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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/28813929/soft-brain-machine-interfaces-for-assistive-robotics-a-novel-control-approach
#4
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/28813231/time-frequency-cross-mutual-information-analysis-of-the-brain-functional-networks-underlying-multiclass-motor-imagery
#5
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/28809822/assessment-and-communication-for-people-with-disorders-of-consciousness
#6
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
#7
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
#8
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/28806556/persistent-organic-pollutants-in-fat-of-three-species-of-pacific-pelagic-longline-caught-sea-turtles-accumulation-in-relation-to-ingested-plastic-marine-debris
#9
Katharine E Clukey, Christopher A Lepczyk, George H Balazs, Thierry M Work, Qing X Li, Melannie J Bachman, Jennifer M Lynch
In addition to eating contaminated prey, sea turtles may be exposed to persistent organic pollutants (POPs) from ingesting plastic debris that has absorbed these chemicals. Given the limited knowledge about POPs in pelagic sea turtles and how plastic ingestion influences POP exposure, our objectives were to: 1) provide baseline contaminant levels of three species of pelagic Pacific sea turtles; and 2) assess trends of contaminant levels in relation to species, sex, length, body condition and capture location...
August 11, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/28806144/particle-size-distributions-of-lead-measured-in-battery-manufacturing-and-secondary-smelter-facilities-and-implications-in-setting-workplace-lead-exposure-limits
#10
Catherine Petito Boyce, Sonja N Sax, Joel M Cohen
Inhalation plays an important role in exposures to lead in airborne particulate matter in occupational settings, and particle size determines where and how much of airborne lead is deposited in the respiratory tract and how much is subsequently absorbed into the body. Although some occupational airborne lead particle size data have been published, limited information is available reflecting current workplace conditions in the U.S. To address this data gap, the Battery Council International (BCI) conducted workplace monitoring studies at nine lead acid battery manufacturing facilities (BMFs) and five secondary smelter facilities (SSFs) across the U...
August 2017: Journal of Occupational and Environmental Hygiene
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
#11
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
#12
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
#13
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/28798411/group-augmentation-in-realistic-visual-search-decisions-via-a-hybrid-brain-computer-interface
#14
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
#15
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
https://www.readbyqxmd.com/read/28777722/toward-an-open-ended-bci-a-user-centered-coadaptive-design
#16
Kiret Dhindsa, Dean Carcone, Suzanna Becker
Brain-computer interfaces (BCIs) allow users to control a device by interpreting their brain activity. For simplicity, these devices are designed to be operated by purposefully modulating specific predetermined neurophysiological signals, such as the sensorimotor rhythm. However, the ability to modulate a given neurophysiological signal is highly variable across individuals, contributing to the inconsistent performance of BCIs for different users. These differences suggest that individuals who experience poor BCI performance with one class of brain signals might have good results with another...
August 4, 2017: Neural Computation
https://www.readbyqxmd.com/read/28769781/enhancing-classification-performance-of-functional-near-infrared-spectroscopy-brain-computer-interface-using-adaptive-estimation-of-general-linear-model-coefficients
#17
Nauman Khalid Qureshi, Noman Naseer, Farzan Majeed Noori, Hammad Nazeer, Rayyan Azam Khan, Sajid Saleem
In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28769776/affective-aspects-of-perceived-loss-of-control-and-potential-implications-for-brain-computer-interfaces
#18
Sebastian Grissmann, Thorsten O Zander, Josef Faller, Jonas Brönstrup, Augustin Kelava, Klaus Gramann, Peter Gerjets
Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28760486/a-novel-brain-computer-interface-for-classification-of-social-joint-attention-in-autism-and-comparison-of-3-experimental-setups-a-feasibility-study
#19
Carlos P Amaral, Marco A Simões, Susana Mouga, João Andrade, Miguel Castelo-Branco
BACKGROUND: We present a novel virtual-reality P300-based Brain Computer Interface (BCI) paradigm using social cues to direct the focus of attention. We combined interactive immersive virtual-reality (VR) technology with the properties of P300 signals in a training tool which can be used in social attention disorders such as autism spectrum disorder (ASD). NEW METHOD: We tested the novel social attention training paradigm (P300-based BCI paradigm for rehabilitation of joint-attention skills) in 13 healthy participants, in 3 EEG systems...
July 29, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/28758809/development-and-testing-an-online-near-infrared-spectroscopy-brain-computer-interface-tailored-to-an-individual-with-severe-congenital-motor-impairments
#20
Larissa C Schudlo, Tom Chau
PURPOSE: For non-verbal individuals, brain-computer interfaces (BCIs) are a potential means of communication. Near-infrared spectroscopy (NIRS) is a brain-monitoring modality that has been considered for BCIs. To date, limited NIRS-BCI testing has involved online classification, particularly with individuals with severe motor impairments. MATERIALS AND METHODS: We tested an online NIRS-BCI developed for a non-verbal individual with severe congenital motor impairments...
July 31, 2017: Disability and Rehabilitation. Assistive Technology
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