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Geon Hwee Kim, Kanghyun Kim, Eunji Lee, Taechang An, WooSeok Choi, Geunbae Lim, Jung Hwal Shin
Brain‒machine interface (BMI) is a promising technology that looks set to contribute to the development of artificial limbs and new input devices by integrating various recent technological advances, including neural electrodes, wireless communication, signal analysis, and robot control. Neural electrodes are a key technological component of BMI, as they can record the rapid and numerous signals emitted by neurons. To receive stable, consistent, and accurate signals, electrodes are designed in accordance with various templates using diverse materials...
October 16, 2018: Materials
Graham Staunton, Kathrin Cohen Kadosh
Real-time functional magnetic resonance imaging (fMRI)-based neurofeedback represents the latest applied behavioural neuroscience methodology developed to train participants in the self-regulation of brain regions or networks. However, as with previous biofeedback approaches which rely on electroencephalography (EEG) or related approaches such as brain-machine interface technology (BCI), individual success rates vary significantly, and some participants never learn to control their brain responses at all. Given that these approaches are often being developed for eventual use in a clinical setting (albeit there is also significant interest in using NF for neuro-enhancement in typical populations), this represents a significant hurdle which requires more research...
October 10, 2018: NeuroImage
Eduardo López-Larraz, Thiago C Figueiredo, Ainhoa Insausti-Delgado, Ulf Ziemann, Niels Birbaumer, Ander Ramos-Murguialday
The electroencephalogram (EEG) constitutes a relevant tool to study neural dynamics and to develop brain-machine interfaces (BMI) for rehabilitation of patients with paralysis due to stroke. However, the EEG is easily contaminated by artifacts of physiological origin, which can pollute the measured cortical activity and bias the interpretations of such data. This is especially relevant when recording EEG of stroke patients while they try to move their paretic limbs, since they generate more artifacts due to compensatory activity...
October 4, 2018: NeuroImage: Clinical
Aref Trigui, Sami Hached, Ahmed Ammari, Yvon Savaria, Mohamad Sawan
Due to the constantly growing geriatric population and the projected increase of the prevalence of chronic diseases that are refractory to drugs, Implantable Medical Devices (IMDs) such as neurostimulators, endoscopic capsules, artificial retinal prostheses, and brain-machine interfaces are being developed. According to many business forecast firms, the IMD market is expected to grow and they are subject to much research aiming to overcome the numerous challenges of their development. One of these challenges consists of designing a wireless power and data transmission system that has high power efficiency, highdata rates, low power consumption, and high-robustness against noise...
October 4, 2018: IEEE Reviews in Biomedical Engineering
Emily M Mugler, Matthew C Tate, Karen Livescu, Jessica W Templer, Matthew A Goldrick, Marc W Slutzky
Speech is a critical form of human communication and is central to our daily lives. Yet, despite decades of study, an understanding of the fundamental neural control of speech production remains incomplete. Current theories model speech production as a hierarchy from sentences and phrases down to words, syllables, speech sounds (phonemes) and the actions of vocal tract articulators used to produce speech sounds (articulatory gestures). Here, we investigate the cortical representation of articulatory gestures and phonemes in ventral precentral and inferior frontal gyri in men and women...
September 26, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Leon Li, Serban Negoita
Advances in electrophysiological methods such as electrocorticography (ECoG) have enabled researchers to decode phonemes, syllables, and words from brain activity. The ultimate aspiration underlying these efforts is the development of a brain-machine interface (BMI) that will enable speakers to produce real-time, naturalistic speech. In the effort to create such a device, researchers have typically followed a bottom-up approach whereby low-level units of language (e.g., phonemes, syllables, or letters) are decoded from articulation areas (e...
September 26, 2018: Journal of Neural Engineering
Yao Zhao, John P Hessburg, Jaganth Nivas Asok Kumar, Joseph T Francis
Neural activity in the primary motor cortex (M1) is known to correlate with movement related variables including kinematics and dynamics. Our recent work, which we believe is part of a paradigm shift in sensorimotor research, has shown that in addition to these movement related variables, activity in M1 and the primary somatosensory cortex (S1) are also modulated by context, such as value, during both active movement and movement observation. Here we expand on the investigation of reward modulation in M1, showing that reward level changes the neural tuning function of M1 units to both kinematic as well as dynamic related variables...
