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Brain machine interfaces

Paul D Marasco, Jacqueline S Hebert, Jon W Sensinger, Courtney E Shell, Jonathon S Schofield, Zachary C Thumser, Raviraj Nataraj, Dylan T Beckler, Michael R Dawson, Dan H Blustein, Satinder Gill, Brett D Mensh, Rafael Granja-Vazquez, Madeline D Newcomb, Jason P Carey, Beth M Orzell
To effortlessly complete an intentional movement, the brain needs feedback from the body regarding the movement's progress. This largely nonconscious kinesthetic sense helps the brain to learn relationships between motor commands and outcomes to correct movement errors. Prosthetic systems for restoring function have predominantly focused on controlling motorized joint movement. Without the kinesthetic sense, however, these devices do not become intuitively controllable. We report a method for endowing human amputees with a kinesthetic perception of dexterous robotic hands...
March 14, 2018: Science Translational Medicine
Xiaofeng Xie, Zhu Liang Yu, Zhenghui Gu, Jun Zhang, Ling Cen, Yuanqing Li
In off-line training of motor imagery-based brain-computer interfaces (BCIs), to enhance the generalization performance of the learned classifier, the local information contained in test data could be used to improve the performance of motor imagery as well. Further considering that the covariance matrices of electroencephalogram (EEG) signal lie on Riemannian manifold, in this paper, we construct a Riemannian graph to incorporate the information of training and test data into processing. The adjacency and weight in Riemannian graph are determined by the geodesic distance of Riemannian manifold...
March 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Hongzhi Qi, Yuqi Xue, Lichao Xu, Yong Cao, Xuejun Jiao
P300 spellers are among the most popular brain-computer interface paradigms, and they are used for many clinical applications. However, building the classifier for identifying event-related potential (ERP) responses, i.e., calibrating the P300 speller, is still a time-consuming and user-dependent problem. This paper proposes a novel method to reduce calibration times significantly. In the proposed method, a small number of ERP epochs from the current user were used to build a reference epoch. Based on this reference, the Riemannian distance measurement was used to select similar ERP samples from an existing data pool, which contained other-subject ERP responses...
March 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Qingqing Zheng, Fengyuan Zhu, Pheng-Ann Heng
Electroencephalogram (EEG) signals are of complex structure and can be naturally represented as matrices. Classification is one of the most important steps for EEG signal processing. Newly developed classifiers can handle these matrix-form data by adding low-rank constraint to leverage the correlation within each data. However, classification of EEG signals is still challenging, because EEG signals are always contaminated by measurement artifacts, outliers, and non-standard noise sources. As a result, existing matrix classifiers may suffer from performance degradation, because they typically assume that the input EEG signals are clean...
March 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Ahmed Youssef Ali Amer, Benjamin Wittevrongel, Marc M Van Hulle
Four novel EEG signal features for discriminating phase-coded steady-state visual evoked potentials (SSVEPs) are presented, and their performance in view of target selection in an SSVEP-based brain-computer interfacing (BCI) is assessed. The novel features are based on phase estimation and correlations between target responses. The targets are decoded from the feature scores using the least squares support vector machine (LS-SVM) classifier, and it is shown that some of the proposed features compete with state-of-the-art classifiers when using short (0...
March 6, 2018: Sensors
Bengt Ljungquist, Per Petersson, Anders J Johansson, Jens Schouenborg, Martin Garwicz
Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources...
March 5, 2018: Neuroinformatics
Ryan M Neely, Aaron C Koralek, Vivek R Athalye, Rui M Costa, Jose M Carmena
Animals acquire behaviors through instrumental conditioning. Brain-machine interfaces have used instrumental conditioning to reinforce patterns of neural activity directly, especially in frontal and motor cortices, which are a rich source of signals for voluntary action. However, evidence suggests that activity in primary sensory cortices may also reflect internally driven processes, instead of purely encoding antecedent stimuli. Here, we show that rats and mice can learn to produce arbitrary patterns of neural activity in their primary visual cortex to control an auditory cursor and obtain reward...
February 22, 2018: Neuron
Fabien Lotte, Laurent Bougrain, Andrzej Cichocki, Maureen Clerc, Marco Congedo, Alain Rakotomamonjy, Florian Yger

 Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs.
