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Computer brain interface

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https://www.readbyqxmd.com/read/28645842/opennft-an-open-source-python-matlab-framework-for-real-time-fmri-neurofeedback-training-based-on-activity-connectivity-and-multivariate-pattern-analysis
#1
Yury Koush, John Ashburner, Evgeny Prilepin, Ronald Sladky, Peter Zeidman, Sergei Bibikov, Frank Scharnowski, Artem Nikonorov, Dimitri Van De Ville
Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments...
June 20, 2017: NeuroImage
https://www.readbyqxmd.com/read/28644398/a-novel-wearable-forehead-eog-measurement-system-for-human-computer-interfaces
#2
Jeong Heo, Heenam Yoon, Kwang Suk Park
Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is generated by eye movements and can be measured with electrodes placed around the eye. In this study, we proposed a new practical electrode position on the forehead to measure EOG signals, and we developed a wearable forehead EOG measurement system for use in Human Computer/Machine interfaces (HCIs/HMIs)...
June 23, 2017: Sensors
https://www.readbyqxmd.com/read/28643185/keeping-disability-in-mind-a-case-study-in-implantable-brain-computer-interface-research
#3
Laura Specker Sullivan, Eran Klein, Tim Brown, Matthew Sample, Michelle Pham, Paul Tubig, Raney Folland, Anjali Truitt, Sara Goering
Brain-Computer Interface (BCI) research is an interdisciplinary area of study within Neural Engineering. Recent interest in end-user perspectives has led to an intersection with user-centered design (UCD). The goal of user-centered design is to reduce the translational gap between researchers and potential end users. However, while qualitative studies have been conducted with end users of BCI technology, little is known about individual BCI researchers' experience with and attitudes towards UCD. Given the scientific, financial, and ethical imperatives of UCD, we sought to gain a better understanding of practical and principled considerations for researchers who engage with end users...
June 22, 2017: Science and Engineering Ethics
https://www.readbyqxmd.com/read/28643118/comparison-of-photon-organ-and-effective-dose-coefficients-for-pimal-stylized-phantom-in-bent-positions-in-standard-irradiation-geometries
#4
Shaheen Dewji, K Lisa Reed, Mauritius Hiller
Computational phantoms with articulated arms and legs have been constructed to enable the estimation of radiation dose in different postures. Through a graphical user interface, the Phantom wIth Moving Arms and Legs (PIMAL) version 4.1.0 software can be employed to articulate the posture of a phantom and generate a corresponding input deck for the Monte Carlo N-Particle (MCNP) radiation transport code. In this work, photon fluence-to-dose coefficients were computed using PIMAL to compare organ and effective doses for a stylized phantom in the standard upright position with those for phantoms in realistic work postures...
June 22, 2017: Radiation and Environmental Biophysics
https://www.readbyqxmd.com/read/28639486/estimated-prevalence-of-the-target-population-for-brain-computer-interface-neurotechnology-in-the-netherlands
#5
Elmar G M Pels, Erik J Aarnoutse, Nick F Ramsey, Mariska J Vansteensel
BACKGROUND: People who suffer from paralysis have difficulties participating in society. Particularly burdensome is the locked-in syndrome (LIS). LIS patients are not able to move and speak but are cognitively healthy. They rely on assistive technology to interact with the world and may benefit from neurotechnological advances. Optimal research and design of such aids requires a well-defined target population. However, the LIS population is poorly characterized and the number of patients in this condition is unknown...
