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Brain-Computer interface

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https://www.readbyqxmd.com/read/28819547/continuous-force-decoding-from-deep-brain-local-field-potentials-for-brain-computer-interfacing
#1
Syed A Shah, Huiling Tan, Peter Brown
Current Brain Computer Interface (BCI) systems are limited by relying on neuronal spikes and decoding limited to kinematics only. For a BCI system to be practically useful, it should be able to decode brain information on a continuous basis with low latency. This study investigates if force can be decoded from local field potentials (LFP) recorded with deep brain electrodes located at the Subthalamic nucleus (STN) using data from 5 patients with Parkinson's disease, on a continuous basis with low latency. A Wiener-Cascade (WC) model based decoder was proposed using both time-domain and frequency-domain features...
2017: International IEEE/EMBS Conference on Neural Engineering: [proceedings]
https://www.readbyqxmd.com/read/28816702/as-above-so-below-towards-understanding-inverse-models-in-bci
#2
Jussi T Lindgren
In Brain-Computer Interfaces (BCI), measurements of the users brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the cortical volume and mixed in the EEG. Does the reconstruction of the source activities in the volume help in building more accurate BCIs? The answer remains inconclusive despite previous work. In this paper, we study the question by contrasting the physiology-driven source reconstruction with data-driven representations obtained by statistical machine learning...
August 17, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28813964/brain-computer-interface-combining-eye-saccade-two-electrode-eeg-signals-and-voice-cues-to-improve-the-maneuverability-of-wheelchair
#3
Ker-Jiun Wang, Lan Zhang, Bo Luan, Hsiao-Wei Tung, Quanfeng Liu, Jiacheng Wei, Mingui Sun, Zhi-Hong Mao
Brain-computer interfaces (BCIs) largely augment human capabilities by translating brain wave signals into feasible commands to operate external devices. However, many issues face the development of BCIs such as the low classification accuracy of brain signals and the tedious human-learning procedures. To solve these problems, we propose to use signals associated with eye saccades and blinks to control a BCI interface. By extracting existing physiological eye signals, the user does not need to adapt his/her brain waves to the device...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813949/monitoring-brain-potentials-to-guide-neurorehabilitation-of-tracking-impairments
#4
Boris Yazmir, Miriam Reiner
Motor impairments come in different forms. One class of motor impairments, relates to accuracy of tracking a moving object, as, for instance, when chasing in an attempt to catch it. Here we look at neural signals associated with errors in tracking, and the implications for brain-computer-interfaces that target impairment-tailored rehabilitation. As a starting point, we characterized EEG signals evoked by tracking errors during continuous natural motion, in healthy participants. Participants played a virtual 3D, ecologically valid haptic tennis game, and had to track a moving tennis ball in order to hit and send the ball towards the opponent's court...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813929/soft-brain-machine-interfaces-for-assistive-robotics-a-novel-control-approach
#5
Lucia Schiatti, Jacopo Tessadori, Giacinto Barresi, Leonardo S Mattos, Arash Ajoudani
Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813921/effect-on-the-classification-of-motor-imagery-in-eeg-after-applying-anodal-tdcs-with-a-4%C3%A3-1-ring-montage-over-the-motor-cortex
#6
I N Angulo-Sherman, M Rodriguez-Ugarte, E Ianez, M Ortiz, J M Azorin
Transcranial direct stimulation (tDCS) is a technique for modulating brain excitability that has potential to be used in motor neurorehabilitation by enhancing motor activity, such as motor imagery (MI). tDCS effects depend on different factors, like current density and the position of the stimulating electrodes. This study presents preliminary results of the evaluation of the effect of current density on MI performance by measuring right-hand/feet MI accuracy of classification from electroencephalographic (EEG) measurements after anodal tDCS is applied with a 4×1 ring montage over the right-hand or feet motor cortex...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813811/a-multichannel-near-infrared-spectroscopy-triggered-robotic-hand-rehabilitation-system-for-stroke-patients
#7
Jongseung Lee, Nobutaka Mukae, Jumpei Arata, Hiroyuki Iwata, Keiji Iramina, Koji Iihara, Makoto Hashizume
There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813231/time-frequency-cross-mutual-information-analysis-of-the-brain-functional-networks-underlying-multiclass-motor-imagery
#8
Anmin Gong, Jianping Liu, Si Chen, Yunfa Fu
To study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time-frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain-computer interface (BCI) electroencephalography data. The authors explored the brain network revealed by these MI tasks using statistical analysis and the analysis of topologic characteristics, and observed significant differences in the reaction level, reaction time, and activated target during 4-class MI tasks...
