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brain-machine interface

Zhichuan Tang, Shouqian Sun, Sanyuan Zhang, Yumiao Chen, Chao Li, Shi Chen
To recognize the user's motion intention, brain-machine interfaces (BMI) usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG) can be useful as control signals for an upper-limb exoskeleton developed by us. A BMI based on event-related desynchronization/synchronization (ERD/ERS) is proposed. In the decoder-training phase, we investigate the offline classification performance of left versus right hand and left hand versus both feet by using motor execution (ME) or motor imagery (MI)...
December 2, 2016: Sensors
Sergei L Shishkin, Yuri O Nuzhdin, Evgeny P Svirin, Alexander G Trofimov, Anastasia A Fedorova, Bogdan L Kozyrskiy, Boris M Velichkovsky
We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions...
2016: Frontiers in Neuroscience
Robert D Flint, Joshua M Rosenow, Matthew C Tate, Marc W Slutzky
OBJECTIVE: Restoring or replacing function in paralyzed individuals will one day be achieved through the use of brain-machine interfaces. Regaining hand function is a major goal for paralyzed patients. Two competing prerequisites for the widespread adoption of any hand neuroprosthesis are accurate control over the fine details of movement, and minimized invasiveness. Here, we explore the interplay between these two goals by comparing our ability to decode hand movements with subdural and epidural field potentials (EFPs)...
November 30, 2016: Journal of Neural Engineering
Chaohua Wu, Ke Lin, Wei Wu, Xiaorong Gao
Recent years have witnessed brain-computer interface (BCI) as a promising technology for integrating human intelligence and machine intelligence. Currently, event-related potential (ERP)-based BCI is an important branch of noninvasive electroencephalogram (EEG)-based BCIs. Extracting ERPs from a limited number of trials remains challenging due to their low signal-to-noise ratio (SNR) and low spatial resolution caused by volume conduction. In this paper, we propose a probabilistic model for trial-by-trial concatenated EEG, in which the concatenated ERPs are expressed as a linear combination of a set of discrete sine and cosine bases...
November 17, 2016: IEEE Transactions on Neural Networks and Learning Systems
Jingyi Liu, Muhammad Abd-El-Barr, John H Chi
No abstract text is available yet for this article.
December 2016: Neurosurgery
Huiling Tan, Alek Pogosyan, Keyoumars Ashkan, Alexander L Green, Tipu Aziz, Thomas Foltynie, Patricia Limousin, Ludvic Zrinzo, Marwan Hariz, Peter Brown
The basal ganglia are known to be involved in the planning, execution and control of gripping force and movement vigour. Here we aim to define the nature of the basal ganglia control signal for force and to decode gripping force based on local field potential (LFP) activities recorded from the subthalamic nucleus (STN) in patients with deep brain stimulation (DBS) electrodes. We found that STN LFP activities in the gamma (55-90 Hz) and beta (13-30 Hz) bands were most informative about gripping force, and that a first order dynamic linear model with these STN LFP features as inputs can be used to decode the temporal profile of gripping force...
November 18, 2016: ELife
Pietro Aricò, Gianluca Borghini, Gianluca Di Flumeri, Alfredo Colosimo, Stefano Bonelli, Alessia Golfetti, Simone Pozzi, Jean-Paul Imbert, Géraud Granger, Raïlane Benhacene, Fabio Babiloni
Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e...
2016: Frontiers in Human Neuroscience
He Cui
While remarkable progress has been made in brain-machine interfaces (BMIs) over the past two decades, it is still difficult to utilize neural signals to drive artificial actuators to produce predictive movements in response to dynamic stimuli. In contrast to naturalistic limb movements largely based on forward planning, brain-controlled neuroprosthetics mainly rely on feedback without prior trajectory formation. As an important sensorimotor interface integrating multisensory inputs and efference copy, the posterior parietal cortex (PPC) might play a proactive role in predictive motor control...
2016: Frontiers in Integrative Neuroscience
Byoung-Kyong Min, Sven Dähne, Min-Hee Ahn, Yung-Kyun Noh, Klaus-Robert Müller
We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter...
November 3, 2016: Scientific Reports
Takufumi Yanagisawa, Ryohei Fukuma, Ben Seymour, Koichi Hosomi, Haruhiko Kishima, Takeshi Shimizu, Hiroshi Yokoi, Masayuki Hirata, Toshiki Yoshimine, Yukiyasu Kamitani, Youichi Saitoh
The cause of pain in a phantom limb after partial or complete deafferentation is an important problem. A popular but increasingly controversial theory is that it results from maladaptive reorganization of the sensorimotor cortex, suggesting that experimental induction of further reorganization should affect the pain, especially if it results in functional restoration. Here we use a brain-machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand...
