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Computer brain interface

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https://www.readbyqxmd.com/read/28422647/eeg-based-affect-and-workload-recognition-in-a-virtual-driving-environment-for-asd-intervention
#1
Jing Fan, Joshua W Wade, Alexandra P Key, Zachary Warren, Nilanjan Sarkar
OBJECTIVE: To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. METHODS: Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist's rating of their affective states and mental workload...
April 12, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28420382/effect-of-tdcs-stimulation-of-motor-cortex-and-cerebellum-on-eeg-classification-of-motor-imagery-and-sensorimotor-band-power
#2
Irma N Angulo-Sherman, Marisol Rodríguez-Ugarte, Nadia Sciacca, Eduardo Iáñez, José M Azorín
BACKGROUND: Transcranial direct current stimulation (tDCS) is a technique for brain modulation that has potential to be used in motor neurorehabilitation. Considering that the cerebellum and motor cortex exert influence on the motor network, their stimulation could enhance motor functions, such as motor imagery, and be utilized for brain-computer interfaces (BCIs) during motor neurorehabilitation. METHODS: A new tDCS montage that influences cerebellum and either right-hand or feet motor area is proposed and validated with a simulation of electric field...
April 19, 2017: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/28420129/hand-motion-detection-in-fnirs-neuroimaging-data
#3
Mohammadreza Abtahi, Amir Mohammad Amiri, Dennis Byrd, Kunal Mankodiya
As the number of people diagnosed with movement disorders is increasing, it becomes vital to design techniques that allow the better understanding of human brain in naturalistic settings. There are many brain imaging methods such as fMRI, SPECT, and MEG that provide the functional information of the brain. However, these techniques have some limitations including immobility, cost, and motion artifacts. One of the most emerging portable brain scanners available today is functional near-infrared spectroscopy (fNIRS)...
April 15, 2017: Healthcare (Basel, Switzerland)
https://www.readbyqxmd.com/read/28417848/dynamic-range-adaptation-in-primary-motor-cortical-populations
#4
Robert G Rasmussen, Andrew Schwartz, Steven M Chase
Neural populations from various sensory regions demonstrate dynamic range adaptation in response to changes in the statistical distribution of their input stimuli. These adaptations help optimize the transmission of information about sensory inputs. Here, we show a similar effect in the firing rates of primary motor cortical cells. We trained monkeys to operate a brain-computer interface in both two- and three-dimensional virtual environments. We found that neurons in primary motor cortex exhibited a change in the amplitude of their directional tuning curves between the two tasks...
April 18, 2017: ELife
https://www.readbyqxmd.com/read/28412441/noise-robust-cortical-tracking-of-attended-speech-in-real-world-acoustic-scenes
#5
Søren Asp Fuglsang, Torsten Dau, Jens Hjortkjær
Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is attending to in a two-talker situation. It is currently unclear how this generalizes to more complex sound environments. Behaviorally, speech perception is robust to the acoustic distortions that listeners typically encounter in everyday life, but it is unknown whether this is mirrored by a noise-robust neural tracking of attended speech...
April 12, 2017: NeuroImage
https://www.readbyqxmd.com/read/28410052/robust-averaging-of-covariances-for-eeg-recordings-classification-in-motor-imagery-brain-computer-interfaces
#6
Takashi Uehara, Matteo Sartori, Toshihisa Tanaka, Simone Fiori
The estimation of covariance matrices is of prime importance to analyze the distribution of multivariate signals. In motor imagery-based brain-computer interfaces (MI-BCI), covariance matrices play a central role in the extraction of features from recorded electroencephalograms (EEGs); therefore, correctly estimating covariance is crucial for EEG classification. This letter discusses algorithms to average sample covariance matrices (SCMs) for the selection of the reference matrix in tangent space mapping (TSM)-based MI-BCI...
