Brain-computer interfaces: principles and practice ,
Electroencephalography (EEG)-Based Brain-Computer Interfaces, 2015. ,
DOI : 10.1002/047134608X.W8278
URL : https://hal.archives-ouvertes.fr/hal-01167515
Translating the Brain-Machine Interface, Science Translational Medicine, vol.5, issue.210, pp.210-227, 2013. ,
DOI : 10.1126/scitranslmed.3007303
A review of classification algorithms for EEG-based brain???computer interfaces, Journal of Neural Engineering, vol.4, issue.2, pp.1-13, 2007. ,
DOI : 10.1088/1741-2560/4/2/R01
URL : https://hal.archives-ouvertes.fr/inria-00134950
Could Anyone Use a BCI? Brain-Computer Interfaces ,
Evolving Signal Processing for Brain–Computer Interfaces, Proceedings of the IEEE, vol.100, issue.Special Centennial Issue, pp.1567-1584, 2012. ,
DOI : 10.1109/JPROC.2012.2185009
BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI?, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.18, issue.2, pp.107-116, 2009. ,
DOI : 10.1109/TNSRE.2009.2039495
How many people are able to control a P300-based brain???computer interface (BCI)?, Neuroscience Letters, vol.462, issue.1, pp.94-98, 2009. ,
DOI : 10.1016/j.neulet.2009.06.045
Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design, Frontiers in Human Neuroscience, vol.7, 2013. ,
DOI : 10.3389/fnhum.2013.00568
URL : https://hal.archives-ouvertes.fr/hal-00862716
Brain-computer interfacing: more than the sum of its parts. Soft computing, pp.317-331, 2013. ,
Thought-based interaction with the physical world. Trends in cognitive sciences, pp.490-492, 2013. ,
The User-Centered Design as Novel Perspective for Evaluating the Usability of BCI-Controlled Applications, PLoS ONE, vol.8, issue.12, p.112392, 2014. ,
DOI : 10.1371/journal.pone.0112392.t006
Toward Independent Home Use of Brain-Computer Interfaces: A Decision Algorithm for Selection of Potential End-Users, Archives of Physical Medicine and Rehabilitation, vol.96, issue.3, 2015. ,
DOI : 10.1016/j.apmr.2014.03.036
Brain Painting: Usability testing according to the user-centered design in end users with severe motor paralysis, Artificial Intelligence in Medicine, vol.59, issue.2 ,
DOI : 10.1016/j.artmed.2013.08.003
Design requirements and potential target users for brain-computer interfaces ??? recommendations from rehabilitation professionals, Brain-Computer Interfaces, vol.67, issue.1, pp.50-61, 2014. ,
DOI : 10.1080/10400435.1999.10131981
What would brain-computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis, Amyotrophic Lateral Sclerosis, vol.55, issue.5, pp.318-324, 2011. ,
DOI : 10.1016/S0022-510X(97)00253-0
Barriers to and mediators of brain???computer interface user acceptance: focus group findings, Ergonomics, vol.59, issue.182, pp.516-525, 2012. ,
DOI : 10.1016/S1388-2457(02)00057-3
Initial constructs for patient-centered outcome measures to evaluate brain???computer interfaces, Disability and Rehabilitation: Assistive Technology, vol.9 ,
DOI : 10.1007/s11136-014-0622-y
What Would Brain-Computer Interface Users Want: Opinions and Priorities of Potential Users With Spinal Cord Injury, Archives of Physical Medicine and Rehabilitation, vol.96, issue.3, pp.38-45, 2015. ,
DOI : 10.1016/j.apmr.2014.05.028
Brain-computer interface users speak up: the Virtual Users' Forum at the 2013 International Brain-Computer Interface meeting. Archives of physical medicine and rehabilitation, pp.33-37, 2015. ,
Long-Term Independent Brain-Computer Interface Home Use Improves Quality of Life of a Patient in the Locked-In State: A Case Study, Archives of Physical Medicine and Rehabilitation, vol.96, issue.3, pp.16-26, 2015. ,
DOI : 10.1016/j.apmr.2014.03.035
A brain-computer interface for long-term independent home use, Amyotrophic Lateral Sclerosis, vol.177, issue.5, pp.449-455, 2010. ,
DOI : 10.1212/01.wnl.0000205136.93011.4e
Noninvasive brain-computer interface enables communication after brainstem stroke, Science Translational Medicine, vol.6, issue.257, pp.257-264, 2014. ,
DOI : 10.1126/scitranslmed.3007801
Neurophysiological predictor of SMR-based BCI performance, NeuroImage, vol.51, issue.4, pp.1303-1309, 2010. ,
DOI : 10.1016/j.neuroimage.2010.03.022
