Single-Trial Analysis

In modern neuroscience, accurate estimations of single-trial parameters in event-related brain responses have been set as a highly desirable goal, since the traditionally used across-trial averaging approach could lead to the loss of the information concerning across-trial variability of both phase-locked ERP and non-phase-locked ERS/ERD responses. In this chapter, we provided the technical details of single-trial analysis methods both in the time domain and the time-frequency domain to enhance the signal-to-noise ratio of event-related brain responses and estimate their single-trial parameters (e.g., latency and amplitude of ERP peaks, as well as latency, frequency, and magnitude of EEG oscillatory features). These methods included probabilistic independent component analysis and common spatial pattern for spatial filtering, continuous wavelet transform for time-frequency filtering, as well as multiple linear regression without/with a dispersion term for feature extraction. Finally, we emphasized the importance of single-trial analysis and discussed its promising applications in basic studies and clinical practice.

[1]  Christopher J. James,et al.  Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis , 2003, IEEE Transactions on Biomedical Engineering.

[2]  A. Engel,et al.  Opinion TRENDS in Cognitive Sciences Vol.10 No.12 Single-trial EEG–fMRI reveals the dynamics of cognitive function , 2022 .

[3]  L. Pelosi,et al.  Working memory impairment in early multiple sclerosis. Evidence from an event-related potential study of patients with clinically isolated myelopathy. , 1997, Brain : a journal of neurology.

[4]  J. P. C. de Weerd,et al.  A posteriori time-varying filtering of averaged evoked potentials , 1981, Biological Cybernetics.

[5]  E. Harth,et al.  Electric Fields of the Brain: The Neurophysics of Eeg , 2005 .

[6]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[7]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals , 2000, Int. J. Neural Syst..

[8]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[9]  C Yiannikas,et al.  Functional prognosis in stroke: use of somatosensory evoked potentials. , 1989, Journal of neurology, neurosurgery, and psychiatry.

[10]  P. Rossini,et al.  Functional source separation from magnetoencephalographic signals , 2006, Human brain mapping.

[11]  Dirk Ostwald,et al.  Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent EEG-fMRI , 2010, NeuroImage.

[12]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[13]  Francesco Bracchi,et al.  Single trial somatosensory evoked potential extraction with ARX filtering for a combined spinal cord intraoperative neuromonitoring technique , 2007, Biomedical engineering online.

[14]  Bettina Sorger,et al.  Improved quality of auditory event-related potentials recorded simultaneously with 3-T fMRI: Removal of the ballistocardiogram artefact , 2007, NeuroImage.

[15]  J. Leong,et al.  Application of time–frequency analysis to somatosensory evoked potential for intraoperative spinal cord monitoring , 2003, Journal of neurology, neurosurgery, and psychiatry.

[16]  André Mouraux,et al.  Single-trial detection of human brain responses evoked by laser activation of Aδ-nociceptors using the wavelet transform of EEG epochs , 2004, Neuroscience Letters.

[17]  Y. Hu,et al.  Effect of stimulus pulse duration on intraoperative somatosensory evoked potential (SEP) monitoring. , 2001, Journal of spinal disorders.

[18]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[19]  L. Zambreanu,et al.  Operculoinsular cortex encodes pain intensity at the earliest stages of cortical processing as indicated by amplitude of laser-evoked potentials in humans , 2005, Neuroscience.

[20]  V. Legrain,et al.  Attentional modulation of the nociceptive processing into the human brain: selective spatial attention, probability of stimulus occurrence, and target detection effects on laser evoked potentials , 2002, PAIN.

[21]  Robert Turner,et al.  A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI , 2000, NeuroImage.

[22]  A. Mouraux,et al.  Nociceptive laser-evoked brain potentials do not reflect nociceptive-specific neural activity. , 2009, Journal of neurophysiology.

[23]  David Friedman,et al.  Single-trial discrimination for integrating simultaneous EEG and fMRI: Identifying cortical areas contributing to trial-to-trial variability in the auditory oddball task , 2009, NeuroImage.

[24]  D. J. Doyle,et al.  Some comments on the use of Wiener filtering for the estimation of evoked potentials. , 1975, Electroencephalography and clinical neurophysiology.

[25]  A. Mouraux,et al.  Determinants of laser-evoked EEG responses: pain perception or stimulus saliency? , 2008, Journal of neurophysiology.

[26]  A. Mouraux,et al.  Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression. , 2011, Journal of neurophysiology.

