Classification of BMI control commands from rat's neural signals using extreme learning machine

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.

[1]  G. Pfurtscheller,et al.  Electrocorticogram as the basis for a direct brain interface: Opportunities for improved detection accuracy , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[2]  Michael J. Black,et al.  Modeling and decoding motor cortical activity using a switching Kalman filter , 2004, IEEE Transactions on Biomedical Engineering.

[3]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[4]  Erich E. Sutter,et al.  The brain response interface: communication through visually-induced electrical brain responses , 1992 .

[5]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[6]  Narasimhan Sundararajan,et al.  Classification of Mental Tasks from Eeg Signals Using Extreme Learning Machine , 2006, Int. J. Neural Syst..

[7]  Wooyoung Jung,et al.  AUTOSAR migration from existing automotive software , 2008, 2008 International Conference on Control, Automation and Systems.

[8]  Gert Pfurtscheller,et al.  Brain-computer interface: a new communication device for handicapped persons , 1993 .

[9]  Myoungho Lee,et al.  Algorithm for Classifying Arrhythmia using Extreme Learning Machine and Principal Component Analysis , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Qinyu Zhang,et al.  Additive and Multiplicative Noise Reduction by Back Propagation Neural Network , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Dennis J. McFarland,et al.  An EEG-based method for graded cursor control , 1993, Psychobiology.

[12]  J. Chapin.  Using multi-neuron population recordings for neural prosthetics , 2004, Nature Neuroscience.

[13]  Chee Kheong Siew,et al.  Real-time learning capability of neural networks , 2006, IEEE Trans. Neural Networks.

[14]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[15]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[16]  Sean M Montgomery,et al.  Integration and Segregation of Activity in Entorhinal-Hippocampal Subregions by Neocortical Slow Oscillations , 2006, Neuron.

[17]  Farzad Rajaei Salmasi,et al.  Design and Optimization of Parallel Hybrid Electric Vehicle (PHEV) Drivetrain based on Mechatronics Design Approach , 2008 .

[18]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[19]  Yonggwan Won,et al.  An Improvement of Extreme Learning Machine for Compact Single-Hidden-Layer Feedforward Neural Networks , 2008, Int. J. Neural Syst..

[20]  S. Meagher.  Instant neural control of a movement signal , 2002 .

[21]  Wei Sun,et al.  A Semisupervised Support Vector Machines Algorithm for BCI Systems , 2007, Comput. Intell. Neurosci..

[22]  Narasimhan Sundararajan,et al.  Fully complex extreme learning machine , 2005, Neurocomputing.

[23]  Hyung-Cheul Shin,et al.  Development of intracranial brain-computer interface system using non-motor brain area for series of motor functions , 2006 .

[24]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[25]  P. Nunez.  Toward a quantitative description of large-scale neocortical dynamic function and EEG , 2000, Behavioral and Brain Sciences.

[26]  Miguel A. L. Nicolelis,et al.  Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex , 1999, Nature Neuroscience.

[27]  P. Somogyi,et al.  Neuronal Diversity and Temporal Dynamics: The Unity of Hippocampal Circuit Operations , 2008, Science.