EEG-based vigilance estimation using extreme learning machines

For many human machine interaction systems, techniques for continuously estimating the vigilance of operators are highly desirable to ensure work safety. Up to now, various signals are studied for vigilance analysis. Among them, electroencephalogram (EEG) is the most commonly used signal. In this paper, extreme learning machine (ELM) and its modifications with L"1 norm and L"2 norm penalties are adopted for EEG-based vigilance estimation. A comparative study on system performance is conducted among ordinary ELM, its modifications, and support vector machines (SVMs). Experimental results show that, compared with SVMs, the ordinary ELM and its modifications can all dramatically speed up the training process while still achieving similar or better vigilance estimation accuracy. In addition, the following three observations have been made from the experiment results: (a) the ordinary ELM and the ELM with L"1 norm penalty (LARS-ELM) are sensitive on the number of hidden nodes; (b) the ELM with L"2 norm penalty (regularized-ELM) and the ELMs with both L"1 norm and L"2 norm penalties (LARS-EN-ELM, TROP-ELM) are stable and insensitive on the number of hidden nodes; and (c) regularized-ELM has a much faster training speed, while LARS-EN-ELM can achieve better vigilance estimation accuracy.

[1]  A. Kok,et al.  The mismatch negativity during natural sleep: intensity deviants , 2000, Clinical Neurophysiology.

[2]  Tzyy-Ping Jung,et al.  Tonic and phasic electroencephalographic dynamics during continuous compensatory tracking , 2008, NeuroImage.

[3]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Richard D. Jones,et al.  EEG-Based Lapse Detection With High Temporal Resolution , 2007, IEEE Transactions on Biomedical Engineering.

[5]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[6]  Glenn Fung,et al.  Proximal support vector machine classifiers , 2001, KDD '01.

[7]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[8]  Tzyy-Ping Jung,et al.  EEG-based drowsiness estimation for safety driving using independent component analysis , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

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

[10]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

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

[12]  Tzyy-Ping Jung,et al.  Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[13]  Matthew B. Weinger.  Vigilance, Boredom, and Sleepiness , 2004, Journal of Clinical Monitoring and Computing.

[14]  T. Sejnowski,et al.  Estimating alertness from the EEG power spectrum , 1997, IEEE Transactions on Biomedical Engineering.

[15]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[16]  Tzyy-Ping Jung,et al.  Estimating level of alertness from EEG , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  W. T. Nelson,et al.  Vigilance , 2013, Royal Society of Health journal.

[18]  S. Makeig,et al.  Lapses in alertness: coherence of fluctuations in performance and EEG spectrum. , 1993, Electroencephalography and clinical neurophysiology.

[19]  Guang-Bin Huang,et al.  Convex incremental extreme learning machine , 2007, Neurocomputing.

[20]  Amaury Lendasse,et al.  A Methodology for Building Regression Models using Extreme Learning Machine: OP-ELM , 2008, ESANN.

[21]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[22]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[23]  Amaury Lendasse,et al.  TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization , 2011, Neurocomputing.

[24]  Qing He,et al.  Extreme Support Vector Machine Classifier , 2008, PAKDD.

[25]  Qinghua Zheng,et al.  Regularized Extreme Learning Machine , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[26]  Tzyy-Ping Jung,et al.  Tonic and phasic EEG and behavioral changes induced by arousing feedback , 2010, NeuroImage.

[27]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[28]  T. Jung,et al.  Changes in alertness are a principal component of variance in the EEG spectrum , 1995, Neuroreport.

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

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

[31]  Bao-Liang Lu,et al.  Off-line and on-line vigilance estimation based on linear dynamical system and manifold learning , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[32]  Raja Parasuraman,et al.  Monitoring an Automated System for a Single Failure: Vigilance and Task Complexity Effects , 1996, Hum. Factors.

[33]  Bao-Liang Lu,et al.  Dynamic clustering for vigilance analysis based on EEG , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.