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Yves Basset, Greg P A Lamarre, Tom Ratz, Simon T Segar, Thibaud Decaëns, Rodolphe Rougerie, Scott E Miller, Filonila Perez, Ricardo Bobadilla, Yacksecari Lopez, José Alejandro Ramirez, Annette Aiello, Héctor Barrios
We have little knowledge of the response of invertebrate assemblages to climate change in tropical ecosystems, and few studies have compiled long-term data on invertebrates from tropical rainforests. We provide an updated list of the 72 species of Saturniidae moths collected on Barro Colorado Island (BCI), Panama, during the period 1958-2016. This list will serve as baseline data for assessing long-term changes of saturniids on BCI in the future, as 81% of the species can be identified by their unique DNA Barcode Index Number, including four cryptic species not yet formally described...
December 2017: Ecology and Evolution
Lei Jiang, Yun Wang, Bangyu Cai, Yueming Wang, Yiwen Wang
The event-related potential (ERP) is the brain response measured in electroencephalography (EEG), which reflects the process of human cognitive activity. ERP has been introduced into brain computer interfaces (BCIs) to communicate the computer with the subject's intention. Due to the low signal-to-noise ratio of EEG, most ERP studies are based on grand-averaging over many trials. Recently single-trial ERP detection attracts more attention, which enables real time processing tasks as rapid face identification...
2017: Frontiers in Computational Neuroscience
M M Rahman, M A Chowdhury, S A Fattah
Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain-computer interface (BCI) applications. In the design of BCI systems, features extracted from lower frequency bands of scalp-recorded EEG signals are generally considered to classify mental tasks and higher frequency bands are mostly ignored as noise. However, in this paper, it is demonstrated that high frequency components of EEG signal can provide accommodating data for enhancing the classification performance of the mental task-based BCI...
December 9, 2017: Brain Informatics
Yang Yu, Zongtan Zhou, Yadong Liu, Jun Jiang, Erwei Yin, Nannan Zhang, Zhihua Wang, Yaru Liu, Xingjie Wu, Dewen Hu
This paper presents a hybrid brain-computer interface (BCI) that combines motor imagery (MI) and P300 potential for the asynchronous operation of a brain-controlled wheelchair whose design is based on a Mecanum wheel. This paradigm is completely user-centric. By sequentially performing MI tasks or paying attention to P300 flashing, the user can use eleven functions to control the wheelchair: move forward/backward, move left/right, move left45/right45, accelerate/decelerate, turn left/right, and stop. The practicality and effectiveness of the proposed approach were validated in eight subjects, all of whom achieved good performance...
December 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Antonio Fernández-Caballero, Elena Navarro, Patricia Fernández-Sotos, Pascual González, Jorge J Ricarte, José M Latorre, Roberto Rodriguez-Jimenez
This perspective paper faces the future of alternative treatments that take advantage of a social and cognitive approach with regards to pharmacological therapy of auditory verbal hallucinations (AVH) in patients with schizophrenia. AVH are the perception of voices in the absence of auditory stimulation and represents a severe mental health symptom. Virtual/augmented reality (VR/AR) and brain computer interfaces (BCI) are technologies that are growing more and more in different medical and psychological applications...
2017: Frontiers in Neuroinformatics
John E Downey, Lucas Brane, Robert A Gaunt, Elizabeth C Tyler-Kabara, Michael L Boninger, Jennifer L Collinger
Brain-computer interface (BCI) controlled prosthetic arms are being developed to restore function to people with upper-limb paralysis. This work provides an opportunity to analyze human cortical activity during complex tasks. Previously we observed that BCI control became more difficult during interactions with objects, although we did not quantify the neural origins of this phenomena. Here, we investigated how motor cortical activity changed in the presence of an object independently of the kinematics that were being generated using intracortical recordings from two people with tetraplegia...
December 5, 2017: Scientific Reports
Sebastian Nagel, Werner Dreher, Wolfgang Rosenstiel, Martin Spüler
BACKGROUND: Visual neuroscience experiments and Brain-Computer Interface (BCI) control often require strict timings in a millisecond scale. As most experiments are performed using a personal computer (PC), the latencies that are introduced by the setup should be taken into account and be corrected. As a standard computer monitor uses a rastering to update each line of the image sequentially, this causes a monitor raster latency which depends on the position, on the monitor and the refresh rate...