August 16, 2017: Journal of Motor Behavior
https://www.readbyqxmd.com/read/28810588/chronic-disorders-of-consciousness
#9
Qiuyou Xie, Xiaoxiao Ni, Ronghao Yu, Yuanqing Li, Ruiwang Huang
Over the last 20 years, studies have provided greater insight into disorders of consciousness (DOC), also known as altered state of consciousness. Increased brain residual functions have been identified in patients with DOC due to the successful application of novel next-generation imaging technologies. Many unconscious patients have now been confirmed to retain considerable cognitive functions. It is hoped that greater insight regarding the psychological state of patients may be achieved through the use of functional magnetic resonance imaging and brain-computer interfaces...
August 2017: Experimental and Therapeutic Medicine
https://www.readbyqxmd.com/read/28809822/assessment-and-communication-for-people-with-disorders-of-consciousness
#10
Rupert Ortner, Brendan Z Allison, Gerald Pichler, Alexander Heilinger, Nikolaus Sabathiel, Christoph Guger
In this experiment, we demonstrate a suite of hybrid Brain-Computer Interface (BCI)-based paradigms that are designed for two applications: assessing the level of consciousness of people unable to provide motor response and, in a second stage, establishing a communication channel for these people that enables them to answer questions with either 'yes' or 'no'. The suite of paradigms is designed to test basic responses in the first step and to continue to more comprehensive tasks if the first tests are successful...
August 1, 2017: Journal of Visualized Experiments: JoVE
https://www.readbyqxmd.com/read/28809705/effects-of-continuous-kinaesthetic-feedback-based-on-tendon-vibration-on-motor-imagery-bci-performance
#11
M Barsotti, D Leonardis, N Vanello, M Bergamasco, A Frisoli
BACKGROUND AND OBJECTIVES: Feedback plays a crucial role for using Brain Computer Interface (BCI) systems. This study proposes the use of vibration-evoked kinaesthetic illusions as part of a novel multisensory feedback for a Motor Imagery (MI) based BCI and investigates its contributions in terms of BCI performance and electroencephalographic (EEG) correlates. METHODS: Sixteen subjects performed two different right arm MI-BCI sessions: with the visual feedback only and with both visual and vibration-evoked kinaesthetic feedback, conveyed by the stimulation of the biceps brachi tendon...
August 14, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28809703/motion-based-rapid-serial-visual-presentation-for-gaze-independent-brain-computer-interfaces
#12
Dong-Ok Won, Han-Jeong Hwang, Dong-Min Kim, Klaus-Robert Muller, Seong-Whan Lee
Most event-related potential (ERP)-based braincomputer interface (BCI) spellers primarily use matrix layouts, and generally require moderate eye movement for successful operation. The fundamental objective of this study is to enhance the perceptibility of target characters by introducing motion stimuli to classical rapid serial visual presentation (RSVP) spellers that do not require any eye movement, thereby applying them to paralyzed patients with oculomotor dysfunctions. To test the feasibility of the proposed motion-based RSVP paradigm, we implemented three RSVP spellers: i) fixed-direction motion (FM-RSVP), ii) random-direction motion (RM-RSVP), and iii) (the conventional) non-motion stimulation (NM-RSVP), and evaluated the effect of the three different stimulation methods on spelling performance...