2018: Frontiers in Neuroscience
Akshay Arora, Jui-Jui Lin, Alec Gasperian, Joel Stein, Joseph Maldjian, Michael J Kahana, Bradley Lega
We sought to test the performance of three strategies for binary classification (logistic regression, support vector machines, and deep learning) for the problem of predicting successful episodic memory encoding using direct brain recordings obtained from human stereo EEG subjects. We also sought to test the impact of applying t-distributed stochastic neighbor embedding (tSNE) for unsupervised dimensionality reduction, as well as testing the effect of reducing input features to a core set of memory relevant brain areas...
September 13, 2018: Journal of Neural Engineering
Soroush Niketeghad, Nader Pouratian
Loss of vision alters the day to day life of blind individuals and may impose a significant burden on their family and the economy. Cortical visual prosthetics have been shown to have the potential of restoring a useful degree of vision via stimulation of primary visual cortex. Due to current advances in electrode design and wireless power and data transmission, development of these prosthetics has gained momentum in the past few years and multiple sites around the world are currently developing and testing their designs...
September 7, 2018: Neurotherapeutics: the Journal of the American Society for Experimental NeuroTherapeutics
Monika Goss-Varley, Andrew J Shoffstall, Keith R Dona, Justin A McMahon, Sydney C Lindner, Evon S Ereifej, Jeffrey R Capadona
Medical devices implanted in the brain hold tremendous potential. As part of a Brain Machine Interface (BMI) system, intracortical microelectrodes demonstrate the ability to record action potentials from individual or small groups of neurons. Such recorded signals have successfully been used to allow patients to interface with or control computers, robotic limbs, and their own limbs. However, previous animal studies have shown that a microelectrode implantation in the brain not only damages the surrounding tissue but can also result in functional deficits...
August 18, 2018: Journal of Visualized Experiments: JoVE
Robert Guggenberger, Dominic Kraus, Georgios Naros, Maria Teresa Leão, Ulf Ziemann, Alireza Gharabaghi
BACKGROUND: Pairing cortical and peripheral input during motor imagery (MI)-related sensorimotor desynchronization (ERD) modulates corticospinal excitability at the cortical representation (hotspot) of the imagined movement. OBJECTIVE: To determine the effects of this associative stimulation protocol on the cortical motor map beyond the hotspot. METHODS: In healthy subjects, peripheral stimulation through passive hand opening by a robotic orthosis and single-pulse transcranial magnetic stimulation to the respective cortical motor representation were applied in a brain-machine interface environment...
August 22, 2018: Brain Stimulation
Dorothée Lulé, Katharina Hörner, Cynthia Vazquez, Helena Aho-Özhan, Jürgen Keller, Martin Gorges, Ingo Uttner, Albert C Ludolph
Background: In many neurological conditions, there is a combination of decline in physical function and cognitive abilities. For far advanced stages of physical disability where speaking and hand motor abilities are severely impaired, there is a lack of standardized approach to screen for cognitive profile. Methods: N = 40 healthy subjects were included in the study. For proof of principle, N = 6 ALS patients were additionally measured. For cognitive screening, we used the Edinburgh cognitive and behavioral ALS screen (ECAS) in the standard paper-and-pencil version...
2018: Frontiers in Neuroscience
Sommer L Amundsen Huffmaster, Gustaf M Van Acker, Carl W Luchies, Paul D Cheney
Neuromuscular control of voluntary movement may be simplified using muscle synergies similar to those found using non-negative matrix factorization. We recently identified synergies in electromyography (EMG) recordings associated with both voluntary movement and movement evoked by high-frequency long-duration intracortical microstimulation applied to the forelimb representation of the primary motor cortex (M1). The goal of this study was to use stimulus-triggered averaging (StTA) of EMG activity to investigate the synergy profiles and weighting coefficients associated with poststimulus facilitation, as synergies may be hard-wired into elemental cortical output modules and revealed by StTA...
