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https://www.readbyqxmd.com/read/27909307/motor-neuron-disease-brain-computer-interface-unlocks-the-mind-of-a-patient-with-als
#1
Heather Wood
No abstract text is available yet for this article.
December 2, 2016: Nature Reviews. Neurology
https://www.readbyqxmd.com/read/27900953/feedback-control-policies-employed-by-people-using-intracortical-brain-computer-interfaces
#2
Francis R Willett, Chethan Pandarinath, Beata Jarosiewicz, Brian A Murphy, William D Memberg, Christine H Blabe, Jad Saab, Benjamin L Walter, Jennifer A Sweet, Jonathan P Miller, Jaimie M Henderson, Krishna V Shenoy, John D Simeral, Leigh R Hochberg, Robert F Kirsch, A Bolu Ajiboye
OBJECTIVE: When using an intracortical BCI (iBCI), users modulate their neural population activity to move an effector towards a target, stop accurately, and correct for movement errors. We call the rules that govern this modulation a 'feedback control policy'. A better understanding of these policies may inform the design of higher-performing neural decoders. APPROACH: We studied how three participants in the BrainGate2 pilot clinical trial used an iBCI to control a cursor in a 2D target acquisition task...
November 30, 2016: Journal of Neural Engineering
https://www.readbyqxmd.com/read/27900952/a-novel-deep-learning-approach-for-classification-of-eeg-motor-imagery-signals
#3
Yousef Rezaei Tabar, Ugur Halici
OBJECTIVE: Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. APPROACH: In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals...
November 30, 2016: Journal of Neural Engineering
https://www.readbyqxmd.com/read/27900950/automated-selection-of-brain-regions-for-real-time-fmri-brain-computer-interfaces
#4
Michael Lührs, Bettina Sorger, Rainer Goebel, Fabrizio Esposito
OBJECTIVE: Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI...
November 30, 2016: Journal of Neural Engineering
https://www.readbyqxmd.com/read/27891083/toward-a-p300-based-brain-computer-interface-for-aphasia-rehabilitation-after-stroke-presentation-of-theoretical-considerations-and-a-pilot-feasibility-study
#5
Sonja C Kleih, Lea Gottschalt, Eva Teichlein, Franz X Weilbach
People with post-stroke motor aphasia know what they would like to say but cannot express it through motor pathways due to disruption of cortical circuits. We present a theoretical background for our hypothesized connection between attention and aphasia rehabilitation and suggest why in this context, Brain-Computer Interface (BCI) use might be beneficial for patients diagnosed with aphasia. Not only could BCI technology provide a communication tool, it might support neuronal plasticity by activating language circuits and thereby boost aphasia recovery...
2016: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/27882256/eeg-triggered-functional-electrical-stimulation-therapy-for-restoring-upper-limb-function-in-chronic-stroke-with-severe-hemiplegia
#6
Cesar Marquez-Chin, Aaron Marquis, Milos R Popovic
We report the therapeutic effects of integrating brain-computer interfacing technology and functional electrical stimulation therapy to restore upper limb reaching movements in a 64-year-old man with severe left hemiplegia following a hemorrhagic stroke he sustained six years prior to this study. He completed 40 90-minute sessions of functional electrical stimulation therapy using a custom-made neuroprosthesis that facilitated 5 different reaching movements. During each session, the participant attempted to reach with his paralyzed arm repeatedly...
2016: Case Reports in Neurological Medicine
https://www.readbyqxmd.com/read/27880768/real-time-control-of-an-articulatory-based-speech-synthesizer-for-brain-computer-interfaces
#7
Florent Bocquelet, Thomas Hueber, Laurent Girin, Christophe Savariaux, Blaise Yvert
Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips) into intelligible speech...
November 2016: PLoS Computational Biology
https://www.readbyqxmd.com/read/27875232/a-novel-algorithm-for-learning-sparse-spatio-spectral-patterns-for-event-related-potentials
#8
Chaohua Wu, Ke Lin, Wei Wu, Xiaorong Gao
Recent years have witnessed brain-computer interface (BCI) as a promising technology for integrating human intelligence and machine intelligence. Currently, event-related potential (ERP)-based BCI is an important branch of noninvasive electroencephalogram (EEG)-based BCIs. Extracting ERPs from a limited number of trials remains challenging due to their low signal-to-noise ratio (SNR) and low spatial resolution caused by volume conduction. In this paper, we propose a probabilistic model for trial-by-trial concatenated EEG, in which the concatenated ERPs are expressed as a linear combination of a set of discrete sine and cosine bases...
November 17, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/27875130/discriminative-ocular-artifact-correction-for-feature-learning-in-eeg-analysis
#9
Xinyang Li, Cuntai Guan, Haihong Zhang, Kai Keng Ang
Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for ICA-based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact...
November 16, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/27872367/the-neurobiology-of-uncertainty-implications-for-statistical-learning
#10
REVIEW
Uri Hasson
The capacity for assessing the degree of uncertainty in the environment relies on estimating statistics of temporally unfolding inputs. This, in turn, allows calibration of predictive and bottom-up processing, and signalling changes in temporally unfolding environmental features. In the last decade, several studies have examined how the brain codes for and responds to input uncertainty. Initial neurobiological experiments implicated frontoparietal and hippocampal systems, based largely on paradigms that manipulated distributional features of visual stimuli...
