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Brain machine interface

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https://www.readbyqxmd.com/read/28821649/effector-invariant-movement-encoding-in-the-human-motor-system
#1
Shlomi Haar, Ilan Dinstein, Ilan Shelef, Opher Donchin
Ipsilateral motor areas of cerebral cortex are active during arm movements and even reliably predict movement direction. Is coding similar during ipsilateral and contralateral movements? If so, is it in extrinsic (world-centered) or intrinsic (joint-configuration) coordinates? We addressed these questions by examining the similarity of multi-voxel fMRI patterns in visuomotor cortical regions during unilateral reaching movements with both arms. The results of three complementary analyses revealed that fMRI response patterns were similar across right and left arm movements to identical targets (extrinsic coordinates) in visual cortices, and across movements with equivalent joint-angles (intrinsic coordinates) in motor cortices...
August 16, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28816702/as-above-so-below-towards-understanding-inverse-models-in-bci
#2
Jussi T Lindgren
In Brain-Computer Interfaces (BCI), measurements of the users brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the cortical volume and mixed in the EEG. Does the reconstruction of the source activities in the volume help in building more accurate BCIs? The answer remains inconclusive despite previous work. In this paper, we study the question by contrasting the physiology-driven source reconstruction with data-driven representations obtained by statistical machine learning...
August 17, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28813935/influence-of-artifacts-on-movement-intention-decoding-from-eeg-activity-in-severely-paralyzed-stroke-patients
#3
Eduardo Lopez-Larraz, Carlos Bibian, Niels Birbaumer, Ander Ramos-Murguialday
Brain-machine interfaces (BMI) can be used to control robotic and prosthetic devices for rehabilitation of motor disorders, such as stroke. The calibration of these BMI systems is of paramount importance in order to establish a precise contingent link between the brain activity related to movement intention and the peripheral feedback. However, electroencephalographic (EEG) activity, commonly used to build non-invasive BMIs, can be easily contaminated by artifacts of electrical or physiological origin. The way these interferences can affect the performance of movement intention decoders has not been deeply studied, especially when dealing with severely paralyzed patients, which often generate more artifacts by compensatory movements...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813934/a-hybrid-brain-machine-interface-based-on-eeg-and-emg-activity-for-the-motor-rehabilitation-of-stroke-patients
#4
Andrea Sarasola-Sanz, Nerea Irastorza-Landa, Eduardo Lopez-Larraz, Carlos Bibian, Florian Helmhold, Doris Broetz, Niels Birbaumer, Ander Ramos-Murguialday
Including supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs for assistive and rehabilitative applications. Electromyographic (EMG) activity is one of the most widely utilized biosignals in hBMIs, as it provides a quite direct measurement of the motion intention of the user...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813929/soft-brain-machine-interfaces-for-assistive-robotics-a-novel-control-approach
#5
Lucia Schiatti, Jacopo Tessadori, Giacinto Barresi, Leonardo S Mattos, Arash Ajoudani
Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813811/a-multichannel-near-infrared-spectroscopy-triggered-robotic-hand-rehabilitation-system-for-stroke-patients
#6
Jongseung Lee, Nobutaka Mukae, Jumpei Arata, Hiroyuki Iwata, Keiji Iramina, Koji Iihara, Makoto Hashizume
There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813805/improving-robotic-stroke-rehabilitation-by-incorporating-neural-intent-detection-preliminary-results-from-a-clinical-trial
#7
Jennifer L Sullivan, Nikunj A Bhagat, Nuray Yozbatiran, Ruta Paranjape, Colin G Losey, Robert G Grossman, Jose L Contreras-Vidal, Gerard E Francisco, Marcia K O'Malley
This paper presents the preliminary findings of a multi-year clinical study evaluating the effectiveness of adding a brain-machine interface (BMI) to the MAHI-Exo II, a robotic upper limb exoskeleton, for elbow flexion/extension rehabilitation in chronic stroke survivors. The BMI was used to trigger robot motion when movement intention was detected from subjects' neural signals, thus requiring that subjects be mentally engaged during robotic therapy. The first six subjects to complete the program have shown improvements in both Fugl-Meyer Upper-Extremity scores as well as in kinematic movement quality measures that relate to movement planning, coordination, and control...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
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
#8
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
#9
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/28769747/movement-related-sensorimotor-high-gamma-activity-mainly-represents-somatosensory-feedback
#10
Seokyun Ryun, June S Kim, Eunjeong Jeon, Chun K Chung
Somatosensation plays pivotal roles in the everyday motor control of humans. During active movement, there exists a prominent high-gamma (HG >50 Hz) power increase in the primary somatosensory cortex (S1), and this provides an important feature in relation to the decoding of movement in a brain-machine interface (BMI). However, one concern of BMI researchers is the inflation of the decoding performance due to the activation of somatosensory feedback, which is not elicited in patients who have lost their sensorimotor function...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28745300/on-robust-parameter-estimation-in-brain-computer-interfacing
#11
Wojciech Samek, Shinichi Nakajima, Motoaki Kawanabe, Klaus-Robert Mueller
The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in Brain-Computer Interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g., due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal...