October 10, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Kojiro Matsushita, Masayuki Hirata, Takafumi Suzuki, Hiroshi Ando, Takeshi Yoshida, Yuki Ota, Fumihiro Sato, Shayne Morris, Hisato Sugata, Tetsu Goto, Takufumi Yanagisawa, Toshiki Yoshimine
Brain-machine interfaces (BMIs) are promising devices that can be used as neuroprostheses by severely disabled individuals. Brain surface electroencephalograms (electrocorticograms, ECoGs) can provide input signals that can then be decoded to enable communication with others and to control intelligent prostheses and home electronics. However, conventional systems use wired ECoG recordings. Therefore, the development of wireless systems for clinical ECoG BMIs is a major goal in the field. We developed a fully implantable ECoG signal recording device for human ECoG BMI, i...
2018: Frontiers in Neuroscience
Takuya Fuchigami, Yumi Shikauchi, Ken Nakae, Manabu Shikauchi, Takeshi Ogawa, Shin Ishii
Functional magnetic resonance imaging (fMRI) acquisitions include a great deal of individual variability. This individuality often generates obstacles to the efficient use of databanks from multiple subjects. Although recent studies have suggested that inter-regional connectivity reflects individuality, conventional three-dimensional (3D) registration methods that calibrate inter-subject variability are based on anatomical information about the gray matter shape (e.g., T1-weighted). Here, we present a new registration method focusing more on the white matter structure, which is directly related to the connectivity in the brain, and apply it to subject-transfer brain decoding...
August 17, 2018: Scientific Reports
Benjamin C K Tee, Jianyong Ouyang
Flexible/stretchable electronic devices and systems are attracting great attention because they can have important applications in many areas, such as artificial intelligent (AI) robotics, brain-machine interfaces, medical devices, structural and environmental monitoring, and healthcare. In addition to the electronic performance, the electronic devices and systems should be mechanically flexible or even stretchable. Traditional electronic materials including metals and semiconductors usually have poor mechanical flexibility and very limited elasticity...
August 13, 2018: Advanced Materials
Eduardo López-Larraz, Carlos Escolano, Luis Montesano, Javier Minguez
Chronic spinal cord injury (SCI) patients present poor motor cortex activation during movement attempts. The reactivation of this brain region can be beneficial for them, for instance, allowing them to use brain-machine interfaces for motor rehabilitation or restoration. These brain-machine interfacess generally use electroencephalography (EEG) to measure the cortical activation during the attempts of movement, quantifying it as the event-related desynchronization (ERD) of the alpha/mu rhythm. Based on previous evidence showing that higher tonic EEG alpha power is associated with higher ERD, we hypothesized that artificially increasing the alpha power over the motor cortex of these patients could enhance their ERD (ie, motor cortical activation) during movement attempts...
August 7, 2018: Clinical EEG and Neuroscience: Official Journal of the EEG and Clinical Neuroscience Society (ENCS)
E López-Larraz, A Sarasola-Sanz, N Irastorza-Landa, N Birbaumer, A Ramos-Murguialday
BACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke...
2018: NeuroRehabilitation
Ryohei Fukuma, Takufumi Yanagisawa, Hiroshi Yokoi, Masayuki Hirata, Toshiki Yoshimine, Youichi Saitoh, Yukiyasu Kamitani, Haruhiko Kishima
Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training...
2018: Frontiers in Neuroscience
Dong Liu, Weihai Chen, Kyuhwa Lee, Ricardo Chavarriaga, Fumiaki Iwane, Mohamed Bouri, Zhongcai Pei, Jose Del R Millan
Brain-machine interfaces have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography. Active neural participation is crucial to promote brain plasticity thus to enhance the opportunity of motor recovery. This paper presents the decoding of lower-limb movement-related cortical potentials with continuous classification and asynchronous detection. We executed experiments in a customized gait trainer, where 10 healthy subjects performed self-initiated ankle plantar flexion...
August 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
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