keyword
MENU ▼
Read by QxMD icon Read
search

Brain machine interface

keyword
https://www.readbyqxmd.com/read/28436837/passive-bci-in-operational-environments-insights-recent-advances-and-future-trends
#1
Pietro Arico, Gianluca Borghini, Gianluca Di Flumeri, Nicolina Sciaraffa, Alfredo Colosimo, Fabio Babiloni
OBJECTIVE: this mini-review aims to highlight recent important aspects to consider and evaluate when passive Brain-Computer Interface (pBCI) systems would be developed and used in operational environments, and remarks future directions of their applications. METHODS: Electroencephalography (EEG)-based pBCI has become an important tool for real-time analysis of brain activity, since it could potentially provide, covertly - without distracting the user from the main task - and objectively - not affected by the subjective judgement of an observer or the user itself - information about the operator cognitive state...
April 17, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28420954/multiple-kernel-based-region-importance-learning-for-neural-classification-of-gait-states-from-eeg-signals
#2
Yuhang Zhang, Saurabh Prasad, Atilla Kilicarslan, Jose L Contreras-Vidal
With the development of Brain Machine Interface (BMI) systems, people with motor disabilities are able to control external devices to help them restore movement abilities. Longitudinal validation of these systems is critical not only to assess long-term performance reliability but also to investigate adaptations in electrocortical patterns due to learning to use the BMI system. In this paper, we decode the patterns of user's intended gait states (e.g., stop, walk, turn left, and turn right) from scalp electroencephalography (EEG) signals and simultaneously learn the relative importance of different brain areas by using the multiple kernel learning (MKL) algorithm...
2017: Frontiers in Neuroscience
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/28411726/a-wellness-platform-for-stereoscopic-3d-video-systems-using-eeg-based-visual-discomfort-evaluation-technology
#4
Min-Koo Kang, Hohyun Cho, Han-Mu Park, Sung Chan Jun, Kuk-Jin Yoon
Recent advances in three-dimensional (3D) video technology have extended the range of our experience while providing various 3D applications to our everyday life. Nevertheless, the so-called visual discomfort (VD) problem inevitably degrades the quality of experience in stereoscopic 3D (S3D) displays. Meanwhile, electroencephalography (EEG) has been regarded as one of the most promising brain imaging modalities in the field of cognitive neuroscience. In an effort to facilitate comfort with S3D displays, we propose a new wellness platform using EEG...
July 2017: Applied Ergonomics
https://www.readbyqxmd.com/read/28407016/learning-from-label-proportions-in-brain-computer-interfaces-online-unsupervised-learning-with-guarantees
#5
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/28389030/harnessing-prefrontal-cognitive-signals-for-brain-machine-interfaces
#6
REVIEW
Byoung-Kyong Min, Ricardo Chavarriaga, José Del R Millán
Brain-machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements, several obstacles remain. The most commonly used BMI control signals are derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of human intentions. Therefore, additional sources of brain activity for controlling BMIs need to be explored. In particular, higher-order cognitive brain signals, specifically those encoding goal-directed intentions, are natural candidates for enlarging the repertoire of BMI control signals and making them more efficient and intuitive...
April 4, 2017: Trends in Biotechnology
https://www.readbyqxmd.com/read/28384122/deciphering-neuronal-population-codes-for-acute-thermal-pain
#7
Zhe Chen, Qiaosheng Zhang, Ai Phuong Sieu Tong, Toby R Manders, Jing Wang
OBJECTIVE: Pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage. Current pain research mostly focuses on molecular and synaptic changes at the spinal and peripheral levels. However, a complete understanding of pain mechanisms requires the physiological study of the neocortex. Our goal is to apply a neural decoding approach to read out the onset of acute thermal pain signals, which can be used for brain-machine interface...
April 6, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28375650/emerging-frontiers-of-neuroengineering-a-network-science-of-brain-connectivity
#8
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/28368083/-training-cortical-signals-by-means-of-a-bmi-eeg-system-its-evolution-and-intervention-a-case-report
#9
E Monge-Pereira, I Casatorres Perez-Higueras, P Fernandez-Gonzalez, J Ibanez-Pereda, J I Serrano, F Molina-Rueda
INTRODUCTION: In the last years, new technologies such as the brain-machine interfaces (BMI) have been incorporated in the rehabilitation process of subjects with stroke. These systems are able to detect motion intention, analyzing the cortical signals using different techniques such as the electroencephalography (EEG). This information could guide different interfaces such as robotic devices, electrical stimulation or virtual reality. CASE REPORT: A 40 years-old man with stroke with two months from the injury participated in this study...