Gamma band activity associated with BCI performance: simultaneous MEG/EEG study, Frontiers in Human Neuroscience, vol.7, 2013. ,
DOI : 10.3389/fnhum.2013.00848
Prediction of SSVEP-based BCI performance by the resting-state EEG network, Journal of Neural Engineering, vol.10, issue.6, p.66017, 2013. ,
DOI : 10.1088/1741-2560/10/6/066017
Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability IEEE Computational Intelligence Magazine, pp.32-61, 2015. ,
Long-term stable control of motor-imagery BCI by a locked-in user through adaptive assistance, IEEE Transactions on Neural Systems and Rehabilitation Engineering ,
DOI : 10.1109/TNSRE.2016.2645681
Effects of resting heart rate variability on performance in the P300 brain-computer interface, International Journal of Psychophysiology, vol.83, issue.3, pp.336-341, 2012. ,
DOI : 10.1016/j.ijpsycho.2011.11.018
Evoked-Potential Correlates of Stimulus Uncertainty, Science, vol.150, issue.3700, pp.1187-1188, 1965. ,
DOI : 10.1126/science.150.3700.1187
Auditory standard oddball and visual P300 brain-computer interface performance, International Journal of Bioelectromagnetism, vol.13, issue.1, pp.5-6, 2011. ,
DOI : 10.1371/journal.pone.0053513
URL : http://doi.org/10.1371/journal.pone.0053513
Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state, Frontiers in Neuroscience, vol.7, 2013. ,
DOI : 10.3389/fnins.2013.00129
Psychological predictors of SMR-BCI performance, Biological Psychology, vol.89, issue.1, pp.80-86, 2012. ,
DOI : 10.1016/j.biopsycho.2011.09.006
Motor imagery questionnaire as a method to detect BCI illiteracy. Paper presented at, 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2010. ,
Advances in User-Training for ,
URL : https://hal.archives-ouvertes.fr/hal-01302138
Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis, Frontiers in Human Neuroscience, vol.7, 2013. ,
DOI : 10.3389/fnhum.2013.00732
Motivation modulates the P300 amplitude during brain???computer interface use, Clinical Neurophysiology, vol.121, issue.7, 2010. ,
DOI : 10.1016/j.clinph.2010.01.034
A portable auditory P300 brain???computer interface with directional cues, Clinical Neurophysiology, vol.124, issue.2, pp.327-338, 2013. ,
DOI : 10.1016/j.clinph.2012.08.006
A clinical screening protocol for the RSVP Keyboard brain???computer interface, Disability and Rehabilitation: Assistive Technology, vol.3, issue.1 ,
DOI : 10.1080/09602010443000425
A public data hub for benchmarking common brain???computer interface algorithms, Journal of Neural Engineering, vol.8, issue.2, p.25021, 2011. ,
DOI : 10.1088/1741-2560/8/2/025021
An Open-Access P300 Speller Database. Paper presented at: Fourth International Brain-Computer Interface Meeting, 2010. ,
URL : https://hal.archives-ouvertes.fr/inria-00549242
Developing Core Sets for Persons With Traumatic Brain Injury Based on the International Classification of Functioning, Disability, and Health, Neurorehabilitation and Neural Repair, vol.23, issue.5, pp.464-467, 2009. ,
DOI : 10.1177/1545968308328725
Towards an ICF Core Set for chronic musculoskeletal conditions: commonalities across ICF Core Sets for osteoarthritis, rheumatoid arthritis, osteoporosis, low back pain and chronic widespread pain, Clinical Rheumatology, vol.36, issue.S44, pp.1355-1361, 2008. ,
DOI : 10.1007/s10067-008-0916-y
Development of ICF core sets for patients with chronic conditions, J Rehabil Med, 2004. ,
Soliciting BCI user experience feedback from people with severe speech and physical impairments, Brain-Computer Interfaces, vol.15, issue.4 ,
DOI : 10.1016/j.jclinepi.2010.04.011
Augmenting communication, emotion expression and interaction capabilities of individuals with cerebral palsy. Paper presented at: 2014 6th International Brain-Computer Interface Conference, pp.312-315, 2014. ,
On the control of brain-computer interfaces by users with cerebral palsy, Clinical Neurophysiology, vol.124, issue.9, pp.1787-1797, 2013. ,
DOI : 10.1016/j.clinph.2013.02.118
Thought-based row-column scanning communication board for individuals with cerebral palsy, Annals of Physical and Rehabilitation Medicine, vol.58, issue.1, pp.14-22, 2015. ,
DOI : 10.1016/j.rehab.2014.11.005
URL : http://dx.doi.org/10.1016/j.rehab.2014.11.005
