A. Ameri, E. N. Kamavuako, E. J. Scheme, K. B. Englehart, and P. A. Parker, Support Vector Regression for Improved Real-Time, Simultaneous Myoelectric Control, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.22, issue.6, pp.1198-1209, 2014.
DOI : 10.1109/TNSRE.2014.2323576

B. Amirikian and A. P. Georgopulos, Directional tuning profiles of motor cortical cells, Neuroscience Research, vol.36, issue.1, pp.73-79, 2000.
DOI : 10.1016/S0168-0102(99)00112-1

D. J. Berger, R. Gentner, T. Edmunds, D. K. Pai, and A. Avella, Differences in Adaptation Rates after Virtual Surgeries Provide Direct Evidence for Modularity, Journal of Neuroscience, vol.33, issue.30, pp.12384-12394, 2013.
DOI : 10.1523/JNEUROSCI.0122-13.2013

S. M. Chase, R. E. Kass, and A. B. Schwartz, Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex, Journal of Neurophysiology, vol.80, issue.2, pp.624-644, 2012.
DOI : 10.1007/s002210050462

S. Dangi, A. L. Orsborn, H. G. Moorman, and J. M. Carmena, Design and Analysis of Closed-Loop Decoder Adaptation Algorithms for Brain-Machine Interfaces, Neural Computation, vol.15, issue.7, pp.25-1693, 2013.
DOI : 10.1088/1741-2560/3/2/010

M. Davare, A. Zénon, M. Desmurget, and E. Olivier, Dissociable contribution of the parietal and frontal cortex to coding movement direction and amplitude, Frontiers in Human Neuroscience, vol.9, p.241, 2015.
DOI : 10.3389/fnhum.2015.00241

A. De-rugy, Generalization of visuomotor adaptation to different muscles is less efficient: Experiment and model, Human Movement Science, vol.29, issue.5, pp.684-700, 2010.
DOI : 10.1016/j.humov.2010.01.008

A. De-rugy and T. J. Carroll, Changes in muscle directional tuning parallel feedforward adaptation to a visuomotor rotation, Experimental Brain Research, vol.24, issue.Pt 3, pp.701-709, 2010.
DOI : 10.1080/00222895.1993.9941644

A. De-rugy, G. E. Loeb, and T. J. Carroll, Muscle Coordination Is Habitual Rather than Optimal, Journal of Neuroscience, vol.32, issue.21, pp.7384-7391, 2012.
DOI : 10.1523/JNEUROSCI.5792-11.2012

A. De-rugy, G. E. Loeb, and T. J. Carroll, Virtual biomechanics: a new method for online reconstruction of force from EMG recordings, Journal of Neurophysiology, vol.14, issue.12, pp.3333-3341, 2012.
DOI : 10.1152/jn.00178.2007

M. Dimitriou, D. M. Wolpert, and D. W. Franklin, The Temporal Evolution of Feedback Gains Rapidly Update to Task Demands, Journal of Neuroscience, vol.33, issue.26, pp.10898-10909, 2013.
DOI : 10.1523/JNEUROSCI.5669-12.2013

O. Donchin, J. T. Francis, and R. Shadmehr, Quantifying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: theory and experiments in human motor control, The Journal of neuroscience, vol.23, issue.27, pp.9032-9045, 2003.

S. Dura-bernal, G. L. Chadderdon, S. A. Neymotin, J. T. Francis, and W. W. Lytton, Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm, Pattern Recognition Letters, vol.36, pp.204-212, 2014.
DOI : 10.1016/j.patrec.2013.05.019

S. Dura-bernal, K. Li, S. A. Neymotin, J. T. Francis, J. C. Principe et al., Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm, Frontiers in Neuroscience, vol.17, issue.99, 2016.
DOI : 10.1038/nrn3112

D. Farina, N. Jiang, H. Rehbaum, A. Holobar, B. Graimann et al., The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.22, issue.4, pp.797-809, 2014.
DOI : 10.1109/TNSRE.2014.2305111

J. M. Galea, A. Vazquez, N. Pasricha, J. O. De-xivry, and P. Celnik, Dissociating the Roles of the Cerebellum and Motor Cortex during Adaptive Learning: The Motor Cortex Retains What the Cerebellum Learns, Cerebral Cortex, vol.21, issue.8, pp.1761-1770, 2011.
DOI : 10.1093/cercor/bhq246

A. P. Georgopoulos, J. T. Lurito, M. Petrides, A. B. Schwartz, and J. Massey, Mental rotation of the neuronal population vector, Science, vol.243, issue.4888, pp.234-236, 1989.
DOI : 10.1126/science.2911737