 We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs...
February 28, 2018: Journal of Neural Engineering
Min-Ki Kim, Jeong-Woo Sohn, Bongsoo Lee, Sung-Phil Kim
BACKGROUND: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble...
February 27, 2018: Biomedical Engineering Online
Somayeh B Shafiei, Ahmed Aly Hussein, Sarah Feldt Muldoon, Khurshid A Guru
Mutual trust is important in surgical teams, especially in robot-assisted surgery (RAS) where interaction with robot-assisted interface increases the complexity of relationships within the surgical team. However, evaluation of trust between surgeons is challenging and generally based on subjective measures. Mentor-Trainee trust was defined as assessment of mentor on trainee's performance quality and approving trainee's ability to continue performing the surgery. Here, we proposed a novel method of objectively assessing mentor-trainee trust during RAS based on patterns of brain activity of surgical mentor observing trainees...
February 26, 2018: Scientific Reports
Jose L Contreras-Vidal, Magdo Bortole, Fangshi Zhu, Kevin Nathan, Anusha Venkatakrishnan, Gerard E Francisco, Rogelio Soto, Jose L Pons
OBJECTIVE: Advancements in robot-assisted gait rehabilitation and brain-machine interfaces (BMI) may enhance stroke physiotherapy by engaging patients while providing information about robot-induced cortical adaptations. We investigate the feasibility of decoding walking from brain activity in stroke survivors during therapy using a powered exoskeleton integrated with an electroencephalography (EEG)-based BMI. DESIGN: The H2 powered exoskeleton was designed for overground gait training with actuated hip, knee and ankle joints...
February 23, 2018: American Journal of Physical Medicine & Rehabilitation
YiYan Wang, Pingxiao Wang, Yuguo Yu
Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen...
2018: Frontiers in Neuroscience
Subash Padmanaban, Justin Baker, Bradley Greger
Objective: The performance of machine learning algorithms used for neural decoding of dexterous tasks may be impeded due to problems arising when dealing with high-dimensional data. The objective of feature selection algorithms is to choose a near-optimal subset of features from the original feature space to improve the performance of the decoding algorithm. The aim of our study was to compare the effects of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis (PCA), and Mutual Information Maximization on SVM classification performance for a dexterous decoding task...
2018: Frontiers in Neuroscience
Zhimin Lin, Chi Zhang, Ying Zeng, Li Tong, Bin Yan
A brain-computer interface (BCI) is an advanced human-machine interaction technology. The BCI speller is a typical application that detects the stimulated source-induced EEG signal to identify the expected characters of the subjects. The current mainstream matrix-based BCI speller involves two problems that remain unsolved, namely, gaze-dependent and space-dependent problems. Some scholars have designed gaze-independent and space-independent spelling systems. However, this system still cannot achieve a satisfactory information transfer rate (ITR)...
February 20, 2018: Scientific Reports
Saurabh Vyas, Nir Even-Chen, Sergey D Stavisky, Stephen I Ryu, Paul Nuyujukian, Krishna V Shenoy
Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity...
February 13, 2018: Neuron
Marc W Slutzky, Robert D Flint
No abstract text is available yet for this article.
February 1, 2018: Journal of Neurophysiology
Mehdi Ordikhani-Seyedlar, Karoline Doser
No abstract text is available yet for this article.
February 1, 2018: Journal of Neurophysiology
Norifumi Tanaka, Katsunari Sano, Md Ashrafur Rahman, Ryota Miyata, Genci Capi, Shigenori Kawahara
Hippocampal theta oscillations have been implicated in working memory and attentional process, which might be useful for the brain-machine interface (BMI). To further elucidate the properties of the hippocampal theta oscillations that can be used in BMI, we investigated hippocampal theta oscillations during a two-lever choice task. During the task body-restrained rats were trained with a food reward to move an e-puck robot towards them by pressing the correct lever, ipsilateral to the robot several times, using the ipsilateral forelimb...
2018: PloS One
Chun-Shu Wei, Yu-Te Wang, Chin-Teng Lin, Tzyy-Ping Jung
Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin...
February 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Andrés Úbeda, José M Azorín, Dario Farina, Massimo Sartori
One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices...
2018: Frontiers in Computational Neuroscience
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