Game-based BCI training: Interactive design for individuals with cerebral palsy. Paper presented at, IEEE International Conference on Systems, Man, and Cybernetics, p.2015, 2015. ,
Let's play Tic-Tac-Toe: A ,
Training locked-in patients: a challenge for the use of brain~computer interfaces, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.11, issue.2, pp.169-172, 2003. ,
DOI : 10.1109/TNSRE.2003.814431
Brain-Computer Interfaces Neurofeedback Training for BCI Control. The Frontiers Collection, pp.65-78, 2010. ,
DOI : 10.1007/978-3-642-02091-9_4
Distributed cortical adaptation during learning of a brain-computer interface task, Proceedings of the National Academy of Sciences, vol.110, issue.26, pp.10818-10841, 2013. ,
DOI : 10.1073/pnas.1221127110
Imagery of motor actions: Differential effects of kinesthetic and visual???motor mode of imagery in single-trial EEG, Cognitive Brain Research, vol.25, issue.3 ,
DOI : 10.1016/j.cogbrainres.2005.08.014
Towards Improved BCI based on Human Learning Principles. Paper presented at: 3rd International Brain-Computer Interfaces Winter Conference, 2015. ,
DOI : 10.1109/iww-bci.2015.7073024
URL : https://hal.archives-ouvertes.fr/hal-01111843
Focus on Formative Feedback, Review of Educational Research, vol.78, issue.1, pp.153-189, 2008. ,
DOI : 10.3102/0034654307313795
The Power of Feedback, Review of Educational Research, vol.77, issue.1, pp.81-112, 2007. ,
DOI : 10.3102/003465430298487
First principles of instruction, instructional design and technology, pp.62-71, 2007. ,
DOI : 10.1007/BF02505024
Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study, Journal of Neural Engineering, vol.13, issue.3, p.36024, 2016. ,
DOI : 10.1088/1741-2560/13/3/036024
URL : https://hal.archives-ouvertes.fr/hal-01302154
Why and How to Use Intelligent Tutoring Systems to Adapt MI-BCI Training to Each User?, Paper presented at: International BCI meeting, 2016. ,
Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns, PLOS ONE, vol.25, issue.1, p.143962, 2015. ,
DOI : 10.1371/journal.pone.0143962.g008
URL : https://hal.archives-ouvertes.fr/hal-01177685
Psychological Factors Influencing Brain-Computer Interface (BCI) Performance. Paper presented at, IEEE International Conference on Systems, pp.3192-3196, 2015. ,
DOI : 10.1109/smc.2015.554
Continuous Tactile Feedback for Motor-Imagery based Brain-Computer Interaction in a Multitasking Context. Paper presented at, Proc. Interact, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01159146
Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery, Journal of Neural Engineering, vol.8, issue.3, p.36005, 2011. ,
DOI : 10.1088/1741-2560/8/3/036005
The effect of multimodal and enriched feedback on SMR-BCI performance, Clinical Neurophysiology, vol.127, issue.1, pp.490-498, 2016. ,
DOI : 10.1016/j.clinph.2015.06.004
Combining BCI with Virtual Reality: Towards New Applications and Improved BCI Towards Practical Brain-Computer Interfaces, pp.197-220, 2013. ,
DOI : 10.1007/978-3-642-29746-5_10
URL : https://hal.inria.fr/hal-00735932/file/Chap_BCI_VR.pdf
A spelling device for the paralysed, Nature, vol.398, issue.6725, pp.297-298, 1999. ,
DOI : 10.1038/18581
Fast acquisition of effective performance in untrained subjects, NeuroImage, vol.37, issue.2, pp.539-550, 2007. ,
A Comprehensive Survey of Brain Interface Technology Designs, Annals of Biomedical Engineering, vol.10, issue.3, pp.137-69, 2007. ,
DOI : 10.1007/s10439-006-9170-0
Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces, IEEE Transactions on Biomedical Engineering, vol.58, issue.3, pp.587-97, 2011. ,
DOI : 10.1109/TBME.2010.2093133
Autocalibration and Recurrent Adaptation: Towards a Plug and Play Online ERD-BCI, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.20, issue.3, pp.313-319, 2012. ,
DOI : 10.1109/TNSRE.2012.2189584
A Co-Adaptive Brain-Computer Interface for End Users with Severe Motor Impairment, PLoS ONE, vol.43, issue.7 ,
DOI : 10.1371/journal.pone.0101168.t002
URL : http://doi.org/10.1371/journal.pone.0101168
Transferring Subspaces Between Subjects in Brain--Computer Interfacing, IEEE Transactions on Biomedical Engineering, vol.60, issue.8, pp.2289-2298, 2013. ,