V. Gilja, P. Nuyujukian, C. A. Chestek, J. P. Cunningham, B. M. Yu et al., A high-performance neural prosthesis enabled by control algorithm design, Nature Neuroscience, vol.2009, issue.12, pp.15-1752, 2012.
DOI : 10.1038/nature04968

J. M. Hahne, F. Biessmann, N. Jiang, H. Rehbaum, D. Farina et al., Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.22, issue.2, pp.269-279, 2014.
DOI : 10.1109/TNSRE.2014.2305520
URL : http://doi.org/10.1109/tnsre.2014.2305520

J. M. Hahne, S. Dähne, H. Hwang, K. Müller, and L. C. Parra, Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.23, issue.4, pp.618-627, 2015.
DOI : 10.1109/TNSRE.2015.2401134

A. M. Haith and J. W. Krakauer, Theoretical models of motor control and motor learning, Routledge handbook of motor control and motor learning pp, pp.7-28, 2013.

J. He, D. Zhang, N. Jiang, X. Sheng, D. Farina et al., User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control, Journal of Neural Engineering, vol.12, issue.4, p.46005, 2015.
DOI : 10.1088/1741-2560/12/4/046005

D. J. Herzfeld, P. A. Vaswani, M. K. Marko, and R. Shadmehr, A memory of errors in sensorimotor learning, Science, vol.17, issue.1, pp.1349-1353, 2014.
DOI : 10.1523/JNEUROSCI.3071-08.2008

E. J. Hwang, P. M. Bailey, and R. A. Andersen, Volitional Control of Neural Activity Relies on the Natural Motor Repertoire, Current Biology, vol.23, issue.5, pp.353-361, 2013.
DOI : 10.1016/j.cub.2013.01.027

C. Igel and M. Hüsken, Empirical evaluation of the improved Rprop learning algorithms, Neurocomputing, vol.50, pp.105-123, 2003.
DOI : 10.1016/S0925-2312(01)00700-7

M. Ison and P. Artemiadis, Proportional Myoelectric Control of Robots: Muscle Synergy Development Drives Performance Enhancement, Retainment, and Generalization, IEEE Transactions on Robotics, vol.31, issue.2, pp.259-268, 2015.
DOI : 10.1109/TRO.2015.2395731

I. Iturrate, J. Grizou, J. Omedes, P. Oudeyer, M. Lopes et al., Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials, PLOS ONE, vol.5, issue.1, pp.10-0131491, 2015.
DOI : 10.1371/journal.pone.0131491.t001
URL : https://hal.archives-ouvertes.fr/hal-01246436

N. Jiang, S. Dosen, K. Müller, and D. Farina, Myoelectric Control of Artificial Limbs???Is There a Need to Change Focus? [In the Spotlight], IEEE Signal Processing Magazine, vol.29, issue.5, pp.152-150, 2012.
DOI : 10.1109/MSP.2012.2203480

N. Jiang, K. B. Englehart, and P. A. Parker, Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal, IEEE Transactions on Biomedical Engineering, vol.56, issue.4, pp.1070-1080, 2009.
DOI : 10.1109/TBME.2008.2007967

J. W. Krakauer, Motor Learning and Consolidation: The Case of Visuomotor Rotation, Adv. Exp. Med. Biol, vol.629, pp.405-421, 2009.
DOI : 10.1007/978-0-387-77064-2_21

J. W. Krakauer, Z. M. Pine, M. Ghilardi, and C. Ghez, Learning of visuomotor transformations for vectorial planning of reaching trajectories, The Journal of neuroscience, vol.20, issue.23, pp.8916-8924, 2000.

K. Nazarpour, A. Barnard, and A. Jackson, Flexible Cortical Control of Task-Specific Muscle Synergies, Journal of Neuroscience, vol.32, issue.36, pp.12349-12360, 2012.
DOI : 10.1523/JNEUROSCI.5481-11.2012

A. L. Orsborn, S. Dangi, H. G. Moorman, and J. M. Carmena, Closed-Loop Decoder Adaptation on Intermediate Time-Scales Facilitates Rapid BMI Performance Improvements Independent of Decoder Initialization Conditions, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.20, issue.4, pp.468-477, 2012.
DOI : 10.1109/TNSRE.2012.2185066

E. Poh, T. J. Carroll, and A. De-rugy, Distinct coordinate systems for adaptations of movement direction and extent, Journal of Neurophysiology, vol.62, issue.5, p.326, 2017.
DOI : 10.1152/jn.00212.2012

