An end-to-end deep learning approach to MI-EEG signal classification for BCIs
Tài liệu tham khảo
Aghaei, 2016, Separable common spatio-spectral patterns for motor imagery BCI systems, IEEE Transactions on Biomedical Engineering, 63, 15, 10.1109/TBME.2015.2487738
AL, 2013, Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals, Circulation, 101, e215
Ang, 2008, Filter bank common spatial pattern (FBCSP) in brain-computer interface, 2390
Bashivan, 2016, Learning representations from EEG with deep recurrent-convolutional neural networks, International Conference on Learning Representations (ICLR)
Bates, 2015, Repetitive transcranial magnetic stimulation for stroke rehabilitation - potential therapy or misplaced hope?, Restorative Neurology and Neuroscience, 33, 557, 10.3233/RNN-130359
Bentlemsan, 2014, Random forest and filter bank common spatial patterns for eeg-based motor imagery classification, 235
Chaudhary, 2016, Brain-computer interfaces for communication and rehabilitation, Nature Reviews Neurology, 12, 513, 10.1038/nrneurol.2016.113
Do, 2011, Brain-computer interface controlled functional electrical stimulation system for ankle movement, Journal of NeuroEngineering and Rehabilitation, 8, 49, 10.1186/1743-0003-8-49
Do, 2012, Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke, 6414
Galán, 2008, A brain-actuated wheelchair: Asynchronous and non-invasive brain-computer interfaces for continuous control of robots, Clinical Neurophysiology, 119, 2159, 10.1016/j.clinph.2008.06.001
Goodfellow, 2016
Grosse-Wentrup, 2008, Multiclass common spatial patterns and information theoretic feature extraction, IEEE Transactions on Biomedical Engineering, 55, 1991, 10.1109/TBME.2008.921154
Guan, 2004, High performance P300 speller for brain-computer interface
Halder, 2007, Online artifact removal for brain-computer interfaces using support vector machines and blind source separation, Computational Intelligence and Neuroscience, 2007, 10.1155/2007/82069
Handiru, 2016, Optimized bi-objective EEG channel selection and cross-subject generalization with brain-computer interfaces, IEEE Transactions on Human-Machine Systems, 46, 777, 10.1109/THMS.2016.2573827
Jure, 2016, BCI-FES System for neuro-rehabilitation of stroke patients, Journal of Physics: Conference Series, 705
Kim, 2016, Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns, Computational Intelligence and Neuroscience, 2016, 10.1155/2016/1489692
Kim, 2016, Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns, Intelligence and Neuroscience, 2016
Kumar, 2016, A deep learning approach for motor imagery EEG signal classification, 34
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Leuthardt, 2009, Evolution of brain-computer interfaces: Going beyond classic motor physiology, Neurosurgical Focus, 27, 10.3171/2009.4.FOCUS0979
Loboda, 2014, Discrimination of EEG-based motor imagery tasks by means of a simple phase information method, International Journal of Advanced Research in Artificial Intelligence, 3, 10.14569/IJARAI.2014.031002
Lotze, 2006, Motor imagery, Journal of Physiology - Paris, 99, 386, 10.1016/j.jphysparis.2006.03.012
Ma, 2016, Classification of motor imagery EEG signals with support vector machines and particle swarm optimization, Computational and Mathematical Methods in Medicine, 2016, 8, 10.1155/2016/4941235
Meng, 2008, BCI-FES training system design and implementation for rehabilitation of stroke patients, 4103
Mulder, 2007, Motor imagery and action observation: Cognitive tools for rehabilitation, Journal of Neural Transmission, 114, 1265, 10.1007/s00702-007-0763-z
National Institute of Neurological Disorders and Stroke (2014). Post-stroke rehabilitation.
Nielsen, 2015
Nielsen, 2015
Park, 2013, Classification of motor imagery BCI using multivariate empirical mode decomposition, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21, 10, 10.1109/TNSRE.2012.2229296
Park, 2014, Augmented complex common spatial patterns for classification of noncircular eeg from motor imagery tasks, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22, 1, 10.1109/TNSRE.2013.2294903
Park, 2014, Augmented complex common spatial patterns for classification of noncircular EEG from motor imagery tasks, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22, 1, 10.1109/TNSRE.2013.2294903
Pichiorri, 2015, Brain-computer interface boosts motor imagery practice during stroke recovery, Annals of Neurology, 77, 851, 10.1002/ana.24390
Pinter, 2013, Role of repetitive transcranial magnetic stimulation in stroke rehabilitation., Front. Neurol. Neurosci., 32, 112, 10.1159/000346433
Schalk, 2004, BCI2000: A general-purpose brain-computer interface (BCI) system, IEEE Transactions on Biomedical Engineering, 51, 1034, 10.1109/TBME.2004.827072
Schirrmeister, 2017, Deep learning with convolutional neural networks for EEG decoding and visualization, Human Brain Mapping, 38, 5391, 10.1002/hbm.23730
Shen, 2017, Classification of motor imagery EEG signals with deep learning models, 181
Stippich, 2002, Somatotopic mapping of the human primary sensorimotor cortex during motor imagery and motor execution by functional magnetic resonance imaging, Neuroscience Letters, 331, 50, 10.1016/S0304-3940(02)00826-1
Tabar, 2017, A novel deep learning approach for classification of EEG motor imagery signals, Journal of Neural Engineering, 14, 16003, 10.1088/1741-2560/14/1/016003
Tang, 2017, Single-trial eeg classification of motor imagery using deep convolutional neural networks, Optik, 130, 11, 10.1016/j.ijleo.2016.10.117
Tolic, 2013, Classification of wavelet transformed EEG signals with neural network for imagined mental and motor tasks, Kinesiology, 45, 130
Vallabhaneni, 2004, Motor imagery task classification for brain computer interface applications using spatiotemporal principle component analysis, Neurological Research, 26, 282, 10.1179/016164104225013950
Wang, 2005, Common spatial pattern method for channel selection in motor imagery based brain-computer interface, 5392
World Stroke Organization (WSO) (2016). WSO Background and mission statement. Annual Report, (p. 6).
Yang, 2015, On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of eeg signals classification, 2015, 2620
Yang, 2016, Subject-specific channel selection using time information for motor imagery brain–computer interfaces, Cognitive Computation, 8, 505, 10.1007/s12559-015-9379-z
Yang, 2014, Time-frequency optimization for discrimination between imagination of right and left hand movements based on two bipolar electroencephalography channels, EURASIP Journal on Advances in Signal Processing, 38, 1
Yang, 2017, Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few laplacian EEG channels, Biomedical Signal Processing and Control, 38, 302, 10.1016/j.bspc.2017.06.016
Young, 2014, BCI-FES: Could a new rehabilitation device hold fresh promise for stroke patients?, Expert Review of Medical Devices, 11, 537, 10.1586/17434440.2014.941811