DOI : 10.1109/TBME.2013.2253608
Subject-oriented training for motor imagery brain-computer interfaces. Paper presented at, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1259-1262, 2014. ,
DOI : 10.1109/embc.2014.6943826
URL : http://infoscience.epfl.ch/record/200202
Restricted Boltzmann Machines in Sensory Motor Rhythm Brain-Computer Interfacing: A study on inter-subject transfer and co-adaptation, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016. ,
DOI : 10.1109/SMC.2016.7844284
A Self-Paced and Calibration-Less SSVEP-Based Brain–Computer Interface Speller, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.18, issue.2, pp.127-133, 2010. ,
DOI : 10.1109/TNSRE.2009.2039594
True Zero-Training Brain-Computer Interfacing ??? An Online Study, PLoS ONE, vol.2010, issue.7 ,
DOI : 10.1371/journal.pone.0102504.g009
Effects of training and motivation on auditory P300 brain???computer interface performance, Clinical Neurophysiology, vol.127, issue.1, pp.379-387, 2016. ,
DOI : 10.1016/j.clinph.2015.04.054
The Elements of Statistical Learning, 2009. ,
Honey, I Shrunk the Sample Covariance Matrix, The Journal of Portfolio Management, vol.30, issue.4, pp.110-119, 2004. ,
DOI : 10.3905/jpm.2004.110
Introduction to machine learning for brain imaging, NeuroImage, vol.56, issue.2, pp.387-399, 2011. ,
DOI : 10.1016/j.neuroimage.2010.11.004
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces, Proceedings of the IEEE, vol.103, issue.6, pp.871-890, 2015. ,
DOI : 10.1109/JPROC.2015.2404941
Optimizing Spatial filters for Robust EEG Single-Trial Analysis, IEEE Signal Processing Magazine, vol.25, issue.1, pp.41-56, 2008. ,
DOI : 10.1109/MSP.2008.4408441
Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation, Frontiers in Neuroscience, vol.61, issue.1033, 2015. ,
DOI : 10.1109/TBME.2014.2312397
A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain???Computer Interfaces, Guide to Brain-Computer Music Interfacing, pp.133-161, 2014. ,
DOI : 10.1007/978-1-4471-6584-2_7
URL : https://hal.archives-ouvertes.fr/hal-01055103
Optimal spatial filtering of single trial EEG during imagined hand movement, IEEE Transactions on Rehabilitation Engineering, vol.8, issue.4, pp.441-446, 2000. ,
DOI : 10.1109/86.895946
Divergence-Based Framework for Common Spatial Patterns Algorithms, IEEE Reviews in Biomedical Engineering, vol.7, pp.50-72, 2013. ,
DOI : 10.1109/RBME.2013.2290621
EEG beta suppression and low gamma modulation are different elements of human upright walking, Frontiers in Human Neuroscience, vol.8, 2014. ,
High and low gamma EEG oscillations in central sensorimotor areas are conversely modulated during the human gait cycle, NeuroImage, vol.112, pp.318-344, 2015. ,
DOI : 10.1016/j.neuroimage.2015.03.045
The effect of distinct mental strategies on classification performance for brain???computer interfaces, International Journal of Psychophysiology, vol.84, issue.1, pp.86-94, 2012. ,
DOI : 10.1016/j.ijpsycho.2012.01.014
Whatever Works: A Systematic User-Centered Training Protocol to Optimize Brain-Computer Interfacing Individually, PLoS ONE, vol.127, issue.9, 2013. ,
DOI : 10.1371/journal.pone.0076214.t002
Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability, PLOS ONE, vol.25, issue.1 ,
DOI : 10.1371/journal.pone.0123727.t001
URL : http://doi.org/10.1371/journal.pone.0123727
Bring mental activity into action! Self-tuning brain-computer interfaces, Paper presented at: 2015 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015. ,
DOI : 10.1109/embc.2015.7318858
EMG and EOG artifacts in brain computer interface systems: A survey, Clinical Neurophysiology, vol.118, issue.3, 2007. ,
DOI : 10.1016/j.clinph.2006.10.019
A fully automated correction method of EOG artifacts in EEG recordings, Clinical Neurophysiology, vol.118, issue.1, pp.98-104, 2007. ,
DOI : 10.1016/j.clinph.2006.09.003
FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.23, issue.5, pp.725-736, 2015. ,
DOI : 10.1109/TNSRE.2014.2346621
The hybrid BCI Front ,
DOI : 10.3389/fnpro.2010.00003
URL : http://doi.org/10.3389/fnpro.2010.00003