April 14, 2017: Neural Computation
https://www.readbyqxmd.com/read/28407016/learning-from-label-proportions-in-brain-computer-interfaces-online-unsupervised-learning-with-guarantees
#7
David Hübner, Thibault Verhoeven, Konstantin Schmid, Klaus-Robert Müller, Michael Tangermann, Pieter-Jan Kindermans
OBJECTIVE: Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder, and thus to achieve a reliable unsupervised calibrationless decoding with a guarantee to recover the true class means...
2017: PloS One
https://www.readbyqxmd.com/read/28406932/a-comparison-of-stimulus-types-in-online-classification-of-the-p300-speller-using-language-models
#8
William Speier, Aniket Deshpande, Lucy Cui, Nand Chandravadia, Dustin Roberts, Nader Pouratian
The P300 Speller is a common brain-computer interface communication system. There are many parallel lines of research underway to overcome the system's low signal to noise ratio and thereby improve performance, including using famous face stimuli and integrating language information into the classifier. While both have been shown separately to provide significant improvements, the two methods have not yet been implemented together to demonstrate that the improvements are complimentary. The goal of this study is therefore twofold...
2017: PloS One
https://www.readbyqxmd.com/read/28393761/enhancing-clinical-communication-assessments-using-an-audiovisual-bci-for-patients-with-disorders-of-consciousness
#9
Fei Wang, Yanbin He, Jun Qu, Qiuyou Xie, Qing Lin, Xiaoxiao Ni, Yan Chen, Steven Laureys, Ronghao Yu, Yuanqing Li
OBJECTIVE: The JFK Coma Recovery Scale-Revised (JFK CRS-R), a behavioral observation scale, is widely used in the clinical diagnosis/assessment of patients with disorders of consciousness (DOC). However, the JFK CRS-R is associated with a high rate of misdiagnosis (approximately 40%) because DOC patients cannot provide sufficient behavioral responses. A brain-computer interface (BCI) that detects command/intention-specific changes in electroencephalography (EEG) signals without the need for behavioral expression may provide an alternative method...
April 10, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28391211/p300-based-asynchronous-brain-computer-interface-for-environmental-control-system
#10
Eda Akman Aydin, Omer Faruk Bay, Inan Guler
An Asynchronous Brain Computer Interface (A-BCI) determines whether or not a subject is on control state, and produces control commands only in case of subject's being on control state. In this study, we propose a novel P300 based A-BCI algorithm that distinguishes control state and non-control state of users. Furthermore, A-BCI algorithm combined with a dynamic stopping function that enables users to select control command independent from a fixed number of intensification sequence. The proposed P300 based A-BCI algorithm uses classification patterns to determine control state and uses optimal operating point of receiver operating characteristics (ROC) curve for dynamic stopping function...
April 4, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28385624/weighted-spatial-based-geometric-scheme-as-an-efficient-algorithm-for-analyzing-single-trial-eegs-to-improve-cue-based-bci-classification
#11
Fatemeh Alimardani, Reza Boostani, Benjamin Blankertz
There is a growing interest in analyzing the geometrical behavior of electroencephalogram (EEG) covariance matrix in the context of brain computer interface (BCI). The bottleneck of the current Riemannian framework is the bias of the mean vector of EEG signals to the noisy trials, which deteriorates the covariance matrix in the manifold space. This study presents a spatial weighting scheme to reduce the effect of noisy trials on the mean vector. To assess the proposed method, dataset IIa from BCI competition IV, containing the EEG trials of 9 subjects performing four mental tasks, was utilized...
March 22, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28379187/a-novel-hybrid-mental-spelling-application-based-on-eye-tracking-and-ssvep-based-bci
#12
Piotr Stawicki, Felix Gembler, Aya Rezeika, Ivan Volosyak
Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users...
April 5, 2017: Brain Sciences
https://www.readbyqxmd.com/read/28375650/emerging-frontiers-of-neuroengineering-a-network-science-of-brain-connectivity
#13
Danielle S Bassett, Ankit N Khambhati, Scott T Grafton
Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph...
