study was anonymous and gathered opinions of individuals who participate in the process to provide wheelchairs and other assistive technologies to clients. Participants were asked to rank the importance of developing various technologies and rank items against each other in terms of order of importance...
March 20, 2018: Disability and Rehabilitation. Assistive Technology
https://www.readbyqxmd.com/read/29540617/illusory-movement-perception-improves-motor-control-for-prosthetic-hands
#11
Paul D Marasco, Jacqueline S Hebert, Jon W Sensinger, Courtney E Shell, Jonathon S Schofield, Zachary C Thumser, Raviraj Nataraj, Dylan T Beckler, Michael R Dawson, Dan H Blustein, Satinder Gill, Brett D Mensh, Rafael Granja-Vazquez, Madeline D Newcomb, Jason P Carey, Beth M Orzell
To effortlessly complete an intentional movement, the brain needs feedback from the body regarding the movement's progress. This largely nonconscious kinesthetic sense helps the brain to learn relationships between motor commands and outcomes to correct movement errors. Prosthetic systems for restoring function have predominantly focused on controlling motorized joint movement. Without the kinesthetic sense, however, these devices do not become intuitively controllable. We report a method for endowing human amputees with a kinesthetic perception of dexterous robotic hands...
March 14, 2018: Science Translational Medicine
https://www.readbyqxmd.com/read/29522413/bilinear-regularized-locality-preserving-learning-on-riemannian-graph-for-motor-imagery-bci
#12
Xiaofeng Xie, Zhu Liang Yu, Zhenghui Gu, Jun Zhang, Ling Cen, Yuanqing Li
In off-line training of motor imagery-based brain-computer interfaces (BCIs), to enhance the generalization performance of the learned classifier, the local information contained in test data could be used to improve the performance of motor imagery as well. Further considering that the covariance matrices of electroencephalogram (EEG) signal lie on Riemannian manifold, in this paper, we construct a Riemannian graph to incorporate the information of training and test data into processing. The adjacency and weight in Riemannian graph are determined by the geodesic distance of Riemannian manifold...
March 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/29522404/a-speedy-calibration-method-using-riemannian-geometry-measurement-and-other-subject-samples-on-a-p300-speller
#13
Hongzhi Qi, Yuqi Xue, Lichao Xu, Yong Cao, Xuejun Jiao
P300 spellers are among the most popular brain-computer interface paradigms, and they are used for many clinical applications. However, building the classifier for identifying event-related potential (ERP) responses, i.e., calibrating the P300 speller, is still a time-consuming and user-dependent problem. This paper proposes a novel method to reduce calibration times significantly. In the proposed method, a small number of ERP epochs from the current user were used to build a reference epoch. Based on this reference, the Riemannian distance measurement was used to select similar ERP samples from an existing data pool, which contained other-subject ERP responses...
March 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/29522399/robust-support-matrix-machine-for-single-trial-eeg-classification
#14
Qingqing Zheng, Fengyuan Zhu, Pheng-Ann Heng
Electroencephalogram (EEG) signals are of complex structure and can be naturally represented as matrices. Classification is one of the most important steps for EEG signal processing. Newly developed classifiers can handle these matrix-form data by adding low-rank constraint to leverage the correlation within each data. However, classification of EEG signals is still challenging, because EEG signals are always contaminated by measurement artifacts, outliers, and non-standard noise sources. As a result, existing matrix classifiers may suffer from performance degradation, because they typically assume that the input EEG signals are clean...
March 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/29509691/accurate-decoding-of-short-phase-encoded-ssveps
#15
Ahmed Youssef Ali Amer, Benjamin Wittevrongel, Marc M Van Hulle
Four novel EEG signal features for discriminating phase-coded steady-state visual evoked potentials (SSVEPs) are presented, and their performance in view of target selection in an SSVEP-based brain-computer interfacing (BCI) is assessed. The novel features are based on phase estimation and correlations between target responses. The targets are decoded from the feature scores using the least squares support vector machine (LS-SVM) classifier, and it is shown that some of the proposed features compete with state-of-the-art classifiers when using short (0...
March 6, 2018: Sensors
https://www.readbyqxmd.com/read/29508123/a-bit-encoding-based-new-data-structure-for-time-and-memory-efficient-handling-of-spike-times-in-an-electrophysiological-setup
#16
Bengt Ljungquist, Per Petersson, Anders J Johansson, Jens Schouenborg, Martin Garwicz
Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources...
March 5, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29503189/volitional-modulation-of-primary-visual-cortex-activity-requires-the-basal-ganglia
#17
Ryan M Neely, Aaron C Koralek, Vivek R Athalye, Rui M Costa, Jose M Carmena
Animals acquire behaviors through instrumental conditioning. Brain-machine interfaces have used instrumental conditioning to reinforce patterns of neural activity directly, especially in frontal and motor cortices, which are a rich source of signals for voluntary action. However, evidence suggests that activity in primary sensory cortices may also reflect internally driven processes, instead of purely encoding antecedent stimuli. Here, we show that rats and mice can learn to produce arbitrary patterns of neural activity in their primary visual cortex to control an auditory cursor and obtain reward...
February 22, 2018: Neuron
https://www.readbyqxmd.com/read/29488902/a-review-of-classification-algorithms-for-eeg-based-brain-computer-interfaces-a-10-year-update
#18
Fabien Lotte, Laurent Bougrain, Andrzej Cichocki, Maureen Clerc, Marco Congedo, Alain Rakotomamonjy, Florian Yger

 Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs.
 
 Approach:
 We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs...
February 28, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29486778/a-simulation-study-on-the-effects-of-neuronal-ensemble-properties-on-decoding-algorithms-for-intracortical-brain-machine-interfaces
#19
Min-Ki Kim, Jeong-Woo Sohn, Bongsoo Lee, Sung-Phil Kim
BACKGROUND: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble...
February 27, 2018: Biomedical Engineering Online
https://www.readbyqxmd.com/read/29483564/functional-brain-states-measure-mentor-trainee-trust-during-robot-assisted-surgery
#20
Somayeh B Shafiei, Ahmed Aly Hussein, Sarah Feldt Muldoon, Khurshid A Guru
Mutual trust is important in surgical teams, especially in robot-assisted surgery (RAS) where interaction with robot-assisted interface increases the complexity of relationships within the surgical team. However, evaluation of trust between surgeons is challenging and generally based on subjective measures. Mentor-Trainee trust was defined as assessment of mentor on trainee's performance quality and approving trainee's ability to continue performing the surgery. Here, we proposed a novel method of objectively assessing mentor-trainee trust during RAS based on patterns of brain activity of surgical mentor observing trainees...
February 26, 2018: Scientific Reports
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