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Brain computer interface

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https://www.readbyqxmd.com/read/28634442/mild-traumatic-brain-injury-evokes-pyramidal-neuron-axon-initial-segment-plasticity-and-diffuse-presynaptic-inhibitory-terminal-loss
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
Kevin T Huang, Ziev B Moses, John H Chi
No abstract text is available yet for this article.
May 1, 2017: Neurosurgery
https://www.readbyqxmd.com/read/28573984/a-gaussian-mixture-model-based-adaptive-classifier-for-fnirs-brain-computer-interfaces-and-its-testing-via-simulation
#15
Zheng Li, Yi-Han Jiang, Lian Duan, Chao-Zhe Zhu
OBJECTIVE: Functional near infra-red spectroscopy (fNIRS) is a promising brain imaging technology for brain-computer interfaces (BCI). Future clinical uses of fNIRS will likely require operation over long time spans, during which neural activation patterns may change. However, current decoders for fNIRS signals are not designed to handle changing activation patterns. The objective of this study is to test via simulations a new adaptive decoder for fNIRS signals, the Gaussian mixture model adaptive classifier (GMMAC)...
June 2, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28573983/neuromorphic-neural-interfaces-from-neurophysiological-inspiration-to-biohybrid-coupling-with-nervous-systems
#16
Frédéric D Broccard, Siddharth Joshi, Jun Wang, Gert Cauwenberghs
OBJECTIVE: Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks...
June 2, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28572817/development-of-a-novel-motor-imagery-control-technique-and-application-in-a-gaming-environment
#17
Ting Li, Jinhua Zhang, Tao Xue, Baozeng Wang
We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player's BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28566997/eeg-based-quantification-of-cortical-current-density-and-dynamic-causal-connectivity-generalized-across-subjects-performing-bci-monitored-cognitive-tasks
#18
Hristos Courellis, Tim Mullen, Howard Poizner, Gert Cauwenberghs, John R Iversen
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28562664/towards-the-automated-localisation-of-targets-in-rapid-image-sifting-by-collaborative-brain-computer-interfaces
#19
Ana Matran-Fernandez, Riccardo Poli
The N2pc is a lateralised Event-Related Potential (ERP) that signals a shift of attention towards the location of a potential object of interest. We propose a single-trial target-localisation collaborative Brain-Computer Interface (cBCI) that exploits this ERP to automatically approximate the horizontal position of targets in aerial images. Images were presented by means of the rapid serial visual presentation technique at rates of 5, 6 and 10 Hz. We created three different cBCIs and tested a participant selection method in which groups are formed according to the similarity of participants' performance...
2017: PloS One
https://www.readbyqxmd.com/read/28562624/application-of-a-single-flicker-online-ssvep-bci-for-spatial-navigation
#20
Jingjing Chen, Dan Zhang, Andreas K Engel, Qin Gong, Alexander Maye
A promising approach for brain-computer interfaces (BCIs) employs the steady-state visual evoked potential (SSVEP) for extracting control information. Main advantages of these SSVEP BCIs are a simple and low-cost setup, little effort to adjust the system parameters to the user and comparatively high information transfer rates (ITR). However, traditional frequency-coded SSVEP BCIs require the user to gaze directly at the selected flicker stimulus, which is liable to cause fatigue or even photic epileptic seizures...
2017: PloS One
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