November 29, 2017: Journal of Neuroscience Methods
Benjamin Wittevrongel, Marc M Van Hulle
Brain-Computer Interfaces (BCIs) decode brain activity with the aim to establish a direct communication channel with an external device. Albeit they have been hailed to (re-)establish communication in persons suffering from severe motor- and/or communication disabilities, only recently BCI applications have been challenging other assistive technologies. Owing to their considerably increased performance and the advent of affordable technological solutions, BCI technology is expected to trigger a paradigm shift not only in assistive technology but also in the way we will interface with technology...
2017: Frontiers in Neuroscience
Denis Delisle-Rodriguez, Ana Cecilia Villa-Parra, Teodiano Bastos-Filho, Alberto López-Delis, Anselmo Frizera-Neto, Sridhar Krishnan, Eduardo Rocon
This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode...
November 25, 2017: Sensors
Katharina Olze, Christof Jan Wehrmann, Luyang Mu, Meinhard Schilling
In brain computer interface (BCI) applications, the use of steady-state visually evoked potentials (SSVEPs) is common. Therefore, a visual stimulation with a constant repetition frequency is necessary. However, using a computer monitor, the set of frequencies that can be used is restricted by the refresh rate of the screen. Frequencies that are not an integer divisor of the refresh rate cannot be displayed correctly. Furthermore, the programming language the stimulation software is written in and the operating system influence the actually generated and presented frequencies...
November 28, 2017: Biomedizinische Technik. Biomedical Engineering
Jaeyoung Shin, Jinuk Kwon, Jongkwan Choi, Chang-Hwan Im
This study investigated the effectiveness of using a high-density multi-distance source-detector (SD) separations in near-infrared spectroscopy (NIRS), for enhancing the performance of a functional NIRS (fNIRS)-based brain-computer interface (BCI). The NIRS system that was used for the experiment was capable of measuring signals from four SD separations: 15, 21.2, 30, and 33.5 mm, and this allowed the measurement of hemodynamic response alterations at various depths. Fifteen participants were asked to perform mental arithmetic and word chain tasks, to induce task-related hemodynamic response variations, or they were asked to stay relaxed to acquire a baseline signal...
November 29, 2017: Scientific Reports
Yue Dong, Kaan E Raif, Sarah C Determan, Yan Gai
Decoding spatial attention based on brain signals has wide applications in brain-computer interface (BCI). Previous BCI systems mostly relied on visual patterns or auditory stimulation (e.g., loudspeakers) to evoke synchronous brain signals. There would be difficulties to cover a large range of spatial locations with such a stimulation protocol. The present study explored the possibility of using virtual acoustic space and a visual-auditory matching paradigm to overcome this issue. The technique has the flexibility of generating sound stimulation from virtually any spatial location...
November 2017: Physiological Reports
Quentin Barthélemy, Louis Mayaud, Yann Renard, Daekeun Kim, Seung-Wan Kang, Jay Gunkelman, Marco Congedo
OBJECTIVE: Due to its high temporal resolution, electroencephalography (EEG) has become a broadly-used technology for real-time brain monitoring applications such as neurofeedback (NFB) and brain-computer interfaces (BCI). However, since EEG signals are prone to artifacts, denoising is a crucial step that enables adequate subsequent data processing and interpretation. The aim of this study is to compare manual denoising to unsupervised online denoising, which is essential to real-time applications...
November 20, 2017: Neurophysiologie Clinique, Clinical Neurophysiology
Maximilian Hommelsen, Matthias Schneiders, Christian Schuld, Philipp Keyl, Rüdiger Rupp
Background: Electroencephalogram (EEG)-based brain-computer interfaces (BCI) represent a promising component of restorative motor therapies in individuals with partial paralysis. However, in those patients, sensory functions such as proprioception are at least partly preserved. The aim of this study was to investigate whether afferent feedback interferes with the BCI-based detection of efferent motor commands during execution of movements. Methods: Brain activity of 13 able-bodied subjects (age: 29.1 ± 4.8 years; 11 males) was compared between a motor task (MT) consisting of an isometric, isotonic grip and a somatosensory electrical stimulation (SS) of the fingertips...
