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Brain-Computer interface

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https://www.readbyqxmd.com/read/28931749/ethics-in-published-brain-computer-interface-research
#1
Laura Specker Sullivan, Judy Illes
OBJECTIVE: Sophisticated signal processing has opened the doors to more research with human subjects than ever before. The increase in the use of human subjects in research comes with a need for increased human subjects protections. APPROACH: We quantified the presence or absence of ethics language in published reports of BCI studies that involved human subjects and qualitatively characterized ethics statements. MAIN RESULTS: Reports of BCI studies with human subjects that are published in neural engineering and engineering journals are anchored in the rationale of technological improvement...
September 21, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28930544/neural-signatures-of-attention-insights-from-decoding-population-activity-patterns
#2
Panagiotis Sapountzis, Georgia G Gregoriou
Understanding brain function and the computations that individual neurons and neuronal ensembles carry out during cognitive functions is one of the biggest challenges in neuroscientific research. To this end, invasive electrophysiological studies have provided important insights by recording the activity of single neurons in behaving animals. To average out noise, responses are typically averaged across repetitions and across neurons that are usually recorded on different days. However, the brain makes decisions on short time scales based on limited exposure to sensory stimulation by interpreting responses of populations of neurons on a moment to moment basis...
January 1, 2018: Frontiers in Bioscience (Landmark Edition)
https://www.readbyqxmd.com/read/28928649/changes-in-electroencephalography-complexity-using-a-brain-computer-interface-motor-observation-training-in-chronic-stroke-patients-a-fuzzy-approximate-entropy-analysis
#3
Rui Sun, Wan-Wa Wong, Jing Wang, Raymond Kai-Yu Tong
Entropy-based algorithms have been suggested as robust estimators of electroencephalography (EEG) predictability or regularity. This study aimed to examine possible disturbances in EEG complexity as a means to elucidate the pathophysiological mechanisms in chronic stroke, before and after a brain computer interface (BCI)-motor observation intervention. Eleven chronic stroke subjects and nine unimpaired subjects were recruited to examine the differences in their EEG complexity. The BCI-motor observation intervention was designed to promote functional recovery of the hand in stroke subjects...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28926593/target-directed-motor-imagery-of-the-lower-limb-enhances-event-related-desynchronization
#4
Kosuke Kitahara, Yoshikatsu Hayashi, Shiro Yano, Toshiyuki Kondo
Event-related desynchronization/synchronization (ERD/S) is an electroencephalogram (EEG) feature widely used as control signals for Brain-Computer Interfaces (BCIs). Nevertheless, the underlying neural mechanisms and functions of ERD/S are largely unknown, thus investigating them is crucial to improve the reliability of ERD/S-based BCIs. This study aimed to identify Motor Imagery (MI) conditions that enhance ERD/S. We investigated following three questions: 1) whether target-directed MI affects ERD/S, 2) whether MI with sound imagery affects ERD/S, and 3) whether ERD/S has a body part dependency of MI...
2017: PloS One
https://www.readbyqxmd.com/read/28925374/task-induced-frequency-modulation-features-for-brain-computer-interfacing
#5
Vinay Jayaram, Matthias Hohmann, Jennifer Just, Bernhard Schölkopf, Moritz Grosse-Wentrup
OBJECTIVE: Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects' intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects' intents with an accuracy comparable to task-induced amplitude modulation...
October 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28924568/convolutional-neural-network-for-high-accuracy-functional-near-infrared-spectroscopy-in-a-brain-computer-interface-three-class-classification-of-rest-right-and-left-hand-motor-execution
#6
Thanawin Trakoolwilaiwan, Bahareh Behboodi, Jaeseok Lee, Kyungsoo Kim, Ji-Woong Choi
The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI...
January 2018: Neurophotonics
https://www.readbyqxmd.com/read/28919854/pranas-a-new-platform-for-retinal-analysis-and-simulation
#7
Bruno Cessac, Pierre Kornprobst, Selim Kraria, Hassan Nasser, Daniela Pamplona, Geoffrey Portelli, Thierry Viéville
The retina encodes visual scenes by trains of action potentials that are sent to the brain via the optic nerve. In this paper, we describe a new free access user-end software allowing to better understand this coding. It is called PRANAS (https://pranas.inria.fr), standing for Platform for Retinal ANalysis And Simulation. PRANAS targets neuroscientists and modelers by providing a unique set of retina-related tools. PRANAS integrates a retina simulator allowing large scale simulations while keeping a strong biological plausibility and a toolbox for the analysis of spike train population statistics...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28914767/feasibility-of-equivalent-dipole-models-for-electroencephalogram-based-brain-computer-interfaces
#8
Paul H Schimpf
This article examines the localization errors of equivalent dipolar sources inverted from the surface electroencephalogram in order to determine the feasibility of using their location as classification parameters for non-invasive brain computer interfaces. Inverse localization errors are examined for two head models: a model represented by four concentric spheres and a realistic model based on medical imagery. It is shown that the spherical model results in localization ambiguity such that a number of dipolar sources, with different azimuths and varying orientations, provide a near match to the electroencephalogram of the best equivalent source...
