Classification of motor imagery electroencephalography signals using spiking neurons with different input encoding strategies

[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 .