June 1, 2017: Neurorehabilitation and Neural Repair
https://www.readbyqxmd.com/read/28638316/a-new-generation-of-brain-computer-interfaces-driven-by-discovery-of-latent-eeg-fmri-linkages-using-tensor-decomposition
#6
Gopikrishna Deshpande, D Rangaprakash, Luke Oeding, Andrzej Cichocki, Xiaoping P Hu
A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the other hand, BCIs based on functional magnetic resonance imaging (fMRI) are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28634442/mild-traumatic-brain-injury-evokes-pyramidal-neuron-axon-initial-segment-plasticity-and-diffuse-presynaptic-inhibitory-terminal-loss
#7
Michal Vascak, Jianli Sun, Matthew Baer, Kimberle M Jacobs, John T Povlishock
The axon initial segment (AIS) is the site of action potential (AP) initiation, thus a crucial regulator of neuronal activity. In excitatory pyramidal neurons, the high density of voltage-gated sodium channels (NaV1.6) at the distal AIS regulates AP initiation. A surrogate AIS marker, ankyrin-G (ankG) is a structural protein regulating neuronal functional via clustering voltage-gated ion channels. In neuronal circuits, changes in presynaptic input can alter postsynaptic output via AIS structural-functional plasticity...
2017: Frontiers in Cellular Neuroscience
https://www.readbyqxmd.com/read/28627505/monitoring-alert-and-drowsy-states-by-modeling-eeg-source-nonstationarity
#8
Sheng-Hsiou Hsu, Tzyy-Ping Jung
As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA)...
June 19, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28620273/state-dependent-decoding-algorithms-improve-the-performance-of-a-bidirectional-bmi-in-anesthetized-rats
#9
Vito De Feo, Fabio Boi, Houman Safaai, Arno Onken, Stefano Panzeri, Alessandro Vato
Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28615329/physiological-properties-of-brain-machine-interface-input-signals
#10
Marc W Slutzky, Robert D Flint
Brain machine interfaces (BMIs), also called brain computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically-viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably the first of these is the selection of brain signals used to control BMIs. Here, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date...
June 14, 2017: Journal of Neurophysiology
https://www.readbyqxmd.com/read/28614588/architecture-of-the-paracellular-channels-formed-by-claudins-of-the-blood-brain-barrier-tight-junctions
#11
Flaviyan Jerome Irudayanathan, Nan Wang, Xiaoyi Wang, Shikha Nangia
Tight junctions (TJs) are key players in determining tissue-specific paracellular permeability across epithelial and endothelial membranes. Claudin proteins, the primary determinants of TJs structure and functionality, assemble in paracellular spaces to form channels and pores that are charge and size selective. Here, using molecular dynamics (MD) simulations, we elucidate the molecular assembly of claudin-3 and claudin-5 proteins of blood-brain barrier TJs. Despite having a high degree of sequence and structural similarity, these two claudins form different types of cis-interactions...
June 14, 2017: Annals of the New York Academy of Sciences
https://www.readbyqxmd.com/read/28613237/major-depression-detection-from-eeg-signals-using-kernel-eigen-filter-bank-common-spatial-patterns
#12
Shih-Cheng Liao, Chien-Te Wu, Hao-Chuan Huang, Wei-Teng Cheng, Yi-Hung Liu
Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i...