[27]  K. Yu,et al.  Optimum Filters for Estimating Evoked Potential Waveforms , 1983, IEEE Transactions on Biomedical Engineering.

[28]  Rami K. Niazy,et al.  Removal of FMRI environment artifacts from EEG data using optimal basis sets , 2005, NeuroImage.

[29]  Stephen D. Mayhew,et al.  Automated single-trial measurement of amplitude and latency of laser-evoked potentials (LEPs) using multiple linear regression , 2006, Clinical Neurophysiology.

[30]  M J Aminoff,et al.  Relative utility of different electrophysiologic techniques in the evaluation of brachial plexopathies , 1988, Neurology.

[31]  Matthew T. Sutherland,et al.  Validation of SOBI components from high-density EEG , 2005, NeuroImage.

[32]  Andrzej Cichocki,et al.  Tensor Decompositions: A New Concept in Brain Data Analysis? , 2013, ArXiv.

[33]  Joseph Dien,et al.  Evaluating two-step PCA of ERP data with Geomin, Infomax, Oblimin, Promax, and Varimax rotations. , 2010, Psychophysiology.

[34]  Vedran Deletis,et al.  Neurophysiology in Neurosurgery: A Modern Intraoperative Approach , 2002 .

[35]  W. J. Williams,et al.  Decomposing delta, theta, and alpha time-frequency ERP activity from a visual oddball task using PCA. , 2007, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[36]  Mark W. Woolrich,et al.  Constrained linear basis sets for HRF modelling using Variational Bayes , 2004, NeuroImage.

[37]  A. Engel,et al.  Trial-by-Trial Coupling of Concurrent Electroencephalogram and Functional Magnetic Resonance Imaging Identifies the Dynamics of Performance Monitoring , 2005, The Journal of Neuroscience.

[38]  Dietmar Stolke,et al.  The impact of neurophysiological intraoperative monitoring on surgical decisions: a critical analysis of 423 cases. , 2002, Journal of neurosurgery.

[39]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects , 2008, IEEE Transactions on Biomedical Engineering.

[40]  G. Barbati,et al.  Functional source separation and hand cortical representation for a brain–computer interface feature extraction , 2007, The Journal of physiology.

[41]  Claudia Plant,et al.  Decoding an individual's sensitivity to pain from the multivariate analysis of EEG data. , 2012, Cerebral cortex.

[42]  David A. Leopold,et al.  Single-trial evoked potential estimation using wavelets , 2007, Comput. Biol. Medicine.

[43]  Léon Plaghki,et al.  Nociceptive processing in the human brain of infrequent task-relevant and task-irrelevant noxious stimuli. A study with event-related potentials evoked by CO2 laser radiant heat stimuli , 2003, Pain.

[44]  P. Rossini,et al.  Hand somatosensory subcortical and cortical sources assessed by functional source separation: An EEG study , 2009, Human brain mapping.

[45]  Gian Domenico Iannetti,et al.  Alpha and gamma oscillation amplitudes synergistically predict the perception of forthcoming nociceptive stimuli , 2015, Human brain mapping.

[46]  Z J Koles,et al.  The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. , 1991, Electroencephalography and clinical neurophysiology.

[47]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[48]  M. Richman,et al.  Rotation of principal components , 1986 .

[49]  P. Rossini,et al.  Hand sensory–motor cortical network assessed by functional source separation , 2008, Human brain mapping.

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

[51]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Rami K. Niazy,et al.  Simultaneous recording of laser-evoked brain potentials and continuous, high-field functional magnetic resonance imaging in humans , 2005, NeuroImage.

[53]  Asma Hayati Ahmad,et al.  The role of the thalamus in modulating pain. , 2006, The Malaysian journal of medical sciences : MJMS.

[54]  A. M. Purves,et al.  Time-shifted averaging for laser evoked potentials. , 1993, Electroencephalography and clinical neurophysiology.

[55]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[56]  Z. G. Zhang,et al.  Single-trial detection of somatosensory evoked potentials by probabilistic independent component analysis and wavelet filtering , 2011, Clinical Neurophysiology.

[57]  Wei Lu,et al.  ICA with Reference , 2006, Neurocomputing.

[58]  A Mouraux,et al.  Non-phase locked electroencephalogram (EEG) responses to CO2 laser skin stimulations may reflect central interactions between A∂- and C-fibre afferent volleys , 2003, Clinical Neurophysiology.

[59]  G. Dawson.  A summation technique for the detection of small evoked potentials. , 1954, Electroencephalography and clinical neurophysiology.

