Fusion of EEG and EMG signals for classification of unilateral foot movements
暂无分享,去创建一个
[1] Daniel P. Ferris,et al. Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton , 2015, Journal of NeuroEngineering and Rehabilitation.
[2] Jose L. Contreras-Vidal,et al. Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors , 2016, Front. Neurosci..
[3] Niels Birbaumer,et al. A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).
[4] Alberto Borboni,et al. EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review , 2018, Sensors.
[5] Fan Zhang,et al. Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion , 2011, IEEE Transactions on Biomedical Engineering.
[6] Andrew N Meltzoff,et al. Neural correlates of action observation and execution in 14-month-old infants: an event-related EEG desynchronization study. , 2011, Developmental science.
[7] Angkoon Phinyomark,et al. Feature extraction of the first difference of EMG time series for EMG pattern recognition , 2014, Comput. Methods Programs Biomed..
[8] Klaus-Robert Müller,et al. Enhanced Performance by a Hybrid Nirs–eeg Brain Computer Interface , 2022 .
[9] Yasuharu Koike,et al. Reconstruction of flexor and extensor muscle activities from electroencephalography cortical currents , 2012, NeuroImage.
[10] Masa-aki Sato,et al. Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methods , 2011, NeuroImage.
[11] C. Braun,et al. Movement related cortical potentials change after EEG-BMI rehabilitation in chronic stroke , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).
[12] Chao Li,et al. A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control , 2016, Sensors.
[13] L. Montesano,et al. Detecting intention to walk in stroke patients from pre-movement EEG correlates , 2015, Journal of NeuroEngineering and Rehabilitation.
[14] Junliang Huang,et al. Hybrid Brain/Muscle Signals Powered Wearable Walking Exoskeleton Enhancing Motor Ability in Climbing Stairs Activity , 2019, IEEE Transactions on Medical Robotics and Bionics.
[15] Gong Chen,et al. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. , 2013, Critical reviews in biomedical engineering.
[16] Andreea Ioana Sburlea,et al. EEG neural correlates of goal-directed movement intention , 2017, NeuroImage.
[17] Nicolas Y. Masse,et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.
[18] Huosheng Hu,et al. Bio-signal based control in assistive robots: a survey , 2015, Digit. Commun. Networks.
[19] G. Pfurtscheller,et al. Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients , 2007, Brain Research.
[20] T. Hortobágyi,et al. Teager–Kaiser energy operator signal conditioning improves EMG onset detection , 2010, European Journal of Applied Physiology.
[21] Sherin Youssef,et al. Hybrid Brain Computer Interface for Movement Control of Upper Limb Prostheses , 2018, 2018 International Conference on Biomedical Engineering and Applications (ICBEA).
[22] Artur Polinski,et al. The role of EMG module in hybrid interface of prosthetic arm , 2017, 2017 10th International Conference on Human System Interactions (HSI).
[23] Arne D. Ekstrom,et al. Single-Neuron Responses in Humans during Execution and Observation of Actions , 2010, Current Biology.
[24] Ping Zhou,et al. Wavelet Packet Feature Assessment for High-Density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation , 2016, Front. Neurol..
[25] Nurhazimah Nazmi,et al. Walking gait event detection based on electromyography signals using artificial neural network , 2019, Biomed. Signal Process. Control..
[26] Min-Chun Pan,et al. Lower-limb motion classification for hemiparetic patients through IMU and EMG signal processing , 2016, 2016 International Conference on Biomedical Engineering (BME-HUST).
[27] Yuan-Ting Zhang,et al. A novel channel selection method for multiple motion classification using high-density electromyography , 2014, BioMedical Engineering OnLine.
[28] Clemens Brunner,et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.
[29] J L Pons,et al. Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials , 2014, Journal of neural engineering.
[30] Angel Rodríguez-Liñán,et al. Hybrid BCI approach to control an artificial tibio-femoral joint , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[31] Siti Anom Ahmad,et al. Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications , 2015, Medical & Biological Engineering & Computing.
