A classification-based method to estimate event-related potentials from single trial EEG

A novel method based on machine learning is developed to estimate event-related potentials from single trial electroencephalography. This paper builds a basic framework using classification and an optimization model based on this framework for estimating event-related potentials. Then the SingleTrialEM algorithm is derived by introducing a logistic regression model, which could be obtained by training before SingleTrialEM is used, to instantiate the optimization model. The simulation tests demonstrate that the proposed method is correct and solid. The advantage of this method is verified by the comparison between this method and the Woody filter in simulation tests. Also, the cognitive test results are consistent with the conclusions of cognitive science.

[1]  Jian Li,et al.  ASEO: A Method for the Simultaneous Estimation of Single-Trial Event-Related Potentials and Ongoing Brain Activities , 2009, IEEE Transactions on Biomedical Engineering.

[2]  S. Bressler,et al.  Trial-to-trial variability of cortical evoked responses: implications for the analysis of functional connectivity , 2002, Clinical Neurophysiology.

[3]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[4]  M. Rugg,et al.  Electrophysiology of Mind: Event-Related Brain Potentials and Cognition , 1995 .

[5]  Dezhong Yao,et al.  Development and evaluation of the sparse decomposition method with mixed over-complete dictionary for evoked potential estimation , 2007, Comput. Biol. Medicine.

[6]  N. Boutros,et al.  P 50 , N 100 , and P 200 sensory gating : Relationships with behavioral inhibition , attention , and working memory , 2010 .

[7]  Mingzhou Ding,et al.  Estimation of single-trial multicomponent ERPs: Differentially variable component analysis (dVCA) , 2003, Biological Cybernetics.

[8]  C. Woody.  Characterization of an adaptive filter for the analysis of variable latency neuroelectric signals , 1967, Medical and biological engineering.

[9]  Gian Domenico Iannetti,et al.  A novel approach for enhancing the signal-to-noise ratio and detecting automatically event-related potentials (ERPs) in single trials , 2010, NeuroImage.

[10]  B. Friedman,et al.  P50 sensory gating and attentional performance. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[11]  R. Quian Quiroga,et al.  Single-trial event-related potentials with wavelet denoising , 2003, Clinical Neurophysiology.

[12]  Hagai Attias,et al.  A Spatiotemporal Framework for Estimating Trial-to-Trial Amplitude Variation in Event-Related MEG/EEG , 2009, IEEE Transactions on Biomedical Engineering.

[13]  J. Leon Kenemans,et al.  Prepulse inhibition and P50 suppression: Commonalities and dissociations , 2006, Psychiatry Research.

[14]  Mingxiong Huang,et al.  Distinct M50 and M100 auditory gating deficits in schizophrenia. , 2005, Psychophysiology.

[15]  T. Gasser,et al.  Trial-to-trial variability of single potentials: methodological concepts and results. , 1987, The International journal of neuroscience.

[16]  T. Gasser,et al.  Variable latencies of noisy signals: Estimation and testing in brain potential data , 1987 .

[17]  Saeid Sanei,et al.  The application of particle filters in single trial event-related potential estimation , 2009, Physiological measurement.

[18]  Hamid Reza Mohseni,et al.  Single trial estimation of event-related potentials using particle filtering , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[19]  Evian Gordon,et al.  Single-event-related potential analysis by means of fragmentary decomposition , 2001, Biological Cybernetics.

[20]  Klaus-Robert Müller,et al.  Enhancing the signal-to-noise ratio of ICA-based extracted ERPs , 2006, IEEE Transactions on Biomedical Engineering.

[21]  R. Coppola,et al.  Signal to noise ratio and response variability measurements in single trial evoked potentials. , 1978, Electroencephalography and clinical neurophysiology.

[22]  José Carlos Príncipe,et al.  A Spatiotemporal Filtering Methodology for Single-Trial ERP Component Estimation , 2009, IEEE Transactions on Biomedical Engineering.

[23]  Giovanni Sparacino,et al.  A Bayesian method to estimate single-trial event-related potentials with application to the study of the P300 variability , 2011, Journal of Neuroscience Methods.

[24]  P. Jaskowski,et al.  Amplitudes and latencies of single-trial ERP's estimated by a maximum-likelihood method , 1999, IEEE Transactions on Biomedical Engineering.

[25]  T. Sejnowski,et al.  Analysis and visualization of single‐trial event‐related potentials , 2001, Human brain mapping.

[26]  M. Kisley,et al.  Comparison of sensory gating to mismatch negativity and self-reported perceptual phenomena in healthy adults. , 2004, Psychophysiology.

[27]  Daniel S. Ruchkin,et al.  Principles of Neurobiological Signal Analysis , 1976 .

[28]  Xingyuan Wang,et al.  Nonlinear dynamic research on EEG signals in HAI experiment , 2009, Appl. Math. Comput..

[29]  N. Boutros,et al.  P50, N100, and P200 sensory gating: relationships with behavioral inhibition, attention, and working memory. , 2009, Psychophysiology.