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https://www.readbyqxmd.com/read/29148137/brain-computer-interfaces-with-multi-sensory-feedback-for-stroke-rehabilitation-a-case-study
#1
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
https://www.readbyqxmd.com/read/29147144/effects-of-spectral-smearing-of-stimuli-on-the-performance-of-auditory-steady-state-response-based-brain-computer-interface
#2
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
https://www.readbyqxmd.com/read/29142267/in-vivo-characterization-of-the-electrophysiological-and-astrocytic-responses-to-a-silicon-neuroprobe-implanted-in-the-mouse-neocortex
#3
Katrien Mols, Silke Musa, Bart Nuttin, Liesbet Lagae, Vincent Bonin
Silicon neuroprobes hold great potential for studies of large-scale neural activity and brain computer interfaces, but data on brain response in chronic implants is limited. Here we explored with in vivo cellular imaging the response to multisite silicon probes for neural recordings. We tested a chronic implant for mice consisting of a CMOS-compatible silicon probe rigidly implanted in the cortex under a cranial imaging window. Multiunit recordings of cortical neurons with the implant showed no degradation of electrophysiological signals weeks after implantation (mean spike and noise amplitudes of 186 ± 42 µVpp and 16 ± 3...
November 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29141030/brain-response-to-luminance-based-and-motion-based-stimulation-using-inter-modulation-frequencies
#4
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
https://www.readbyqxmd.com/read/29134143/eeg-sensorimotor-rhythms-variation-and-functional-connectivity-measures-during-motor-imagery-linear-relations-and-classification-approaches
#5
Carlos A Stefano Filho, Romis Attux, Gabriela Castellano
Hands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands' power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI), in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that information may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node...
2017: PeerJ
https://www.readbyqxmd.com/read/29130453/a-binary-motor-imagery-tasks-based-brain-computer-interface-for-two-dimensional-movement-control
#6
Bin Xia, Lei Cao, Oladazimi Maysam, Jie Li, Hong Xie, Caixia Su, Niels Birbaumer
OBJECTIVE: Two-dimensional movement control is a popular issue in brain-computer interface (BCI) research and has many applications in the real world. In this paper, we introduce a combined control strategy to a binary class-based BCI system that allows the user to move a cursor in a two-dimensional (2D) plane. Users focus on a single moving vector to control 2D movement instead of controlling vertical and horizontal movement separately. APPROACH: Five participants took part in a fixed-target experiment and random-target experiment to verify the effectiveness of the combination control strategy under the fixed and random routine conditions...
November 13, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29130452/augmenting-intracortical-brain-machine-interface-with-neurally-driven-error-detectors
#7
Nir Even-Chen, Sergey D Stavisky, Jonathan C Kao, Stephen I Ryu, Krishna V Shenoy
OBJECTIVE: Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs...
November 13, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29129011/embodiment-and-estrangement-results-from-first-in-human-intelligent-bci-trial
#8
F Gilbert, M Cook, T O'Brien, J Illes
While new generations of implantable brain computer interface (BCI) devices are being developed, evidence in the literature about their impact on the patient experience is lagging. In this article, we address this knowledge gap by analysing data from the first-in-human clinical trial to study patients with implanted BCI advisory devices. We explored perceptions of self-change across six patients who volunteered to be implanted with artificially intelligent BCI devices. We used qualitative methodological tools grounded in phenomenology to conduct in-depth, semi-structured interviews...
November 11, 2017: Science and Engineering Ethics
https://www.readbyqxmd.com/read/29127346/microelectrode-implantation-in-motor-cortex-causes-fine-motor-deficit-implications-on-potential-considerations-to-brain-computer-interfacing-and-human-augmentation
#9
Monika Goss-Varley, Keith R Dona, Justin A McMahon, Andrew J Shoffstall, Evon S Ereifej, Sydney C Lindner, Jeffrey R Capadona
Intracortical microelectrodes have shown great success in enabling locked-in patients to interact with computers, robotic limbs, and their own electrically driven limbs. The recent advances have inspired world-wide enthusiasm resulting in billions of dollars invested in federal and industrial sponsorships to understanding the brain for rehabilitative applications. Additionally, private philanthropists have also demonstrated excitement in the field by investing in the use of brain interfacing technologies as a means to human augmentation...
November 10, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29125134/implicit-relevance-feedback-from-electroencephalography-and-eye-tracking-in-image-search
#10
Jan Eike Golenia, Markus Andreas Wenzel, Mihail Bogojeski, Benjamin Blankertz
OBJECTIVE: Methods from brain-computer interfacing (BCI) open a direct access to the mental processes of computer users, which offers particular benefits in comparison to standard methods for inferring user-related information. The signals can be recorded unobtrusively in the background, which circumvents the time-consuming and distracting need for the users to give explicit feedback to questions concerning the individual interest. The obtained implicit information makes it possible to create dynamic user interest profiles in real-time, that can be taken into account by novel types of adaptive, personalised software...
