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https://www.readbyqxmd.com/read/28211450/corrigendum-brain-computer-interfaces-for-communication-and-rehabilitation
#1
Ujwal Chaudhary, Niels Birbaumer, Ander Ramos-Murguialday
No abstract text is available yet for this article.
February 17, 2017: Nature Reviews. Neurology
https://www.readbyqxmd.com/read/28208734/towards-building-a-computer-aided-education-system-for-special-students-using-wearable-sensor-technologies
#2
Raja Majid Mehmood, Hyo Jong Lee
Human computer interaction is a growing field in terms of helping people in their daily life to improve their living. Especially, people with some disability may need an interface which is more appropriate and compatible with their needs. Our research is focused on similar kinds of problems, such as students with some mental disorder or mood disruption problems. To improve their learning process, an intelligent emotion recognition system is essential which has an ability to recognize the current emotional state of the brain...
February 8, 2017: Sensors
https://www.readbyqxmd.com/read/28207882/improving-brain-computer-interface-research-through-user-involvement-the-transformative-potential-of-integrating-civil-society-organisations-in-research-projects
#3
Bernd Carsten Stahl, Kutoma Wakunuma, Stephen Rainey, Christian Hansen
Research on Brain Computer Interfaces (BCI) often aims to provide solutions for vulnerable populations, such as individuals with diseases, conditions or disabilities that keep them from using traditional interfaces. Such research thereby contributes to the public good. This contribution to the public good corresponds to a broader drive of research and funding policy that focuses on promoting beneficial societal impact. One way of achieving this is to engage with the public. In practical terms this can be done by integrating civil society organisations (CSOs) in research...
2017: PloS One
https://www.readbyqxmd.com/read/28207382/a-portable-brain-computer-interface-platform-for-eeg-acquisition-and-decoding
#4
Colin McCrimmon, Jonathan Fu, Ming Wang, Lucas Silva Lopes, Po Wang, Alireza Karimi-Bidhendi, Charles Liu, Payam Heydari, Zoran Nenadic, An Do
OBJECTIVE: Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI. METHODS: The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen...
February 13, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28198356/a-comparison-study-of-visually-stimulated-brain-computer-and-eye-tracking-interfaces
#5
Kaori Suefusa, Toshihisa Tanaka
OBJECTIVE: Brain--computer interfacing (BCI) based on visual stimuli detects the target on a screen on which a user is focusing. The detection of the gazing target can be achieved by tracking gaze positions with a video camera, which is called eye tracking or eye tracking interfaces (ETIs). Both types of interfaces have been developed in different communities. Thus, little work on the comprehensive comparison between these two types of interfaces has been reported. This paper quantitatively compares the performance of these two interfaces on the same experimental platform...
February 15, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28197092/bci-control-of-heuristic-search-algorithms
#6
Marc Cavazza, Gabor Aranyi, Fred Charles
The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users' mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28193497/i-act-therefore-i-err-eeg-correlates-of-success-and-failure-in-a-virtual-throwing-game
#7
Boris Yazmir, Miriam Reiner
What are the neural responses to success and failure in a throwing task? To answer this question, we compared Event Related Potentials (ERPs) correlated with success and failure during a highly-ecological-virtual game. Participants played a tennis-like game in an immersive 3D virtual world, against a computer player, by controlling a virtual tennis racket with a force feedback robotic arm. Results showed that success, i.e. hitting the target, and failure, by missing the target, evoked ERP's that differ by peak, latencies, scalp signal distributions, sLORETA source estimation, and time-frequency patterns...
February 10, 2017: International Journal of Psychophysiology
https://www.readbyqxmd.com/read/28192282/classifier-transfer-with-data-selection-strategies-for-online-support-vector-machine-classification-with-class-imbalance
#8
Mario Michael Krell, Nils Wilshusen, Anett Seeland, Su Kyoung Kim
OBJECTIVE: Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the support vector machine (SVM). APPROACH: We focus on data selection strategies which limit the size of the stored training data by different inclusion, exclusion, and further dataset manipulation criteria like handling class imbalance with two new approaches...