August 11, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28808655/multirapid-serial-visual-presentation-framework-for-eeg-based-target-detection
#13
Zhimin Lin, Ying Zeng, Hui Gao, Li Tong, Chi Zhang, Xiaojuan Wang, Qunjian Wu, Bin Yan
Target image detection based on a rapid serial visual presentation (RSVP) paradigm is a typical brain-computer interface system with various applications, such as image retrieval. In an RSVP paradigm, a P300 component is detected to determine target images. This strategy requires high-precision single-trial P300 detection methods. However, the performance of single-trial detection methods is relatively lower than that of multitrial P300 detection methods. Image retrieval based on multitrial P300 is a new research direction...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28806960/16-channel-biphasic-current-mode-programmable-charge-balanced-neural-stimulation
#14
Xiaoran Li, Shunan Zhong, James Morizio
BACKGROUND: Neural stimulation is an important method used to activate or inhibit action potentials of the neuronal anatomical targets found in the brain, central nerve and peripheral nerve. The neural stimulator system produces biphasic pulses that deliver balanced charge into tissue from single or multichannel electrodes. The timing and amplitude of these biphasic pulses are precisely controlled by the neural stimulator software or imbedded algorithms. Amplitude mismatch between the anodic current and cathodic current of the biphasic pulse will cause permanently damage for the neural tissues...
August 14, 2017: Biomedical Engineering Online
https://www.readbyqxmd.com/read/28805731/the-role-of-visual-noise-in-influencing-mental-load-and-fatigue-in-a-steady-state-motion-visual-evoked-potential-based-brain-computer-interface
#15
Jun Xie, Guanghua Xu, Ailing Luo, Min Li, Sicong Zhang, Chengcheng Han, Wenqiang Yan
As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and fatigue occurring in the concentration on the visual stimuli. Noise, as a ubiquitous random perturbation with the power of randomness, may be exploited by the human visual system to enhance higher-level brain functions...
August 14, 2017: Sensors
https://www.readbyqxmd.com/read/28804712/virtual-and-actual-humanoid-robot-control-with-four-class-motor-imagery-based-optical-brain-computer-interface
#16
Alyssa M Batula, Youngmoo E Kim, Hasan Ayaz
Motor-imagery tasks are a popular input method for controlling brain-computer interfaces (BCIs), partially due to their similarities to naturally produced motor signals. The use of functional near-infrared spectroscopy (fNIRS) in BCIs is still emerging and has shown potential as a supplement or replacement for electroencephalography. However, studies often use only two or three motor-imagery tasks, limiting the number of available commands. In this work, we present the results of the first four-class motor-imagery-based online fNIRS-BCI for robot control...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28798676/tradeoff-between-user-experience-and-bci-classification-accuracy-with-frequency-modulated-steady-state-visual-evoked-potentials
#17
Alexander M Dreyer, Christoph S Herrmann, Jochem W Rieger
Steady-state visual evoked potentials (SSVEPs) have been widely employed for the control of brain-computer interfaces (BCIs) because they are very robust, lead to high performance, and allow for a high number of commands. However, such flickering stimuli often also cause user discomfort and fatigue, especially when several light sources are used simultaneously. Different variations of SSVEP driving signals have been proposed to increase user comfort. Here, we investigate the suitability of frequency modulation of a high frequency carrier for SSVEP-BCIs...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28798675/multisubject-learning-for-mental-workload-classification-using-concurrent-eeg-fnirs-and-physiological-measures
#18
Yichuan Liu, Hasan Ayaz, Patricia A Shewokis
An accurate measure of mental workload level has diverse neuroergonomic applications ranging from brain computer interfacing to improving the efficiency of human operators. In this study, we integrated electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), and physiological measures for the classification of three workload levels in an n-back working memory task. A significantly better than chance level classification was achieved by EEG-alone, fNIRS-alone, physiological alone, and EEG+fNIRS based approaches...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28798411/group-augmentation-in-realistic-visual-search-decisions-via-a-hybrid-brain-computer-interface
#19
Davide Valeriani, Caterina Cinel, Riccardo Poli
Groups have increased sensing and cognition capabilities that typically allow them to make better decisions. However, factors such as communication biases and time constraints can lead to less-than-optimal group decisions. In this study, we use a hybrid Brain-Computer Interface (hBCI) to improve the performance of groups undertaking a realistic visual-search task. Our hBCI extracts neural information from EEG signals and combines it with response times to build an estimate of the decision confidence. This is used to weigh individual responses, resulting in improved group decisions...
August 10, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28790910/hybrid-brain-computer-interface-techniques-for-improved-classification-accuracy-and-increased-number-of-commands-a-review
#20
REVIEW
Keum-Shik Hong, Muhammad Jawad Khan
In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker...
2017: Frontiers in Neurorobotics
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