April 16, 2017: Revista de Neurologia
https://www.readbyqxmd.com/read/28367109/low-latency-estimation-of-motor-intentions-to-assist-reaching-movements-along-multiple-sessions-in-chronic-stroke-patients-a-feasibility-study
#10
Jaime Ibáñez, Esther Monge-Pereira, Francisco Molina-Rueda, J I Serrano, Maria D Del Castillo, Alicia Cuesta-Gómez, María Carratalá-Tejada, Roberto Cano-de-la-Cuerda, Isabel M Alguacil-Diego, Juan C Miangolarra-Page, Jose L Pons
Background: The association between motor-related cortical activity and peripheral stimulation with temporal precision has been proposed as a possible intervention to facilitate cortico-muscular pathways and thereby improve motor rehabilitation after stroke. Previous studies with patients have provided evidence of the possibility to implement brain-machine interface platforms able to decode motor intentions and use this information to trigger afferent stimulation and movement assistance. This study tests the use a low-latency movement intention detector to drive functional electrical stimulation assisting upper-limb reaching movements of patients with stroke...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28361947/mapping-ecog-channel-contributions-to-trajectory-and-muscle-activity-prediction-in-human-sensorimotor-cortex
#11
Yasuhiko Nakanishi, Takufumi Yanagisawa, Duk Shin, Hiroyuki Kambara, Natsue Yoshimura, Masataka Tanaka, Ryohei Fukuma, Haruhiko Kishima, Masayuki Hirata, Yasuharu Koike
Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies have also identified distributions of "extrinsic-like" and "intrinsic-like" neurons in the premotor (PM) and primary motor (M1) cortices. Here, we investigated whether trajectories and muscle activity predicted from ECoG were obtained based on signals derived from extrinsic-like or intrinsic-like neurons. Three participants carried objects of three different masses along the same counterclockwise path on a table...
March 31, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28358916/classifying-three-imaginary-states-of-the-same-upper-extremity-using-time-domain-features
#12
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/28348527/tuning-up-the-old-brain-with-new-tricks-attention-training-via-neurofeedback
#13
REVIEW
Yang Jiang, Reza Abiri, Xiaopeng Zhao
Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children...
2017: Frontiers in Aging Neuroscience
https://www.readbyqxmd.com/read/28342747/subthalamic-nucleus-beta-and-gamma-activity-is-modulated-depending-on-the-level-of-imagined-grip-force
#14
Petra Fischer, Alek Pogosyan, Binith Cheeran, Alexander L Green, Tipu Z Aziz, Jonathan Hyam, Simon Little, Thomas Foltynie, Patricia Limousin, Ludvic Zrinzo, Marwan Hariz, Michael Samuel, Keyoumars Ashkan, Peter Brown, Huiling Tan
Motor imagery involves cortical networks similar to those activated by real movements, but the extent to which the basal ganglia are recruited is not yet clear. Gamma and beta oscillations in the subthalamic nucleus (STN) vary with the effort of sustained muscle activity. We recorded local field potentials in Parkinson's disease patients and investigated if similar changes can be observed during imagined gripping at three different 'forces'. We found that beta activity decreased significantly only for imagined grips at the two stronger force levels...
March 22, 2017: Experimental Neurology
https://www.readbyqxmd.com/read/28336339/optimal-feature-selection-from-fnirs-signals-using-genetic-algorithms-for-bci
#15
Farzan Majeed Noori, Noman Naseer, Nauman Khalid Qureshi, Hammad Nazeer, Rayyan Azam Khan
In this paper, a novel technique for determination of the optimal feature combinations and, thereby, acquisition of the maximum classification performance for a functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI), is proposed. After obtaining motor-imagery and rest signals from the motor cortex, filtering is applied to remove the physiological noises. Six features (signal slope, signal mean, signal variance, signal peak, signal kurtosis and signal skewness) are then extracted from the oxygenated hemoglobin (HbO)...
March 20, 2017: Neuroscience Letters
https://www.readbyqxmd.com/read/28325008/feasibility-of-an-ultra-low-power-digital-signal-processor-platform-as-a-basis-for-a-fully-implantable-brain-computer-interface-system
#16
Po T Wang, Keulanna Gandasetiawan, Colin M McCrimmon, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do
A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28321973/cortical-and-subcortical-mechanisms-of-brain-machine-interfaces
#17
Silvia Marchesotti, Roberto Martuzzi, Aaron Schurger, Maria Laura Blefari, José R Del Millán, Hannes Bleuler, Olaf Blanke
Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level...
March 21, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28315750/fun-cube-based-brain-gym-cognitive-function-assessment-system
#18
Tao Zhang, Chung-Chih Lin, Tsang-Chu Yu, Jing Sun, Wen-Chuin Hsu, Alice May-Kuen Wong
The aim of this study is to design and develop a fun cube (FC) based brain gym (BG) cognitive function assessment system using the wireless sensor network and multimedia technologies. The system comprised (1) interaction devices, FCs and a workstation used as interactive tools for collecting and transferring data to the server, (2) a BG information management system responsible for managing the cognitive games and storing test results, and (3) a feedback system used for conducting the analysis of cognitive functions to assist caregivers in screening high risk groups with mild cognitive impairment...
March 3, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28298888/reaching-and-grasping-a-glass-of-water-by-locked-in-als-patients-through-a-bci-controlled-humanoid-robot
#19
Rossella Spataro, Antonio Chella, Brendan Allison, Marcello Giardina, Rosario Sorbello, Salvatore Tramonte, Christoph Guger, Vincenzo La Bella
Locked-in Amyotrophic Lateral Sclerosis (ALS) patients are fully dependent on caregivers for any daily need. At this stage, basic communication and environmental control may not be possible even with commonly used augmentative and alternative communication devices. Brain Computer Interface (BCI) technology allows users to modulate brain activity for communication and control of machines and devices, without requiring a motor control. In the last several years, numerous articles have described how persons with ALS could effectively use BCIs for different goals, usually spelling...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28275048/brain-machine-interfaces-from-basic-science-to-neuroprostheses-and-neurorehabilitation
#20
REVIEW
Mikhail A Lebedev, Miguel A L Nicolelis
Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity...
April 2017: Physiological Reviews
keyword
keyword
24988
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"