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https://www.readbyqxmd.com/read/28810588/chronic-disorders-of-consciousness
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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/28783638/cmos-ultralow-power-brain-signal-acquisition-front-ends-design-and-human-testing
#13
Alireza Karimi-Bidhendi, Omid Malekzadeh-Arasteh, Mao-Cheng Lee, Colin M McCrimmon, Po T Wang, Akshay Mahajan, Charles Yu Liu, Zoran Nenadic, An H Do, Payam Heydari
Two brain signal acquisition (BSA) front-ends incorporating two CMOS ultralow power, low-noise amplifier arrays and serializers operating in mosfet weak inversion region are presented. To boost the amplifier's gain for a given current budget, cross-coupled-pair active load topology is used in the first stages of these two amplifiers. These two BSA front-ends are fabricated in 130 and 180 nm CMOS processes, occupying 5.45 mm (2) and 0.352 mm (2) of die areas, respectively (excluding pad rings). The CMOS 130-nm amplifier array is comprised of 64 elements, where each amplifier element consumes 0...
August 1, 2017: IEEE Transactions on Biomedical Circuits and Systems
https://www.readbyqxmd.com/read/28777722/toward-an-open-ended-bci-a-user-centered-coadaptive-design
#14
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
#15
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
#16
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/28762027/a-simple-and-efficient-algorithm-operating-with-linear-time-for-mceeg-data-compression
#17
Geevarghese Titus, M S Sudhakar
Popularisation of electroencephalograph (EEG) signals in diversified fields have increased the need for devices capable of operating at lower power and storage requirements. This has led to a great deal of research in data compression, that can address (a) low latency in the coding of the signal, (b) reduced hardware and software dependencies, (c) quantify the system anomalies, and (d) effectively reconstruct the compressed signal. This paper proposes a computationally simple and novel coding scheme named spatial pseudo codec (SPC), to achieve lossy to near lossless compression of multichannel EEG (MCEEG)...
July 31, 2017: Australasian Physical & Engineering Sciences in Medicine
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
#18
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
#19
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
https://www.readbyqxmd.com/read/28756027/key-considerations-in-designing-a-speech-brain-computer-interface
#20
Florent Bocquelet, Thomas Hueber, Laurent Girin, Stéphan Chabardès, Blaise Yvert
Restoring communication in case of aphasia is a key challenge for neurotechnologies. To this end, brain-computer strategies can be envisioned to allow artificial speech synthesis from the continuous decoding of neural signals underlying speech imagination. Such speech brain-computer interfaces do not exist yet and their design should consider three key choices that need to be made: the choice of appropriate brain regions to record neural activity from, the choice of an appropriate recording technique, and the choice of a neural decoding scheme in association with an appropriate speech synthesis method...
August 7, 2017: Journal of Physiology, Paris
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