Classification of motor imagery electroencephalography signals using spiking neurons with different input encoding strategies
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[1] Maryam Gholami Doborjeh,et al. Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications , 2016, Neural Networks.
[2] Beatriz A. Garro,et al. Classification of DNA microarrays using artificial neural networks and ABC algorithm , 2016, Appl. Soft Comput..
[3] Saeid Nahavandi,et al. Fuzzy system with tabu search learning for classification of motor imagery data , 2015, Biomed. Signal Process. Control..
[4] Beatriz A. Garro,et al. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms , 2015, Comput. Intell. Neurosci..
[5] Nikola K. Kasabov,et al. Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes , 2015, Inf. Sci..
[6] Beatriz A. Garro,et al. Training Spiking Neural Models Using Artificial Bee Colony , 2015, Comput. Intell. Neurosci..
[7] Mark D. Huffman,et al. AHA Statistical Update Heart Disease and Stroke Statistics — 2012 Update A Report From the American Heart Association WRITING GROUP MEMBERS , 2010 .
[8] Roberto Antonio Vázquez,et al. Tuning the parameters of an integrate and fire neuron via a genetic algorithm for solving pattern recognition problems , 2015, Neurocomputing.
[9] Brendan Z. Allison,et al. How Many People Can Use a BCI System , 2015 .
[10] Saeid Nahavandi,et al. EEG data classification using wavelet features selected by Wilcoxon statistics , 2014, Neural Computing and Applications.
[11] Jessica Cantillo-Negrete,et al. An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender , 2014, BioMedical Engineering OnLine.
[12] Sarah N. Kraeutner,et al. Motor imagery-based brain activity parallels that of motor execution: Evidence from magnetic source imaging of cortical oscillations , 2014, Brain Research.
[13] Ammar Belatreche,et al. An online supervised learning method for spiking neural networks with adaptive structure , 2014, Neurocomputing.
[14] Kay Chen Tan,et al. A brain-inspired spiking neural network model with temporal encoding and learning , 2014, Neurocomputing.
[15] J. Cantillo-Negrete,et al. [Characterization of electrical brain activity related to hand motor imagery in healthy subjects]. , 2014, Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion.
[16] Nikola K. Kasabov,et al. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data , 2014, Neural Networks.
[17] D. E. Viñas,et al. Caracterización de la actividad eléctrica cerebral relacionada con la imaginación del movimiento de la mano en sujetos sanos , 2014 .
[18] Jin Hu,et al. NeuCubeRehab: A Pilot Study for EEG Classification in Rehabilitation Practice Based on Spiking Neural Networks , 2013, ICONIP.
[19] Jing Yang,et al. A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks , 2013, Neural Networks.
[20] R. Ebrahimpour,et al. Multiple classifier system for EEG signal classification with application to brain–computer interfaces , 2013, Neural Computing and Applications.
[21] Stylianos Kampakis,et al. Improved Izhikevich neurons for spiking neural networks , 2012, Soft Comput..
[22] Pedro J. García-Laencina,et al. Automatic and Adaptive Classification of Electroencephalographic Signals for Brain Computer Interfaces , 2012, Journal of Medical Systems.
[23] Myoungho Lee,et al. Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects , 2011, Biomedical engineering online.
[24] Li Yao,et al. Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data , 2011, PloS one.
[25] Suzanne Kieffer,et al. Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry , 2011, Biomedical engineering online.
[26] Robert Oostenveld,et al. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..
[27] Yasuharu Koike,et al. A real-time BCI with a small number of channels based on CSP , 2011, Neural Computing and Applications.
[28] Andrzej J. Kasinski,et al. Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting , 2010, Neural Computation.
[29] Vera Kaiser,et al. Fast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG , 2010, Medical & Biological Engineering & Computing.
[30] Catalina Llanos,et al. The kinematics of motor imagery: Comparing the dynamics of real and virtual movements , 2009, Neuropsychologia.
[31] M. Carrillo-de-la-Peña,et al. Equivalent is not equal: Primary motor cortex (MI) activation during motor imagery and execution of sequential movements , 2008, Brain Research.
[32] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[33] Clemens Brunner,et al. Better than random? A closer look on BCI results , 2008 .
[34] Eugene M. Izhikevich,et al. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting , 2006 .
[35] H. Sompolinsky,et al. The tempotron: a neuron that learns spike timing–based decisions , 2006, Nature Neuroscience.
[36] Eugene M. Izhikevich,et al. Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.
[37] Wulfram Gerstner,et al. Spiking Neuron Models: Formal spiking neuron models , 2002 .
[38] Sander M. Bohte,et al. Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.
[39] Gert Pfurtscheller,et al. Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.
[40] F. L. D. Silva,et al. Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.
[41] Yue Shi,et al. A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).
[42] A. Roli. Artificial Neural Networks , 2012, Lecture Notes in Computer Science.
[43] A. Hodgkin,et al. A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990, Bulletin of mathematical biology.
[44] B. Hjorth. An on-line transformation of EEG scalp potentials into orthogonal source derivations. , 1975, Electroencephalography and clinical neurophysiology.
[45] Robert H. Riffenburgh,et al. Linear Discriminant Analysis , 1960 .