February 13, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28185910/ultrasoft-microwire-neural-electrodes-improve-chronic-tissue-integration
#9
Zhanhong Jeff Du, Christi L Kolarcik, Takashi D Y Kozai, Silvia D Luebben, Shawn A Sapp, Xin Sally Zheng, James A Nabity, X Tracy Cui
: Chronically implanted neural multi-electrode arrays (MEA) are an essential technology for recording electrical signals from neurons and/or modulating neural activity through stimulation. However, current MEAs, regardless of the type, elicit an inflammatory response that ultimately leads to device failure. Traditionally, rigid materials like tungsten and silicon have been employed to interface with the relatively soft neural tissue. The large stiffness mismatch is thought to exacerbate the inflammatory response...
February 6, 2017: Acta Biomaterialia
https://www.readbyqxmd.com/read/28177925/signal-independent-noise-in-intracortical-brain-computer-interfaces-causes-movement-time-properties-inconsistent-with-fitts-law
#10
Francis R Willett, Brian A Murphy, William D Memberg, Christine H Blabe, Chethan Pandarinath, Benjamin L Walter, Jennifer A Sweet, Jonathan P Miller, Jaimie M Henderson, Krishna V Shenoy, Leigh R Hochberg, Robert F Kirsch, A Bolu Ajiboye
OBJECTIVE: Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text])...
February 8, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28167121/assessing-motor-imagery-in-brain-computer-interface-training-psychological-and-neurophysiological-correlates
#11
Anatoly Vasilyev, Sofya Liburkina, Lev Yakovlev, Olga Perepelkina, Alexander Kaplan
Motor imagery (MI) is considered to be a promising cognitive tool for improving motor skills as well as for rehabilitation therapy of movement disorders. It is believed that MI training efficiency could be improved by using the brain-computer interface (BCI) technology providing real-time feedback on person's mental attempts. While BCI is indeed a convenient and motivating tool for practicing MI, it is not clear whether it could be used for predicting or measuring potential positive impact of the training. In this study, we are trying to establish whether the proficiency in BCI control is associated with any of the neurophysiological or psychological correlates of motor imagery, as well as to determine possible interrelations among them...
February 4, 2017: Neuropsychologia
https://www.readbyqxmd.com/read/28161876/a-spatial-frequency-temporal-optimized-feature-sparse-representation-based-classification-method-for-motor-imagery-eeg-pattern-recognition
#12
Minmin Miao, Aimin Wang, Feixiang Liu
Effective feature extraction and classification methods are of great importance for motor imagery (MI)-based brain-computer interface (BCI) systems. The common spatial pattern (CSP) algorithm is a widely used feature extraction method for MI-based BCIs. In this work, we propose a novel spatial-frequency-temporal optimized feature sparse representation-based classification method. Optimal channels are selected based on relative entropy criteria. Significant CSP features on frequency-temporal domains are selected automatically to generate a column vector for sparse representation-based classification (SRC)...
February 4, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28157960/demonstration-of-long-distance-hazard-free-wearable-eeg-monitoring-system-using-mobile-phone-visible-light-communication
#13
Vega Pradana Rachim, Yubing Jiang, Hyeon-Seok Lee, Wan-Young Chung
A wearable electroencephalogram (EEG) is a small mobile device used for long-term brain monitoring systems. Applications of these systems include fatigue monitoring, mental/emotional monitoring, and brain-computer interfaces. However, the usage of wireless wearable EEG systems is limited due to the risks posed by the wireless RF communication radiation in a long-term exposure to the human brain. A novel microwave radiation-free system was developed by integrating visible light communication technology into a wearable EEG device...
January 23, 2017: Optics Express
https://www.readbyqxmd.com/read/28151957/correlation-based-model-of-artificially-induced-plasticity-in-motor-cortex-by-a-bidirectional-brain-computer-interface
#14
Guillaume Lajoie, Nedialko I Krouchev, John F Kalaska, Adrienne L Fairhall, Eberhard E Fetz
Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes...