October 27, 2016: Nature Communications
Camilo Libedinsky, Rosa So, Zhiming Xu, Toe K Kyar, Duncun Ho, Clement Lim, Louiza Chan, Yuanwei Chua, Lei Yao, Jia Hao Cheong, Jung Hyup Lee, Kulkarni Vinayak Vishal, Yongxin Guo, Zhi Ning Chen, Lay K Lim, Peng Li, Lei Liu, Xiaodan Zou, Kai K Ang, Yuan Gao, Wai Hoe Ng, Boon Siew Han, Keefe Chng, Cuntai Guan, Minkyu Je, Shih-Cheng Yen
Individuals with tetraplegia lack independent mobility, making them highly dependent on others to move from one place to another. Here, we describe how two macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex, and decoded to achieve brain control of the platform...
2016: PloS One
Alireza Gharabaghi
Brain-machine interfaces (BMI) may support motor impaired patients during activities of daily living by controlling external devices such as prostheses (assistive BMI). Moreover, BMIs are applied in conjunction with robotic orthoses for rehabilitation of lost motor function via neurofeedback training (restorative BMI). Using assistive BMI in a rehabilitation context does not automatically turn them into restorative devices. This perspective article suggests key features of restorative BMI and provides the supporting evidence: In summary, BMI may be referred to as restorative tools when demonstrating subsequently (i) operant learning and progressive evolution of specific brain states/dynamics, (ii) correlated modulations of functional networks related to the therapeutic goal, (iii) subsequent improvement in a specific task, and (iv) an explicit correlation between the modulated brain dynamics and the achieved behavioral gains...
2016: Frontiers in Neuroscience
Rakesh Khilwani, Peter J Gilgunn, Takashi D Y Kozai, Xiao Chuan Ong, Emrullah Korkmaz, Pallavi K Gunalan, X Tracy Cui, Gary K Fedder, O Burak Ozdoganlar
Stable chronic functionality of intracortical probes is of utmost importance toward realizing clinical application of brain-machine interfaces. Sustained immune response from the brain tissue to the neural probes is one of the major challenges that hinder stable chronic functionality. There is a growing body of evidence in the literature that highly compliant neural probes with sub-cellular dimensions may significantly reduce the foreign-body response, thereby enhancing long term stability of intracortical recordings...
December 2016: Biomedical Microdevices
Maria Dadarlat, Philip Sabes
Naturalistic control of brain-machine interfaces will require artificial proprioception, potentially delivered via intracortical microstimulation (ICMS).We have previously shown that multi-channel ICMS can guide a monkey reaching to unseen targets in a planar workspace. Here, we expand on that work, asking how ICMS is decoded into target angle and distance by analyzing the performance of a monkey when ICMS feedback was degraded. From the resulting pattern of errors, we found that the animal's estimate of target direction was consistent with a weighted circular-mean strategy-close to the optimal decoding strategy given the ICMS encoding...
October 11, 2016: IEEE Transactions on Haptics
Alexey Petrushin, Lorenzo Ferrara, Axel Blau
OBJECTIVE: In light of recent progress in mapping neural function to behavior, we briefly and selectively review past and present endeavors to reveal and reconstruct nervous system function in Caenorhabditis elegans through simulation. APPROACH: Rather than presenting an all-encompassing review on the mathematical modeling of C. elegans, this contribution collects snapshots of pathfinding key works and emerging technologies that recent single- and multi-center simulation initiatives are building on...
December 2016: Journal of Neural Engineering
Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, Keum-Shik Hong
We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks...
2016: Computational Intelligence and Neuroscience
Shahab Shahdoost, Randolph Nudo, Pedram Mohseni
Brain-machine-body interfaces (BMBIs) aim to create an artificial connection in the nervous system by converting neural activity recorded from one cortical region to electrical stimuli delivered to another cortical region, spinal cord, or muscles in real-time. In particular, conditioning-mode BMBIs utilize such activity-dependent stimulation strategies to induce functional re-organization in the nervous system and promote functional recovery after injury by exploiting mechanisms underlying neuroplasticity. This paper reports on reconfigurable, field-programmable gate array (FPGA)-based implementation of a translation algorithm to extract multichannel stimulus trigger signals from intracortical neural spike activity...
October 5, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Stefano Vassanelli, Mufti Mahmud
Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function...
2016: Frontiers in Neuroscience
Nicholas R Waytowich, Vernon J Lawhern, Addison W Bohannon, Kenneth R Ball, Brent J Lance
Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects...
2016: Frontiers in Neuroscience
Romain Grandchamp, Arnaud Delorme
Recent theoretical and technological advances in neuroimaging techniques now allow brain electrical activity to be recorded using affordable and user-friendly equipment for nonscientist end-users. An increasing number of educators and artists have begun using electroencephalogram (EEG) to control multimedia and live artistic contents. In this paper, we introduce a new concept based on brain computer interface (BCI) technologies: the Brainarium. The Brainarium is a new pedagogical and artistic tool, which can deliver and illustrate scientific knowledge, as well as a new framework for scientific exploration...
2016: Computational Intelligence and Neuroscience
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