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Brain machine interfaces

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https://www.readbyqxmd.com/read/28730995/decoding-human-mental-states-by-whole-head-eeg-fnirs-during-category-fluency-task-performance
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
https://www.readbyqxmd.com/read/28692997/automated-classification-and-removal-of-eeg-artifacts-with-svm-and-wavelet-ica
#8
Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Paul Cumming, Masahiro Iwahashi
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain computer interface (BCI) applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform (DWT) has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal...
July 4, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28688489/a-pca-aided-cross-covariance-scheme-for-discriminative-feature-extraction-from-eeg-signals
#9
Roozbeh Zarei, Jing He, Siuly Siuly, Yanchun Zhang
BACKGROUND AND OBJECTIVES: Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. METHODS: This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications...
July 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28682261/towards-on-demand-deep-brain-stimulation-using-online-parkinson-s-disease-prediction-driven-by-dynamic-detection
#10
Ameer Mohammed, Majid Zamani, Richard Bayford, Andreas Demosthenous
In Parkinson's disease (PD), on-demand deep brain stimulation (DBS) is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation and real-time detection...
July 3, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28680906/neurofeedback-based-functional-near-infrared-spectroscopy-upregulates-motor-cortex-activity-in-imagined-motor-tasks
#11
Pawan Lapborisuth, Xian Zhang, Adam Noah, Joy Hirsch
Neurofeedback is a method for using neural activity displayed on a computer to regulate one's own brain function and has been shown to be a promising technique for training individuals to interact with brain-machine interface applications such as neuroprosthetic limbs. The goal of this study was to develop a user-friendly functional near-infrared spectroscopy (fNIRS)-based neurofeedback system to upregulate neural activity associated with motor imagery, which is frequently used in neuroprosthetic applications...
April 2017: Neurophotonics
https://www.readbyqxmd.com/read/28676734/fuzzy-decision-making-fuser-fdmf-for-integrating-human-machine-autonomous-hma-systems-with-adaptive-evidence-sources
#12
Yu-Ting Liu, Nikhil R Pal, Amar R Marathe, Yu-Kai Wang, Chin-Teng Lin
A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28669301/editorial-advancement-in-brain-machine-interfaces-for-patients-with-tetraplegia-neurosurgical-perspective
#13
Kejia Hu, Firas Bounni, Ziv Williams
No abstract text is available yet for this article.
July 2017: Neurosurgical Focus
https://www.readbyqxmd.com/read/28665058/decoding-saccadic-directions-using-epidural-ecog-in-non-human-primates
#14
Jeyeon Lee, Hoseok Choi, Seho Lee, Baek Hwan Cho, Kyoung Ha Ahn, In Young Kim, Kyoung Min Lee, Dong Pyo Jang
A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution...
August 2017: Journal of Korean Medical Science
https://www.readbyqxmd.com/read/28648720/an-engineered-home-environment-for-untethered-data-telemetry-from-nonhuman-primates
#15
Marc P Powell, William R Britz, James S Harper, David A Borton
BACKGROUND: Wireless neural recording technologies now provide untethered access to large populations of neurons in the nonhuman primate brain. Such technologies enable long-term, continuous interrogation of neural circuits and importantly open the door for chronic neurorehabilitation platforms. For example, by providing continuous consistent closed loop feedback from a brain machine interface, the nervous system can leverage plasticity to integrate more effectively into the system than would be possible in short experimental sessions...
June 23, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/28644398/a-novel-wearable-forehead-eog-measurement-system-for-human-computer-interfaces
#16
Jeong Heo, Heenam Yoon, Kwang Suk Park
Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is generated by eye movements and can be measured with electrodes placed around the eye. In this study, we proposed a new practical electrode position on the forehead to measure EOG signals, and we developed a wearable forehead EOG measurement system for use in Human Computer/Machine interfaces (HCIs/HMIs)...
June 23, 2017: Sensors
https://www.readbyqxmd.com/read/28642168/the-posterior-parietal-cortex-as-integrative-hub-for-whisker-sensorimotor-information
#17
REVIEW
Hemanth Mohan, Roel de Haan, Huibert D Mansvelder, Christiaan P J de Kock
Our daily life consists of a continuous interplay between incoming sensory information and outgoing motor plans. Particularly during goal-directed behavior and active exploration of the sensory environment, brain circuits are merging sensory and motor signals. This is referred to as sensorimotor integration and is relevant for locomotion, vision or tactile exploration. The somatosensory (tactile) system is an attractive modality to study sensorimotor integration in health and disease, motivated by the need for revolutionary technology that builds upon conceptual understanding of sensorimotor integration, such as brain-machine-interfaces and neuro-prosthetics...
June 19, 2017: Neuroscience
https://www.readbyqxmd.com/read/28640544/interactions-of-neurons-with-physical-environments
#18
REVIEW
Michal Marcus, Koby Baranes, Matthew Park, Insung S Choi, Kyungtae Kang, Orit Shefi
Nerve growth strongly relies on multiple chemical and physical signals throughout development and regeneration. Currently, a cure for injured neuronal tissue is an unmet need. Recent advances in fabrication technologies and materials led to the development of synthetic interfaces for neurons. Such engineered platforms that come in 2D and 3D forms can mimic the native extracellular environment and create a deeper understanding of neuronal growth mechanisms, and ultimately advance the development of potential therapies for neuronal regeneration...
June 22, 2017: Advanced Healthcare Materials
https://www.readbyqxmd.com/read/28630937/high-precision-neural-decoding-of-complex-movement-trajectories-using-recursive-bayesian-estimation-with-dynamic-movement-primitives
#19
Guy Hotson, Ryan J Smith, Adam G Rouse, Marc H Schieber, Nitish V Thakor, Brock A Wester
Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedom of robotic end-effectors. While BMIs have yet to achieve the level of reliability desired for widespread clinical use, environmental sensors (e.g. RGB-D cameras for object detection) and prior knowledge of common movement trajectories hold great potential for improving system performance...
July 2016: IEEE Robotics and Automation Letters
https://www.readbyqxmd.com/read/28625485/motor-cortical-visuomotor-feedback-activity-is-initially-isolated-from-downstream-targets-in-output-null-neural-state-space-dimensions
#20
Sergey D Stavisky, Jonathan C Kao, Stephen I Ryu, Krishna V Shenoy
Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent...
July 5, 2017: Neuron
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