February 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28145274/the-hybrid-bci-system-for-movement-control-by-combining-motor-imagery-and-moving-onset-visual-evoked-potential
#15
Teng Ma, Hui Li, Lili Deng, Hao Yang, Xulin Lv, Peiyang Li, Fali Li, Rui Zhang, Tiejun Liu, Dezhong Yao, Peng Xu
OBJECTIVE: Movement control is an important application for EEG-BCI (EEG-based brain-computer interface) systems. A single-modality BCI cannot provide an efficient and natural control strategy, but a hybrid BCI system that combines two or more different tasks can effectively overcome the drawbacks encountered in single-modality BCI control. APPROACH: In current paper, we developed a new hybrid BCI system by combining MI (motor imagery) and mVEP (motion-onset visual evoked potential), aiming to realize the more efficient 2D movement control of a cursor...
February 1, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28141803/brain-computer-interface-based-communication-in-the-completely-locked-in-state
#16
Ujwal Chaudhary, Bin Xia, Stefano Silvoni, Leonardo G Cohen, Niels Birbaumer
Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain-computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)-two of them in permanent CLIS and two entering the CLIS without reliable means of communication-learned to answer personal questions with known answers and open questions all requiring a "yes" or "no" thought using frontocentral oxygenation changes measured with fNIRS...
January 2017: PLoS Biology
https://www.readbyqxmd.com/read/28140332/robust-electroencephalogram-phase-estimation-with-applications-in-brain-computer-interface-systems
#17
Esmaeil Seraj, Reza Sameni
OBJECTIVE: In this study, a robust method is developed for frequency-specific electroencephalogram (EEG) phase extraction using the analytic representation of the EEG. Based on recent theoretical findings in this area, it is shown that some of the phase variations-previously associated to the brain response-are systematic side-effects of the methods used for EEG phase calculation, especially during low analytical amplitude segments of the EEG. APPROACH: With this insight, the proposed method generates randomized ensembles of the EEG phase using minor perturbations in the zero-pole loci of narrow-band filters, followed by phase estimation using the signal's analytical form and ensemble averaging over the randomized ensembles to obtain a robust EEG phase and frequency...
January 31, 2017: Physiological Measurement
https://www.readbyqxmd.com/read/28131888/eeg-neural-correlates-of-goal-directed-movement-intention
#18
Joana Pereira, Patrick Ofner, Andreas Schwarz, Andreea Ioana Sburlea, Gernot R Müller-Putz
Using low-frequency time-domain electroencephalographic (EEG) signals we show, for the same type of upper limb movement, that goal-directed movements have different neural correlates than movements without a particular goal. In a reach-and-touch task, we explored the differences in the movement-related cortical potentials (MRCPs) between goal-directed and non-goal-directed movements. We evaluated if the detection of movement intention was influenced by the goal-directedness of the movement. In a single-trial classification procedure we found that classification accuracies are enhanced if there is a goal-directed movement in mind...
January 25, 2017: NeuroImage
https://www.readbyqxmd.com/read/28129143/riemannian-geometry-applied-to-detection-of-respiratory-states-from-eeg-signals-the-basis-for-a-brain-ventilator-interface
#19
Xavier Navarro-Sune, Anna L Hudson, Fabrizio De Vico Fallani, Jaques Martinerie, Adrien Witon, Pierre Pouget, Mathieu Raux, Thomas Similowski, Mario Chavez
During mechanical ventilation, patient-ventilator disharmony is frequently observed and may result in increased breathing effort, compromising the patient's comfort and recovery. This circumstance requires clinical intervention and becomes challenging when patients are sedated or verbal communication is difficult. In this work, we propose a brain computer interface (BCI) to automatically and non-invasively detect patient-ventilator disharmony from electroencephalographic (EEG) signals: a brain-ventilator interface...
July 19, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28124985/a-genetic-based-feature-selection-approach-in-the-identification-of-left-right-hand-motor-imagery-for-a-brain-computer-interface
#20
Charles Yaacoub, Georges Mhanna, Sandy Rihana
Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering...
January 23, 2017: Brain Sciences
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