EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine.
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Wei Cheng | Lin Gao | Jue Wang | Jinhua Zhang | Lin Gao | Jue Wang | Jinhua Zhang | Wei Cheng
[1] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[2] Jin Xu,et al. Quantitative measure of complexity of the dynamic event-related EEG data , 2006, Neurocomputing.
[3] Klaus-Robert Müller,et al. Spatio-spectral filters for improving the classification of single trial EEG , 2005, IEEE Transactions on Biomedical Engineering.
[4] G Pfurtscheller,et al. Current trends in Graz Brain-Computer Interface (BCI) research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[5] Yong Liu,et al. Multi-class AdaBoost ELM , 2015 .
[6] René de Jesús Romero-Troncoso,et al. Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method , 2017, Comput. Intell. Neurosci..
[7] Moritz Grosse-Wentrup,et al. Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction , 2008, IEEE Transactions on Biomedical Engineering.
[8] Trevor Hastie,et al. Multi-class AdaBoost ∗ , 2009 .
[9] E Donchin,et al. Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[10] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[11] G. Pfurtscheller,et al. EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.
[12] J Wackermann,et al. Multichannel EEG fields during and without visual input: frequency domain model source locations and dimensional complexities , 1997, Neuroscience Letters.
[13] G. Pfurtscheller,et al. On-line EEG classification during externally-paced hand movements using a neural network-based classifier. , 1996, Electroencephalography and clinical neurophysiology.
[14] Jin Xu,et al. Adaboost with SVM-Based Classifier for the Classification of Brain Motor Imagery Tasks , 2011, HCI.
[15] G. Pfurtscheller,et al. EEG-based neuroprosthesis control: A step towards clinical practice , 2005, Neuroscience Letters.
[16] G Pfurtscheller,et al. Contrasting behavior of beta event-related synchronization and somatosensory evoked potential after median nerve stimulation during finger manipulation in man , 2002, Neuroscience Letters.
[17] L. Kirkup,et al. Control of a hand grasp neuroprosthesis using an electroencephalogram-triggered switch: Demonstration of improvements in performance using wavepacket analysis , 2002, Medical and Biological Engineering and Computing.
[18] R. Hari,et al. Functional Segregation of Movement-Related Rhythmic Activity in the Human Brain , 1995, NeuroImage.
[19] Gert Pfurtscheller,et al. Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.
[20] Hongming Zhou,et al. Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[21] G. Pfurtscheller,et al. Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.
[22] B. Kamousi,et al. Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[23] G. Pfurtscheller,et al. An SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[24] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .
[25] Yoram Singer,et al. Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..
[26] G. R. Muller,et al. Brain oscillations control hand orthosis in a tetraplegic , 2000, Neuroscience Letters.
[27] Zhi-Zhong Mao,et al. An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace , 2010, IEEE Transactions on Automation Science and Engineering.
[28] Lin Gao,et al. Event-related desynchronization and synchronization quantification in motor-related EEG by Kolmogorov entropy , 2013, Journal of neural engineering.
[29] F Babiloni,et al. Linear classification of low-resolution EEG patterns produced by imagined hand movements. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[30] G. Pfurtscheller,et al. ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia , 2003, Neuroscience Letters.
[31] Klaus-Robert Müller,et al. Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms , 2004, IEEE Transactions on Biomedical Engineering.
[32] Tomaso A. Poggio,et al. Face recognition: component-based versus global approaches , 2003, Comput. Vis. Image Underst..
[33] Klaus-Robert Müller,et al. The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects , 2008, IEEE Transactions on Biomedical Engineering.
[34] Wei He,et al. Performance of Motor Imagery Brain-Computer Interface Based on Anodal Transcranial Direct Current Stimulation Modulation , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[35] Jongin Kim,et al. Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram , 2016, BioMed research international.
[36] G Florian,et al. Dynamic spectral analysis of event-related EEG data. , 1995, Electroencephalography and clinical neurophysiology.
[37] F. L. D. Silva,et al. Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.
[38] Steven Collazos,et al. Classification of Movement and Inhibition Using a Hybrid BCI , 2017, Front. Neurorobot..
[39] G Pfurtscheller,et al. Discrimination between phase-locked and non-phase-locked event-related EEG activity. , 1995, Electroencephalography and clinical neurophysiology.
[40] T. Martin McGinnity,et al. EEG-Based Mobile Robot Control Through an Adaptive Brain–Robot Interface , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[41] Abraham Lempel,et al. On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.