[60]  Tom Minka,et al.  Automatic Choice of Dimensionality for PCA , 2000, NIPS.

[61]  C. Tenke,et al.  Optimizing PCA methodology for ERP component identification and measurement: theoretical rationale and empirical evaluation , 2003, Clinical Neurophysiology.

[62]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[63]  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.

[64]  S. Cerutti,et al.  Dynamic time warping in the analysis of event-related potentials , 2005, IEEE Engineering in Medicine and Biology Magazine.

[65]  Michael S. Lazar,et al.  Spatial patterns underlying population differences in the background EEG , 2005, Brain Topography.

[66]  Steve Majerus,et al.  Assessment and detection of pain in noncommunicative severely brain-injured patients , 2010, Expert review of neurotherapeutics.

[67]  M R Nuwer,et al.  Spinal cord monitoring with somatosensory techniques. , 1998, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[68]  I. Jolliffe.  Principal Component Analysis , 2002 .

[69]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[70]  EEG signatures of auditory activity correlate with simultaneously recorded fMRI responses in humans , 2010, NeuroImage.

[71]  R.Quian Quiroga,et al.  Obtaining single stimulus evoked potentials with wavelet denoising , 2000, nlin/0006027.

[72]  A Mouraux,et al.  Across-trial averaging of event-related EEG responses and beyond. , 2008, Magnetic resonance imaging.

[73]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[74]  P. M. Rossini,et al.  Recommendations for the clinical use of somatosensory-evoked potentials , 2008, Clinical Neurophysiology.

[75]  P. Anderson,et al.  Intraoperative neurophysiologic monitoring: focus on cervical myelopathy and related issues. , 2006, The spine journal : official journal of the North American Spine Society.

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

[77]  M. Frot,et al.  Brain generators of laser-evoked potentials: from dipoles to functional significance , 2003, Neurophysiologie Clinique/Clinical Neurophysiology.

[78]  Yong Hu,et al.  Multi-adaptive filtering technique for surface somatosensory evoked potentials processing. , 2005, Medical engineering & physics.

[79]  Gian Domenico Iannetti,et al.  Multiple linear regression to estimate time-frequency electrophysiological responses in single trials , 2015, NeuroImage.

[80]  Gian Domenico Iannetti,et al.  A novel approach to predict subjective pain perception from single-trial laser-evoked potentials , 2013, NeuroImage.

[81]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[82]  Steve Vucic,et al.  Utility of somatosensory evoked potentials in chronic acquired demyelinating neuropathy , 2008, Muscle & nerve.

[83]  Tom Eichele,et al.  Tracking pattern learning with single-trial event-related potentials , 2006, Clinical Neurophysiology.

[84]  G. Tognola,et al.  Wavelet analysis of click-evoked otoacoustic emissions , 1998, IEEE Transactions on Biomedical Engineering.

[85]  I. Tracey,et al.  Similar nociceptive afferents mediate psychophysical and electrophysiological responses to heat stimulation of glabrous and hairy skin in humans , 2006, The Journal of physiology.

[86]  Dawson Gd.  A summation technique for detecting small signals in a large irregular background. , 1951 .

[87]  Andrew P. Bagshaw,et al.  Single trial variability of EEG and fMRI responses to visual stimuli , 2007, NeuroImage.

[88]  William J. Williams,et al.  Decomposing ERP time–frequency energy using PCA , 2005, Clinical Neurophysiology.

[89]  J. P. C. de Weerd,et al.  A posteriori time-varying filtering of averaged evoked potentials , 1981, Biological Cybernetics.

[90]  Hans Knutsson,et al.  Adaptive analysis of fMRI data , 2003, NeuroImage.

[91]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[92]  Franca Tecchio,et al.  Functional source separation applied to induced visual gamma activity , 2008, Human brain mapping.

[93]  Gholam-Ali Hossein-Zadeh,et al.  A signal subspace approach for modeling the hemodynamic response function in fMRI. , 2003, Magnetic resonance imaging.

[94]  De-Shuang Huang,et al.  A New Constrained Independent Component Analysis Method , 2007, IEEE Transactions on Neural Networks.

[95]  Yong Hu,et al.  Adaptive signal enhancement of somatosensory evoked potential for spinal cord compression detection: an experimental study , 2005, Comput. Biol. Medicine.

[96]  Louis Lemieux,et al.  Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction , 1998, NeuroImage.

[97]  A. Mouraux,et al.  Characterizing the Cortical Activity through Which Pain Emerges from Nociception , 2009, The Journal of Neuroscience.