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Brain machine interface

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https://www.readbyqxmd.com/read/29446329/response-to-contribution-of-eeg-signals-to-brain-machine-interfaces
#1
Marc W Slutzky, Robert D Flint
No abstract text is available yet for this article.
February 1, 2018: Journal of Neurophysiology
https://www.readbyqxmd.com/read/29446328/contribution-of-eeg-signals-to-brain-machine-interfaces
#2
Mehdi Ordikhani-Seyedlar, Karoline Doser
No abstract text is available yet for this article.
February 1, 2018: Journal of Neurophysiology
https://www.readbyqxmd.com/read/29432436/change-in-hippocampal-theta-oscillation-associated-with-multiple-lever-presses-in-a-bimanual-two-lever-choice-task-for-robot-control-in-rats
#3
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
https://www.readbyqxmd.com/read/29432111/toward-drowsiness-detection-using-non-hair-bearing-eeg-based-brain-computer-interfaces
#4
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
https://www.readbyqxmd.com/read/29422842/estimation-of-neuromuscular-primitives-from-eeg-slow-cortical-potentials-in-incomplete-spinal-cord-injury-individuals-for-a-new-class-of-brain-machine-interfaces
#5
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
https://www.readbyqxmd.com/read/29422841/detecting-pilot-s-engagement-using-fnirs-connectivity-features-in-an-automated-vs-manual-landing-scenario
#6
Kevin J Verdière, Raphaëlle N Roy, Frédéric Dehais
Monitoring pilot's mental states is a relevant approach to mitigate human error and enhance human machine interaction. A promising brain imaging technique to perform such a continuous measure of human mental state under ecological settings is Functional Near-InfraRed Spectroscopy (fNIRS). However, to our knowledge no study has yet assessed the potential of fNIRS connectivity metrics as long as passive Brain Computer Interfaces (BCI) are concerned. Therefore, we designed an experimental scenario in a realistic simulator in which 12 pilots had to perform landings under two contrasted levels of engagement (manual vs...
2018: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/29402310/fnirs-based-neurorobotic-interface-for-gait-rehabilitation
#7
Rayyan Azam Khan, Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, Hammad Nazeer, Muhammad Umer Khan
BACKGROUND: In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented. METHODS: fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error...
February 5, 2018: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/29379140/operation-of-a-p300-based-brain-computer-interface-in-patients-with-duchenne-muscular-dystrophy
#8
Kota Utsumi, Kouji Takano, Yoji Okahara, Tetsuo Komori, Osamu Onodera, Kenji Kansaku
A brain-computer interface (BCI) or brain-machine interface is a technology that enables the control of a computer and other external devices using signals from the brain. This technology has been tested in paralysed patients, such as those with cervical spinal cord injuries or amyotrophic lateral sclerosis, but it has not been tested systematically in Duchenne muscular dystrophy (DMD), which is a severe type of muscular dystrophy due to the loss of dystrophin and is often accompanied by progressive muscle weakness and wasting...
January 29, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29364932/prediction-of-movement-intention-using-connectivity-within-motor-related-network-an-electrocorticography-study
#9
Byeong Keun Kang, June Sic Kim, Seokyun Ryun, Chun Kee Chung
Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention...
2018: PloS One
https://www.readbyqxmd.com/read/29357477/emergent-coordination-underlying-learning-to-reach-to-grasp-with-a-brain-machine-interface
#10
Mukta Vaidya, Karthikeyan Balasubramanian, Joshua Southerland, Islam Badreldin, Ahmed Eleryan, Kelsey Shattuck, Suchin Gururangan, Marc W Slutzky, Leslie C Osborne, Andrew H Fagg, Karim G Oweiss, Nicholas G Hatsopoulos
The development of coordinated reach to grasp has been well-studied in infants and children (Kuhtz-Buschbeck, Stolze, Jöhnk, Boczek-Funcke, & Illert, 1998; von Hofsten, 1984a). However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach using a brain-machine interface (BMI) paradigm in rhesus macaques with prior therapeutic amputations to examine the emergence of novel, coordinated reach-to-grasp. Previous research has shown that after amputation, the cortical area previously involved in the control of the lost limb undergoes reorganization (Qi, Stepniewska, & Kaas, 2000; Schieber & Deuel, 1997; Wu & Kaas, 1999), but prior BMI work has largely relied on finding neurons that already encode specific movement-related information...
December 13, 2017: Journal of Neurophysiology
https://www.readbyqxmd.com/read/29357468/real-time-particle-filtering-and-smoothing-algorithms-for-detecting-abrupt-changes-in-neural-ensemble-spike-activity
#11
Sile Hu, Qiaosheng Zhang, Jing Wang, Zhe Chen
Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen et al., 2017, J. Neural Eng.), we have developed a latent state space model, known as Poisson linear dynamical system (PLDS), for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable...
December 20, 2017: Journal of Neurophysiology
https://www.readbyqxmd.com/read/29345632/brain-machine-interfaces-for-controlling-lower-limb-powered-robotic-systems
#12
Yongtian He, David Eguren, José M Azorín, Robert Grossman, Trieu Phat Luu, Jose Luis Pepe Contreras-Vidal
Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking...