G. Raphael, G. A. Tsianos, and G. E. Loeb, Spinal-Like Regulator Facilitates Control of a Two-Degree-of-Freedom Wrist, Journal of Neuroscience, vol.30, issue.28, pp.9431-9444, 2010.
DOI : 10.1523/JNEUROSCI.5537-09.2010

I. M. Rezazadeh, M. Firoozabadi, H. Hu, and S. M. Golpayegani, Co-Adaptive and Affective Human-Machine Interface for Improving Training Performances of Virtual Myoelectric Forearm Prosthesis, IEEE Transactions on Affective Computing, vol.3, issue.3, pp.285-297, 2012.
DOI : 10.1109/T-AFFC.2012.3

M. Riedmiller and H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, IEEE International Conference on Neural Networks, pp.586-591, 1993.
DOI : 10.1109/ICNN.1993.298623

P. T. Sadtler, K. M. Quick, M. D. Golub, S. M. Chase, S. I. Ryu et al., Neural constraints on learning, Neural constraints on learning, pp.423-426, 2014.
DOI : 10.1037/h0055392
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4393644/pdf

S. H. Scott, T. Clu?, C. R. Lowrey, and T. Takei, Feedback control during voluntary motor actions, Current Opinion in Neurobiology, vol.33, pp.85-94, 2015.
DOI : 10.1016/j.conb.2015.03.006

M. M. Shanechi, A. L. Orsborn, and J. M. Carmena, Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering, PLOS Computational Biology, vol.11, issue.6, p.1004730, 2016.
DOI : 10.1371/journal.pcbi.1004730.s010
URL : http://doi.org/10.1371/journal.pcbi.1004730

K. V. Shenoy and J. M. Carmena, Combining Decoder Design and Neural Adaptation in Brain-Machine Interfaces, Neuron, vol.84, issue.4, pp.665-680, 2014.
DOI : 10.1016/j.neuron.2014.08.038
URL : https://doi.org/10.1016/j.neuron.2014.08.038

M. A. Smith, A. Ghazizadeh, and R. Shadmehr, Interacting adaptive processes with di?erent timescales underlie short-term motor learning, PLoS Biol, vol.4, pp.1035-1043, 2006.
DOI : 10.1371/journal.pbio.0040179
URL : https://doi.org/10.1371/journal.pbio.0040179

H. Tanaka, T. J. Sejnowski, and J. W. Krakauer, Adaptation to Visuomotor Rotation Through Interaction Between Posterior Parietal and Motor Cortical Areas, Journal of Neurophysiology, vol.15, issue.5, pp.2921-2932, 2009.
DOI : 10.1016/0006-8993(78)90036-7
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2777823/pdf

J. A. Taylor, L. L. Hieber, and R. B. Ivry, Feedback-dependent generalization, Journal of Neurophysiology, vol.20, issue.1, pp.202-215, 2013.
DOI : 10.1007/s00221-002-1211-9
URL : http://jn.physiology.org/content/jn/109/1/202.full.pdf

K. A. Thoroughman and R. Shadmehr, Learning of action through adaptive combination of motor primitives, Nature, vol.407, pp.742-747, 2000.

G. A. Tsianos, J. Goodner, and G. E. Loeb, Useful properties of spinal circuits for learning and performing planar reaches, Journal of Neural Engineering, vol.11, issue.5, p.56006, 2014.
DOI : 10.1088/1741-2560/11/5/056006

M. R. Tuga, R. Rupp, D. Liebetanz, R. Mikut, and M. Reischl, Concept of a co-adaptive training environment for human-machine interfaces based on emgcontrol, Biomed Tech, vol.58, 2013.

C. Vidaurre, C. Sannelli, K. Müller, and B. Blankertz, Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces, Neural Computation, vol.8, issue.3, pp.791-816, 2011.
DOI : 10.1016/S1388-2457(02)00057-3

M. M. Vidovic, L. P. Paredes, H. Hwang, S. Amsu, J. Pahl et al., Covariate shift adaptation in EMG pattern recognition for prosthetic device control, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.4370-4373, 2014.
DOI : 10.1109/EMBC.2014.6944592

P. Vindras, M. Desmurget, and P. Viviani, Error Parsing in Visuomotor Pointing Reveals Independent Processing of Amplitude and Direction, Journal of Neurophysiology, vol.94, issue.2, pp.1212-1224, 2005.
DOI : 10.1007/BF00230024

B. Wodlinger, J. Downey, E. Tyler-kabara, A. Schwartz, M. Boninger et al., Ten-dimensional anthropomorphic arm control in a human brain???machine interface: difficulties, solutions, and limitations, Journal of Neural Engineering, vol.12, issue.1, p.16011, 2014.
DOI : 10.1088/1741-2560/12/1/016011