[32] Pornchai Phukpattaranont,et al. Feature reduction and selection for EMG signal classification , 2012, Expert Syst. Appl..
[33] Masa-aki Sato,et al. Evaluation of hierarchical Bayesian method through retinotopic brain activities reconstruction from fMRI and MEG signals , 2008, NeuroImage.
[34] N. Ward,et al. Assessment of cortical reorganisation for hand function after stroke , 2011, The Journal of physiology.
[35] Neelesh Kumar,et al. Scrutinizing different EEG-Based Mechanisms for Motor Control and Rehabilitation of Lower Limb Disabilities , 2017 .
[36] Yoshiaki Hayashi,et al. Towards Hybrid EEG-EMG-Based Control Approaches to be Used in Bio-robotics Applications: Current Status, Challenges and Future Directions , 2013, Paladyn J. Behav. Robotics.
[37] Antonio Frisoli,et al. Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications , 2019, IEEE Robotics and Automation Letters.
[38] Dong Liu,et al. EEG-Based Lower-Limb Movement Onset Decoding: Continuous Classification and Asynchronous Detection , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[39] Oluwarotimi Williams Samuel,et al. A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees , 2017, Journal of NeuroEngineering and Rehabilitation.
[40] N. Bolognini,et al. Grasping with the foot: Goal and motor expertise in action observation , 2014, Human brain mapping.
[41] Yi Wu,et al. Neurophysiological substrates of stroke patients with motor imagery-based brain-computer interface training , 2014, The International journal of neuroscience.
[42] Jerald D. Kralik,et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.
[43] Guanglin Li,et al. New Control Strategies for Multifunctional Prostheses that Combine Electromyographic and Speech Signals , 2015, IEEE Intelligent Systems.
[44] Ricardo Chavarriaga,et al. A hybrid brain–computer interface based on the fusion of electroencephalographic and electromyographic activities , 2011, Journal of neural engineering.
[45] S. Kastner,et al. Complex organization of human primary motor cortex: a high-resolution fMRI study. , 2008, Journal of neurophysiology.
[46] Sangtae Ahn,et al. Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces – Current Limitations and Future Directions , 2017, Front. Hum. Neurosci..
[47] Yasuharu Koike,et al. Decoding finger movement in humans using synergy of EEG cortical current signals , 2017, Scientific Reports.
[48] Ganesh R. Naik,et al. Nonnegative Matrix Factorization for the Identification of EMG Finger Movements: Evaluation Using Matrix Analysis , 2015, IEEE Journal of Biomedical and Health Informatics.
[49] Dennis C. Tkach,et al. Study of stability of time-domain features for electromyographic pattern recognition , 2010, Journal of NeuroEngineering and Rehabilitation.
[50] Neelesh Kumar,et al. A Novel Approach for Real-Time Gait Events Detection Using Developed Wireless Foot Sensor Module , 2019, IEEE Sensors Letters.
[51] Miguel A. L. Nicolelis,et al. Extracting Kinematic Parameters for Monkey Bipedal Walking from Cortical Neuronal Ensemble Activity , 2009, Front. Integr. Neurosci..
[52] Wellington Pinheiro dos Santos,et al. Electromyography-controlled car: A proof of concept based on surface electromyography, Extreme Learning Machines and low-cost open hardware , 2019, Comput. Electr. Eng..
[53] Dario Farina,et al. The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[54] Yasuharu Koike,et al. Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods , 2018, Front. Neurosci..
[55] Hiroshi Yokoi,et al. Training in Use of Brain–Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements , 2018, Front. Neurosci..
[56] Jinghui Cao,et al. Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation , 2018, Adv. Robotics.
[57] G. Pfurtscheller,et al. Foot and hand area mu rhythms. , 1997, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[58] G. Pfurtscheller,et al. Functional dissociation of lower and upper frequency mu rhythms in relation to voluntary limb movement , 2000, Clinical Neurophysiology.
[59] Yunfa Fu,et al. A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching , 2015, Journal of neural engineering.