January 5, 2017: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
https://www.readbyqxmd.com/read/27858227/plug-play-brain-computer-interfaces-for-effective-active-and-assisted-living-control
#11
Niccolò Mora, Ilaria De Munari, Paolo Ciampolini, José Del R Millán
Brain-Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including Active and Assisted Living (AAL) systems control. Interaction barriers could be removed indeed, enabling user with severe motor impairments to gain control over a wide range of AAL features. In this paper, a cost-effective BCI solution, targeted (but not limited) to AAL system control is presented...
November 17, 2016: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/27857680/spiking-neural-networks-based-on-oxram-synapses-for-real-time-unsupervised-spike-sorting
#12
Thilo Werner, Elisa Vianello, Olivier Bichler, Daniele Garbin, Daniel Cattaert, Blaise Yvert, Barbara De Salvo, Luca Perniola
In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes...
2016: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/27852775/eeg-oscillations-are-modulated-in-different-behavior-related-networks-during-rhythmic-finger-movements
#13
Martin Seeber, Reinhold Scherer, Gernot R Müller-Putz
: Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. We separated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for α (10-12 Hz) and β (18-24 Hz), but increase for high γ (60-80 Hz) frequencies during the entire movement period...
November 16, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/27849545/open-access-dataset-for-eeg-nirs-single-trial-classification
#14
Jaeyoung Shin, Alexander von Luhmann, Benjamin Blankertz, Do-Won Kim, Jichai Jeong, Han-Jeong Hwang, Klaus-Robert Muller
We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we con-ducted two BCI experiments (left vs. right hand motor imagery; mental arithmetic vs. resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capability of discriminating different mental states can be en-hanced by using a hybrid approach, when comparing to single modality analyses...
November 11, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/27849543/a-benchmark-dataset-for-ssvep-based-brain-computer-interfaces
#15
Yijun Wang, Xiaogang Chen, Xiaorong Gao, Shangkai Gao
This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain-computer interface (BCI) speller. The dataset consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed a cue-guided target selecting task. The virtual keyboard of the speller was composed of 40 visual flickers, which were coded using a joint frequency and phase modulation (JFPM) approach. The stimulation frequencies ranged from 8Hz to 15...
November 10, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/27845666/riemannian-approaches-in-brain-computer-interfaces-a-review
#16
Florian Yger, Maxime Berar, Fabien Lotte
Although promising from numerous applications, current Brain-Computer Interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the non-stationarity of ElectroEncephaloGraphic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers...
November 9, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/27845150/the-extraction-of-motion-onset-vep-bci-features-based-on-deep-learning-and-compressed-sensing
#17
Teng Ma, Hui Li, Hao Yang, Xulin Lv, Peiyang Li, Tiejun Liu, Dezhong Yao, Peng Xu
BACKGROUND: Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. NEW METHOD: In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance...
November 11, 2016: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/27833542/adaptive-automation-triggered-by-eeg-based-mental-workload-index-a-passive-brain-computer-interface-application-in-realistic-air-traffic-control-environment
#18
Pietro Aricò, Gianluca Borghini, Gianluca Di Flumeri, Alfredo Colosimo, Stefano Bonelli, Alessia Golfetti, Simone Pozzi, Jean-Paul Imbert, Géraud Granger, Raïlane Benhacene, Fabio Babiloni
Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e...
2016: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/27831885/an-asynchronous-p300-based-brain-computer-interface-web-browser-for-severely-disabled-people
#19
Victor Martinez-Cagigal, Javier Gomez-Pilar, Daniel Alvarez, Roberto Hornero
This paper presents an electroencephalographic (EEG) P300-based brain-computer interface (BCI) Internet browser. The system uses the "odd-ball" row-col paradigm for generating the P300 evoked potentials on the scalp of the user, which are immediately processed and translated into web browser commands. There were previous approaches for controlling a BCI web browser. However, to the best of our knowledge, none of them was focused on an assistive context, failing to test their applications with a suitable number of end users...
October 31, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/27830790/a-brain-spine-interface-alleviating-gait-deficits-after-spinal-cord-injury-in-primates
#20
Marco Capogrosso, Tomislav Milekovic, David Borton, Fabien Wagner, Eduardo Martin Moraud, Jean-Baptiste Mignardot, Nicolas Buse, Jerome Gandar, Quentin Barraud, David Xing, Elodie Rey, Simone Duis, Yang Jianzhong, Wai Kin D Ko, Qin Li, Peter Detemple, Tim Denison, Silvestro Micera, Erwan Bezard, Jocelyne Bloch, Grégoire Courtine
Spinal cord injury disrupts the communication between the brain and the spinal circuits that orchestrate movement. To bypass the lesion, brain-computer interfaces have directly linked cortical activity to electrical stimulation of muscles, and have thus restored grasping abilities after hand paralysis. Theoretically, this strategy could also restore control over leg muscle activity for walking. However, replicating the complex sequence of individual muscle activation patterns underlying natural and adaptive locomotor movements poses formidable conceptual and technological challenges...
November 9, 2016: Nature
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