September 15, 2017: Brain Sciences
https://www.readbyqxmd.com/read/28914232/combined-rtms-and-virtual-reality-brain-computer-interface-training-for-motor-recovery-after-stroke
#9
Nessa N Johnson, James Carey, Bradley Edelman, Alexander Doud, Andrew Grande, Kamakshi Lakshminarayan, Bin He
OBJECTIVE: Combining repetitive transcranial magnetic stimulation (rTMS) with brain-computer interface (BCI) training can address motor impairment after stroke by down-regulating exaggerated inhibition from the contralesional hemisphere and encouraging ipsilesional activation. The objective was to evaluate the efficacy of combined rTMS+BCI, compared to sham rTMS+BCI, on motor recovery after stroke in subjects with lasting motor paresis. APPROACH: Three stroke subjects approximately one year post-stroke participated in three weeks of combined rTMS (real or sham) and BCI, followed by three weeks of BCI alone...
September 15, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28913349/brain-computer-interface-for-clinical-purposes-cognitive-assessment-and-rehabilitation
#10
REVIEW
Laura Carelli, Federica Solca, Andrea Faini, Paolo Meriggi, Davide Sangalli, Pietro Cipresso, Giuseppe Riva, Nicola Ticozzi, Andrea Ciammola, Vincenzo Silani, Barbara Poletti
Alongside the best-known applications of brain-computer interface (BCI) technology for restoring communication abilities and controlling external devices, we present the state of the art of BCI use for cognitive assessment and training purposes. We first describe some preliminary attempts to develop verbal-motor free BCI-based tests for evaluating specific or multiple cognitive domains in patients with Amyotrophic Lateral Sclerosis, disorders of consciousness, and other neurological diseases. Then we present the more heterogeneous and advanced field of BCI-based cognitive training, which has its roots in the context of neurofeedback therapy and addresses patients with neurological developmental disorders (autism spectrum disorder and attention-deficit/hyperactivity disorder), stroke patients, and elderly subjects...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28912701/spectral-entropy-can-predict-changes-of-working-memory-performance-reduced-by-short-time-training-in-the-delayed-match-to-sample-task
#11
Yin Tian, Huiling Zhang, Wei Xu, Haiyong Zhang, Li Yang, Shuxing Zheng, Yupan Shi
Spectral entropy, which was generated by applying the Shannon entropy concept to the power distribution of the Fourier-transformed electroencephalograph (EEG), was utilized to measure the uniformity of power spectral density underlying EEG when subjects performed the working memory tasks twice, i.e., before and after training. According to Signed Residual Time (SRT) scores based on response speed and accuracy trade-off, 20 subjects were divided into two groups, namely high-performance and low-performance groups, to undertake working memory (WM) tasks...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28893295/a-brain-computer-interface-driven-by-imagining-different-force-loads-on-a-single-hand-an-online-feasibility-study
#12
Kun Wang, Zhongpeng Wang, Yi Guo, Feng He, Hongzhi Qi, Minpeng Xu, Dong Ming
BACKGROUND: Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads...
September 11, 2017: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/28891513/electrode-channel-selection-based-on-backtracking-search-optimization-in-motor-imagery-brain-computer-interfaces
#13
Shengfa Dai, Qingguo Wei
Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain-computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy. In this paper, a novel method named backtracking search optimization algorithm is proposed to automatically select the optimal channel set for common spatial pattern...
2017: Journal of Integrative Neuroscience
https://www.readbyqxmd.com/read/28887545/human-to-human-closed-loop-control-based-on-brain-to-brain-interface-and-muscle-to-muscle-interface
#14
M Ebrahim M Mashat, Guangye Li, Dingguo Zhang
Novel communication techniques have always been fascinating for humankind. This pilot study presents an approach to human interaction by combining direct brain-to-brain interface (BBI) and muscle-to-muscle interface (MMI) in a closed-loop pattern. In this system, artificial paths (data flows) functionally connect natural paths (nerves). The intention from one subject (sender) is recognized using electroencephalography (EEG) based brain-computer interface (BCI), which is sent out to trigger transcranial magnetic stimulation (TMS) on the other subject (receiver) and induce hand motion; meanwhile TMS results in a significant change on the motor evoked potentials (MEP) recorded by electromyography (EMG) of the receiver's arm, which triggers functional electrical stimulation (FES) applied to the sender's arm and generates hand motion...