March 27, 2017: Annual Review of Biomedical Engineering
https://www.readbyqxmd.com/read/28373984/evaluation-of-a-compact-hybrid-brain-computer-interface-system
#14
Jaeyoung Shin, Klaus-Robert Müller, Christoph H Schmitz, Do-Won Kim, Han-Jeong Hwang
We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects' forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28368689/text-entry-rate-of-access-interfaces-used-by-people-with-physical-disabilities-a-systematic-review
#15
Heidi Horstmann Koester, Sajay Arthanat
This study systematically reviewed the research on assistive technology (AT) access interfaces used for text entry, and conducted a quantitative synthesis of text entry rates (TER) associated with common interfaces. We searched 10 databases and included studies in which: typing speed was reported in words per minute (WPM) or equivalent; the access interface was available for public use; and individuals with physical impairments were in the study population. For quantitative synthesis, we used only the TER reported for individuals with physical impairments...
April 3, 2017: Assistive Technology: the Official Journal of RESNA
https://www.readbyqxmd.com/read/28367834/electroencephalographic-identifiers-of-motor-adaptation-learning
#16
Ozan Ozdenizci, Mustafa Yalcin, Ahmetcan Erdogan, Volkan Patoglu, Moritz Grosse-Wentrup, Mujdat Cetin
OBJECTIVE: Recent brain-computer interface (BCI) assisted stroke rehabilitation protocols tend to focus on sensorimotor activity of the brain. Relying on evidence claiming that a variety of brain rhythms beyond sensorimotor areas are related to the extent of motor deficits, we propose to identify neural correlates of motor learning beyond sensorimotor areas spatially and spectrally for further use in novel BCI-assisted neurorehabilitation settings. APPROACH: Electroencephalographic (EEG) data were recorded from healthy subjects participating in a physical force-field adaptation task involving reaching movements through a robotic handle...
April 3, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28363483/restoration-of-reaching-and-grasping-movements-through-brain-controlled-muscle-stimulation-in-a-person-with-tetraplegia-a-proof-of-concept-demonstration
#17
A Bolu Ajiboye, Francis R Willett, Daniel R Young, William D Memberg, Brian A Murphy, Jonathan P Miller, Benjamin L Walter, Jennifer A Sweet, Harry A Hoyen, Michael W Keith, P Hunter Peckham, John D Simeral, John P Donoghue, Leigh R Hochberg, Robert F Kirsch
BACKGROUND: People with chronic tetraplegia, due to high-cervical spinal cord injury, can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as functional electrical stimulation (FES). Users typically command FES systems through other preserved, but unrelated and limited in number, volitional movements (eg, facial muscle activity, head movements, shoulder shrugs). We report the findings of an individual with traumatic high-cervical spinal cord injury who coordinated reaching and grasping movements using his own paralysed arm and hand, reanimated through implanted FES, and commanded using his own cortical signals through an intracortical brain-computer interface (iBCI)...
March 28, 2017: Lancet
https://www.readbyqxmd.com/read/28358916/classifying-three-imaginary-states-of-the-same-upper-extremity-using-time-domain-features
#18
Mojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon
Brain-computer interface (BCI) allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG) signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area...
2017: PloS One
https://www.readbyqxmd.com/read/28357991/a-new-multivariate-empirical-mode-decomposition-method-for-improving-the-performance-of-ssvep-based-brain-computer-interface
#19
Yi-Feng Chen, Kiran Atal, Shengquan Xie, Quan Liu
OBJECTIVE: Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications. APPROACH: Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts...
March 30, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28351691/optical-flow-analysis-toolbox-for-characterization-of-spatiotemporal-dynamics-in-mesoscale-optical-imaging-of-brain-activity
#20
Navvab Afrashteh, Samsoon Inayat, Mostafa Mohsenvand, Majid H Mohajerani
Wide-field optical imaging techniques constitute powerful tools to investigate mesoscale neuronal activity. The sampled data constitutes a sequence of image frames in which one can investigate the flow of brain activity starting and terminating at source and sink locations respectively. Approaches to the analyses of information flow include qualitative assessment to identify sources and sinks of activity as well as their trajectories, and quantitative measurements based on computing the temporal variation of the intensity of pixels...
March 25, 2017: NeuroImage
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