A hybrid brain???computer interface based on the fusion of electroencephalographic and electromyographic activities, Journal of Neural Engineering, vol.8, issue.2, p.25011, 2011. ,
DOI : 10.1088/1741-2560/8/2/025011
Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond, Proceedings of the IEEE, vol.103, issue.6, pp.926-943, 2015. ,
DOI : 10.1109/JPROC.2015.2411333
EEG artifact removal???state-of-the-art and guidelines, Journal of Neural Engineering, vol.12, issue.3, p.31001, 2015. ,
DOI : 10.1088/1741-2560/12/3/031001
An introduction to ROC analysis Pattern Recognition Letters, pp.861-874, 2006. ,
DOI : 10.1016/j.patrec.2005.10.010
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.646.2144
Brain-computer interface technology: a review of the first international meeting, IEEE Transactions on Rehabilitation Engineering, vol.8, issue.2, pp.164-173, 2000. ,
DOI : 10.1109/TRE.2000.847807
Evaluation of the performances of different P300 based brain???computer interfaces by means of the efficiency metric, Journal of Neuroscience Methods, vol.203, issue.2, pp.361-368, 2012. ,
DOI : 10.1016/j.jneumeth.2011.10.010
An analysis of performance evaluation for motor-imagery based BCI, Journal of Neural Engineering, vol.10, issue.3, p.31001, 2013. ,
DOI : 10.1088/1741-2560/10/3/031001
URL : https://hal.archives-ouvertes.fr/hal-00821971
On the Usage of Linear Regression Models to Reconstruct Limb Kinematics from Low Frequency EEG Signals, PLoS ONE, vol.25, issue.4, p.61976, 2013. ,
DOI : 10.1371/journal.pone.0061976.g009
Comparing metrics to evaluate performance of regression methods for decoding of neural signals, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ,
DOI : 10.1109/EMBC.2015.7318553
Better than random: A closer look on BCI results, International Journal of Bioelectromagnetism, vol.10, pp.52-55, 2008. ,
Nonparametric statistical testing of EEG- and MEG-data, Journal of Neuroscience Methods, vol.164, issue.1, pp.177-190, 2007. ,
DOI : 10.1016/j.jneumeth.2007.03.024
The ASA's statement on p-values: context, process, and purpose The American Statistician, pp.129-133, 2016. ,
The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study, Frontiers in Neuroscience, vol.4, p.55, 2010. ,
DOI : 10.3389/fnins.2010.00055
Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis. Amyotrophic lateral sclerosis and frontotemporal degeneration, pp.3-4207, 2014. ,
Evaluation Criteria for BCI Research Toward brain-computer interfacing, 2007. ,
The Utility Metric: A Novel Method to Assess the Overall Performance of Discrete Brain–Computer Interfaces, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.18, issue.1, pp.20-28, 2010. ,
DOI : 10.1109/TNSRE.2009.2032642
A general method for assessing brain???computer interface performance and its limitations, Journal of Neural Engineering, vol.11, issue.2, p.26018, 2014. ,
DOI : 10.1088/1741-2560/11/2/026018
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4113089
Workload measurement in a communication application operated through a P300-based brain???computer interface, Journal of Neural Engineering, vol.8, issue.2, p.25028, 2011. ,
DOI : 10.1088/1741-2560/8/2/025028
Development of NASA-TLX (Task Load Index ,
How scientists fool themselves ??? and how they can stop, Nature, vol.526, issue.7572, pp.182-185, 2015. ,
DOI : 10.1038/526182a
Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls, Frontiers in Neuroscience, vol.9, p.136, 2015. ,
DOI : 10.3389/fnins.2015.00136
URL : http://doi.org/10.3389/fnins.2015.00136
Event-related potentials in clinical research: Guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400, Clinical Neurophysiology, vol.120, issue.11, pp.1883-1908, 2009. ,
DOI : 10.1016/j.clinph.2009.07.045
Ten Simple Rules for Effective Statistical Practice, PLOS Computational Biology, vol.531, issue.151, p.1004961, 2016. ,
DOI : 10.1371/journal.pcbi.1004961
URL : http://doi.org/10.1371/journal.pcbi.1004961
Matching person & technology (MPT) assessment process, Technology and Disability, vol.14, issue.3, pp.125-131, 2002. ,
DOI : 10.1037/e315622004-001
A framework for modelling the selection of assistive technology devices (ATDs), Disability and Rehabilitation: Assistive Technology, vol.50, issue.2, pp.1-8, 2007. ,
DOI : 10.1037/10629-000