July 26, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28745299/inferring-imagined-speech-using-eeg-signals-a-new-approach-using-riemannian-manifold-features
#12
Chuong H Nguyen, Georgios Karavas, Panagiotis Artemiadis
OBJECTIVE: In this paper, we investigate the suitability of imagined speech for Brain-Computer Interface applications (BCI). APPROACH: A novel method based on covariance matrix descriptors, which lie in Riemannian manifold, and the Relevance Vector Machines classifier is proposed. The method is applied on ElectroEncephaloGraphic (EEG) signals and tested in multiple subjects. MAIN RESULTS: The method is shown to outperform other approaches in the field with respect to accuracy and robustness...
July 26, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28740505/object-extraction-in-cluttered-environments-via-a-p300-based-ifce
#13
Xiaoqian Mao, Wei Li, Huidong He, Bin Xian, Ming Zeng, Huihui Zhou, Linwei Niu, Genshe Chen
One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE)...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28730995/decoding-human-mental-states-by-whole-head-eeg-fnirs-during-category-fluency-task-performance
#14
Ahmet Omurtag, Haleh Aghajani, Hasan Onur Keles
Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results...
July 21, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28722685/neural-control-of-finger-movement-via-intracortical-brain-machine-interface
#15
Zachary T Irwin, Karen E Schroeder, Philip P Vu, Autumn J Bullard, Derek M Tat, Chrono S Nu, Alex Vaskov, Samuel R Nason, David E Thompson, Nicole Bentley, Parag G Patil, Cynthia A Chestek
OBJECTIVE: Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. APPROACH: In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets...
July 19, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28718779/a-review-and-experimental-study-on-application-of-classifiers-and-evolutionary-algorithms-in-eeg-based-brain-machine-interface-systems
#16
Farajollah Tahernezhad-Javazm, Vahid Azimirad, Maryam Shoaran
OBJECTIVE: Considering the importance and the near future development of noninvasive Brain-Machine Interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. APPROACH: The paper is divided into two main parts. In the first part a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and also evolutionary algorithms are reviewed and investigated...
July 18, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28715332/current-source-density-estimation-enhances-the-performance-of-motor-imagery-related-brain-computer-interface
#17
Dheeraj Rathee, Haider Raza, Girijesh Prasad, Hubert Cecotti
The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI) related brain-computer interface systems. Two EEG referencing schemes: common referencing, common average referencing (CAR) and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model, were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks...
July 13, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28713235/connecting-the-brain-to-itself-through-an-emulation
#18
Mijail D Serruya
Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28711988/emotion-recognition-based-on-eeg-features-in-movie-clips-with-channel-selection
#19
Mehmet Siraç Özerdem, Hasan Polat
Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain-computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems...
July 15, 2017: Brain Informatics
https://www.readbyqxmd.com/read/28696340/brain-actuated-gait-trainer-with-visual-and-proprioceptive-feedback
#20
Dong Liu, Weihai Chen, Kyuhwa Lee, Ricardo Chavarriaga, Mohamed Bouri, Zhongcai Pei, Jose Del R Millan
OBJECTIVE: Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension. APPROACH: We executed experiments in a lower-limb gait trainer (the legoPress) where nine able-bodied subjects participated in three consecutive sessions based on a crossover design...
July 11, 2017: Journal of Neural Engineering
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