June 14, 2017: Sensors
https://www.readbyqxmd.com/read/28611620/atpp-a-pipeline-for-automatic-tractography-based-brain-parcellation
#13
Hai Li, Lingzhong Fan, Junjie Zhuo, Jiaojian Wang, Yu Zhang, Zhengyi Yang, Tianzi Jiang
There is a longstanding effort to parcellate brain into areas based on micro-structural, macro-structural, or connectional features, forming various brain atlases. Among them, connectivity-based parcellation gains much emphasis, especially with the considerable progress of multimodal magnetic resonance imaging in the past two decades. The Brainnetome Atlas published recently is such an atlas that follows the framework of connectivity-based parcellation. However, in the construction of the atlas, the deluge of high resolution multimodal MRI data and time-consuming computation poses challenges and there is still short of publically available tools dedicated to parcellation...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28611611/a-synchronous-motor-imagery-based-neural-physiological-paradigm-for-brain-computer-interface-speller
#14
Lei Cao, Bin Xia, Oladazimi Maysam, Jie Li, Hong Xie, Niels Birbaumer
Brain Computer Interface (BCI) speller is a typical BCI-based application to help paralyzed patients express their thoughts. This paper proposed a novel motor imagery based BCI speller with Oct-o-spell paradigm for word input. Furthermore, an intelligent input method was used for improving the performance of the BCI speller. For the English word spelling experiment, we compared synchronous control with previous asynchronous control under the same experimental condition. There were no significant differences between these two control methods in the classification accuracy, information transmission rate (ITR) or letters per minute (LPM)...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28611579/stacked-autoencoders-for-the-p300-component-detection
#15
Lukáš Vařeka, Pavel Mautner
Novel neural network training methods (commonly referred to as deep learning) have emerged in recent years. Using a combination of unsupervised pre-training and subsequent fine-tuning, deep neural networks have become one of the most reliable classification methods. Since deep neural networks are especially powerful for high-dimensional and non-linear feature vectors, electroencephalography (EEG) and event-related potentials (ERPs) are one of the promising applications. Furthermore, to the authors' best knowledge, there are very few papers that study deep neural networks for EEG/ERP data...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28602817/on-memories-neural-ensembles-and-mental-flexibility
#16
Dimitris A Pinotsis, Scott L Brincat, Earl K Miller
Memories are assumed to be represented by groups of co-activated neurons, called neural ensembles. Describing ensembles is a challenge: complexity of the underlying micro-circuitry is immense. Current approaches use a piecemeal fashion, focusing on single neurons and employing local measures like pairwise correlations. We introduce an alternative approach that identifies ensembles and describes the effective connectivity between them in a holistic fashion. It also links the oscillatory frequencies observed in ensembles with the spatial scales at which activity is expressed...
June 9, 2017: NeuroImage
https://www.readbyqxmd.com/read/28598972/non-invasive-transmission-of-sensorimotor-information-in-humans-using-an-eeg-focused-ultrasound-brain-to-brain-interface
#17
Wonhye Lee, Suji Kim, Byeongnam Kim, Chungki Lee, Yong An Chung, Laehyun Kim, Seung-Schik Yoo
We present non-invasive means that detect unilateral hand motor brain activity from one individual and subsequently stimulate the somatosensory area of another individual, thus, enabling the remote hemispheric link between each brain hemisphere in humans. Healthy participants were paired as a sender and a receiver. A sender performed a motor imagery task of either right or left hand, and associated changes in the electroencephalogram (EEG) mu rhythm (8-10 Hz) originating from either hemisphere were programmed to move a computer cursor to a target that appeared in either left or right of the computer screen...
2017: PloS One
https://www.readbyqxmd.com/read/28596725/a-ternary-brain-computer-interface-based-on-single-trial-readiness-potentials-of-self-initiated-fine-movements-a-diversified-classification-scheme
#18
Elias Abou Zeid, Alborz Rezazadeh Sereshkeh, Benjamin Schultz, Tom Chau
In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have mainly attempted binary single-trial classification of RP. An RP-based BCI with three or more states would expand the options for functional control...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28588442/a-multifunctional-brain-computer-interface-intended-for-home-use-an-evaluation-with-healthy-participants-and-potential-end-users-with-dry-and-gel-based-electrodes
#19
Ivo Käthner, Sebastian Halder, Christoph Hintermüller, Arnau Espinosa, Christoph Guger, Felip Miralles, Eloisa Vargiu, Stefan Dauwalder, Xavier Rafael-Palou, Marc Solà, Jean M Daly, Elaine Armstrong, Suzanne Martin, Andrea Kübler
Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28586492/advances-in-implanted-brain-computer-interfaces-allow-for-independent-communication-in-a-locked-in-patient
#20
Kevin T Huang, Ziev B Moses, John H Chi
No abstract text is available yet for this article.
May 1, 2017: Neurosurgery
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