September 8, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28879007/investigating-the-impact-of-feedback-update-interval-on-the-efficacy-of-restorative-brain-computer-interfaces
#15
Sam Darvishi, Michael C Ridding, Brenton Hordacre, Derek Abbott, Mathias Baumert
Restorative brain-computer interfaces (BCIs) have been proposed to enhance stroke rehabilitation. Restorative BCIs are able to close the sensorimotor loop by rewarding motor imagery (MI) with sensory feedback. Despite the promising results from early studies, reaching clinically significant outcomes in a timely fashion is yet to be achieved. This lack of efficacy may be due to suboptimal feedback provision. To the best of our knowledge, the optimal feedback update interval (FUI) during MI remains unexplored...
August 2017: Royal Society Open Science
https://www.readbyqxmd.com/read/28877175/composing-only-by-thought-novel-application-of-the-p300-brain-computer-interface
#16
Andreas Pinegger, Hannah Hiebel, Selina C Wriessnegger, Gernot R Müller-Putz
The P300 event-related potential is a well-known pattern in the electroencephalogram (EEG). This kind of brain signal is used for many different brain-computer interface (BCI) applications, e.g., spellers, environmental controllers, web browsers, or for painting. In recent times, BCI systems are mature enough to leave the laboratories to be used by the end-users, namely severely disabled people. Therefore, new challenges arise and the systems should be implemented and evaluated according to user-centered design (USD) guidelines...
2017: PloS One
https://www.readbyqxmd.com/read/28875947/improved-prediction-of-bimanual-movements-by-a-two-staged-effector-then-trajectory-decoder-with-epidural-ecog-in-nonhuman-primates
#17
Hoseok Choi, Jeyeon Lee, Jinsick Park, Seho Lee, Kyoung-Ha Ahn, In Young Kim, Kyoung-Min Lee, Dong Pyo Jang
OBJECTIVE: In arm movement BCIs (brain-computer interfaces), unimanual research has been much more extensively studied than its bimanual counterpart. However, it is well known that the bimanual brain state is different from the unimanual one. Conventional methodology used in unimanual studies does not take the brain stage into consideration, and therefore appears to be insufficient for decoding bimanual movements. In this paper, we propose the use of a two-staged (effector-then-trajectory) decoder, which combines the classification of movement conditions and uses a hand trajectory predicting algorithm for unimanual and bimanual movements, for application to in real-world BCIs...
September 6, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28874909/identification-of-anisomerous-motor-imagery-eeg-signals-based-on-complex-algorithms
#18
Rensong Liu, Zhiwen Zhang, Feng Duan, Xin Zhou, Zixuan Meng
Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28870435/short-progressive-muscle-relaxation-or-motor-coordination-training-does-not-increase-performance-in-a-brain-computer-interface-based-on-sensorimotor-rhythms-smr
#19
L Botrel, L Acqualagna, B Blankertz, A Kübler
Brain computer interfaces (BCIs) allow for controlling devices through modulation of sensorimotor rhythms (SMR), yet a profound number of users is unable to achieve sufficient accuracy. Here, we investigated if visuo-motor coordination (VMC) training or Jacobsen's progressive muscle relaxation (PMR) prior to BCI use would increase later performance compared to a control group who performed a reading task (CG). Running the study in two different BCI-labs, we achieved a joint sample size of in N=154 naïve participants...
September 1, 2017: International Journal of Psychophysiology
https://www.readbyqxmd.com/read/28863361/evaluating-brain-computer-interface-performance-using-color-in-the-p300-checkerboard-speller
#20
D B Ryan, G Townsend, N A Gates, K Colwell, E W Sellers
OBJECTIVE: Current Brain-Computer Interface (BCI) systems typically flash an array of items from grey to white (GW). The objective of this study was to evaluate BCI performance using uniquely colored stimuli. METHODS: In addition to the GW stimuli, the current study tested two types of color stimuli (grey to color [GC] and color intensification [CI]). The main hypotheses were that in a checkboard paradigm, unique color stimuli will: (1) increase BCI performance over the standard GW paradigm; (2) elicit larger event-related potentials (ERPs); and, (3) improve offline performance with an electrode selection algorithm (i...
August 8, 2017: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
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