January 18, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29343650/decoder-calibration-with-ultra-small-current-sample-set-for-intracortical-brain-machine-interface
#13
Peng Zhang, Xuan Ma, Luyao Chen, Jin Zhou, Changyong Wang, Wei Li, Jiping He
OBJECTIVE: Intracortical brain-machine interfaces (iBMIs) aim to restore efficient communication and movement ability for paralyzed patients. However, frequent recalibration is required for consistency and reliability, and every recalibration will require relatively large most current sample set. The aim in this study is to develop an effective decoder calibration method that can achieve good performance while minimizing recalibration time. APPROACH: Two rhesus macaques implanted with intracortical microelectrode arrays were trained separately on movement and sensory paradigm...
January 18, 2018: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29335359/recruitment-of-additional-corticospinal-pathways-in-the-human-brain-with-state-dependent-paired-associative-stimulation
#14
Dominic Kraus, Georgios Naros, Robert Guggenberger, Maria Teresa Leão, Ulf Ziemann, Alireza Gharabaghi
Standard brain stimulation protocols modify human motor cortex excitability by modulating the gain of the activated corticospinal pathways. However, the restoration of motor function following lesions of the corticospinal tract requires also the recruitment of additional neurons to increase the net corticospinal output. For this purpose, we investigated a novel protocol based on brain state-dependent paired associative stimulation.Motor imagery (MI)-related electroencephalography was recorded in healthy males and females for brain state-dependent control of both cortical and peripheral stimulation in a brain-machine interface environment...
January 15, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/29298691/volition-adaptive-control-for-gait-training-using-wearable-exoskeleton-preliminary-tests-with-incomplete-spinal-cord-injury-individuals
#15
Vijaykumar Rajasekaran, Eduardo López-Larraz, Fernando Trincado-Alonso, Joan Aranda, Luis Montesano, Antonio J Del-Ama, Jose L Pons
BACKGROUND: Gait training for individuals with neurological disorders is challenging in providing the suitable assistance and more adaptive behaviour towards user needs. The user specific adaptation can be defined based on the user interaction with the orthosis and by monitoring the user intentions. In this paper, an adaptive control model, commanded by the user intention, is evaluated using a lower limb exoskeleton with incomplete spinal cord injury individuals (SCI). METHODS: A user intention based adaptive control model has been developed and evaluated with 4 incomplete SCI individuals across 3 sessions of training per individual...
January 3, 2018: Journal of Neuroengineering and Rehabilitation
https://www.readbyqxmd.com/read/29297303/an-improved-discriminative-filter-bank-selection-approach-for-motor-imagery-eeg-signal-classification-using-mutual-information
#16
Shiu Kumar, Alok Sharma, Tatsuhiko Tsunoda
BACKGROUND: Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. METHODS: In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks...
December 28, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29281985/deep-convolutional-neural-networks-for-pan-specific-peptide-mhc-class-i-binding-prediction
#17
Youngmahn Han, Dongsup Kim
BACKGROUND: Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding...
December 28, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29277720/extracting-information-from-the-shape-and-spatial-distribution-of-evoked-potentials
#18
Vítor Lopes-Dos-Santos, Hernan G Rey, Joaquin Navajas, Rodrigo Quian Quiroga
BACKGROUND: Over 90 years after its first recording, scalp electroencephalography (EEG) remains one of the most widely used techniques in human neuroscience research, in particular for the study of event-related potentials (ERPs). However, because of its low signal-to-noise ratio, extracting useful information from these signals continues to be a hard-technical challenge. Many studies focus on simple properties of the ERPs such as peaks, latencies, and slopes of signal deflections. NEW METHOD: To overcome these limitations, we developed the Wavelet-Information method which uses wavelet decomposition, information theory, and a quantification based on single-trial decoding performance to extract information from evoked responses...
December 22, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/29234269/application-of-the-stockwell-transform-to-electroencephalographic-signal-analysis-during-gait-cycle
#19
Mario Ortiz, Marisol Rodríguez-Ugarte, Eduardo Iáñez, José M Azorín
The analysis of electroencephalographic signals in frequency is usually not performed by transforms that can extract the instantaneous characteristics of the signal. However, the non-steady state nature of these low voltage electrical signals makes them suitable for this kind of analysis. In this paper a novel tool based on Stockwell transform is tested, and compared with techniques such as Hilbert-Huang transform and Fast Fourier Transform, for several healthy individuals and patients that suffer from lower limb disability...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29224063/an-efficient-scheme-for-mental-task-classification-utilizing-reflection-coefficients-obtained-from-autocorrelation-function-of-eeg-signal
#20
M M Rahman, M A Chowdhury, S A Fattah
Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain-computer interface (BCI) applications. In the design of BCI systems, features extracted from lower frequency bands of scalp-recorded EEG signals are generally considered to classify mental tasks and higher frequency bands are mostly ignored as noise. However, in this paper, it is demonstrated that high frequency components of EEG signal can provide accommodating data for enhancing the classification performance of the mental task-based BCI...
December 9, 2017: Brain Informatics
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