2017: Frontiers in Human Neuroscience
Alan D Degenhart, Shivayogi V Hiremath, Ying Yang, Stephen T Foldes, Jennifer L Collinger, Michael L Boninger, Elizabeth C Tyler-Kabara, Wei Wang
OBJECTIVE: Brain-computer interface (BCI) technology aims to provide individuals with paralysis a means to restore function. Electrocorticography (ECoG) uses disc electrodes placed on either the surface of the dura or the cortex to record field potential activity. ECoG has been proposed as a viable neural recording modality for BCI systems, potentially providing stable, long-term recordings of cortical activity with high spatial and temporal resolution. Previously we have demonstrated that a subject with spinal cord injury (SCI) could control an ECoG-based BCI system with up to three degrees of freedom [Wang et al...
November 21, 2017: Journal of Neural Engineering
Jane E Huggins, Christoph Guger, Mounia Ziat, Thorsten O Zander, Denise Taylor, Michael Tangermann, Aureli Soria-Frisch, John Simeral, Reinhold Scherer, Rüdiger Rupp, Giulio Ruffini, Douglas K R Robinson, Nick F Ramsey, Anton Nijholt, Gernot Müller-Putz, Dennis J McFarland, Donatella Mattia, Brent J Lance, Pieter-Jan Kindermans, Iñaki Iturrate, Christian Herff, Disha Gupta, An H Do, Jennifer L Collinger, Ricardo Chavarriaga, Steven M Chase, Martin G Bleichner, Aaron Batista, Charles W Anderson, Erik J Aarnoutse
The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public...
2017: Brain Computer Interfaces
Danut C Irimia, Woosang Cho, Rupert Ortner, Brendan Z Allison, Bogdan E Ignat, Guenter Edlinger, Christoph Guger
Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)-based brain-computer interface (BCI), functional electrical stimulation (FES), and visual feedback technologies for a complete sensorimotor closed-loop therapy system for poststroke rehabilitation. The proposed system was tested on two chronic stroke patients in a clinical environment...
November 2017: Artificial Organs
Jong Ho Hwang, Kyoung Won Nam, Dong Pyo Jang, In Young Kim
There have been few reports that investigated the effects of the degree and pattern of a spectral smearing of stimuli due to deteriorated hearing ability on the performance of auditory brain-computer interface (BCI) systems. In this study, we assumed that such spectral smearing of stimuli may affect the performance of an auditory steady-state response (ASSR)-based BCI system and performed subjective experiments using 10 normal-hearing subjects to verify this assumption. We constructed smearing-reflected stimuli using an 8-channel vocoder with moderate and severe hearing loss setups and, using these stimuli, performed subjective concentration tests with three symmetric and six asymmetric smearing patterns while recording electroencephalogram signals...
December 2017: Cognitive Neurodynamics
A Khasnobish, S Datta, R Bose, D N Tibarewala, A Konar
Tactual exploration of objects produce specific patterns in the human brain and hence objects can be recognized by analyzing brain signals during tactile exploration. The present work aims at analyzing EEG signals online for recognition of embossed texts by tactual exploration. EEG signals are acquired from the parietal region over the somatosensory cortex of blindfolded healthy subjects while they tactually explored embossed texts, including symbols, numbers, and alphabets. Classifiers based on the principle of supervised learning are trained on the extracted EEG feature space, comprising three features, namely, adaptive autoregressive parameters, Hurst exponents, and power spectral density, to recognize the respective texts...
December 2017: Cognitive Neurodynamics
Xin Zhang, Guanghua Xu, Jun Xie, Xun Zhang
Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) has advantages of high information transfer rate (ITR), less electrodes and little training. So it has been widely investigated. However, the available stimulus frequencies are limited by brain responses. Simultaneous modulation of stimulus luminance is a novel method to resolve this problem. In this study, three experiments were devised to gain a deeper understanding of the brain response to the stimulation using inter-modulation frequencies...
2017: PloS One
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