November 10, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29121942/ethical-aspects-of-brain-computer-interfaces-a-scoping-review
#11
Sasha Burwell, Matthew Sample, Eric Racine
BACKGROUND: Brain-Computer Interface (BCI) is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes (e.g., gaming) as well as health-related purposes (e...
November 9, 2017: BMC Medical Ethics
https://www.readbyqxmd.com/read/29118386/code-modulated-visual-evoked-potentials-using-fast-stimulus-presentation-and-spatiotemporal-beamformer-decoding
#12
Benjamin Wittevrongel, Elia Van Wolputte, Marc M Van Hulle
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase lags of which can be tied to these targets. The cVEP paradigm has enjoyed interest in the brain-computer interfacing (BCI) community for the reported high information transfer rates (ITR, in bits/min). In this study, we introduce a novel decoding algorithm based on spatiotemporal beamforming, and show that this algorithm is able to accurately identify the gazed target...
November 8, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29117100/feature-selection-for-motor-imagery-eeg-classification-based-on-firefly-algorithm-and-learning-automata
#13
Aiming Liu, Kun Chen, Quan Liu, Qingsong Ai, Yi Xie, Anqi Chen
Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy...
November 8, 2017: Sensors
https://www.readbyqxmd.com/read/29115915/neuromodulation-in-multiple-sclerosis
#14
Hesham Abboud, Eddie Hill, Junaid Siddiqui, Alessandro Serra, Benjamin Walter
Neuromodulation, or the utilization of advanced technology for targeted electrical or chemical neuronal stimulation or inhibition, has been expanding in several neurological subspecialties. In the past decades, immune-modulating therapy has been the main focus of multiple sclerosis (MS) research with little attention to neuromodulation. However, with the recent advances in disease-modifying therapies, it is time to shift the focus of MS research to neuromodulation and restoration of function as with other neurological subspecialties...
November 2017: Multiple Sclerosis: Clinical and Laboratory Research
https://www.readbyqxmd.com/read/29115280/real-time-cerebellar-neuroprosthetic-system-based-on-a-spiking-neural-network-model-of-motor-learning
#15
Tao Xu, Na Xiao, Xiaolong Zhai, Pak Kwan Chan, Chung Tin
Damage to the brain, as a result of various medical conditions, impacts everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach: The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network...
November 8, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29104877/the-sixth-international-brain-computer-interface-meeting-advances-in-basic-and-clinical-research
#16
Jane E Huggins, Gernot Müller-Putz, Jonathan R Wolpaw
No abstract text is available yet for this article.
2017: Brain Computer Interfaces
https://www.readbyqxmd.com/read/29100117/csp-tsm-optimizing-the-performance-of-riemannian-tangent-space-mapping-using-common-spatial-pattern-for-mi-bci
#17
Shiu Kumar, Kabir Mamun, Alok Sharma
BACKGROUND: Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. METHOD: We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices...
October 24, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29099388/a-review-of-rapid-serial-visual-presentation-based-brain-computer-interfaces
#18
Stephanie Lees, Natalie Dayan, Hubert Cecotti, Paul McCullagh, Liam Maguire, Fabien Lotte, Damien H Coyle
Rapid serial visual presentation (RSVP) combined with the detection of event related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs), measured non-invasively with electroencephalography (EEG), can be associated with infrequent target stimuli(images) in groups of images, potentially providing an interface for human-machine symbiosis, where humans can interact and interface with a computer without moving and which may offer faster image sorting than scenarios where humans are expected to physically react when a target image is detected...
November 3, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29096552/exploring-differences-between-left-and-right-hand-motor-imagery-via-spatio-temporal-eeg-microstate
#19
Weifeng Liu, Xiaoming Liu, Ruomeng Dai, Xiaoying Tang
EEG-based motor imagery is very useful in brain-computer interface. How to identify the imaging movement is still being researched. Electroencephalography (EEG) microstates reflect the spatial configuration of quasi-stable electrical potential topographies. Different microstates represent different brain functions. In this paper, microstate method was used to process the EEG-based motor imagery to obtain microstate. The single-trial EEG microstate sequences differences between two motor imagery tasks - imagination of left and right hand movement were investigated...
November 3, 2017: Computer Assisted Surgery (Abingdon, England)
https://www.readbyqxmd.com/read/29093673/multi-modal-integration-of-eeg-fnirs-for-brain-computer-interfaces-current-limitations-and-future-directions
#20
REVIEW
Sangtae Ahn, Sung C Jun
Multi-modal integration, which combines multiple neurophysiological signals, is gaining more attention for its potential to supplement single modality's drawbacks and yield reliable results by extracting complementary features. In particular, integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is cost-effective and portable, and therefore is a fascinating approach to brain-computer interface (BCI). However, outcomes from the integration of these two modalities have yielded only modest improvement in BCI performance because of the lack of approaches to integrate the two different features...
2017: Frontiers in Human Neuroscience
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