A new method to detect event-related potentials based on Pearson\u2019s correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience. Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP\u2019s waveform, these waveforms being time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson\u2019s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase\u2014in consonance with the stimuli\u2014in EEG signal correlation over all channels. This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs. These hidden components seem to be caused by variations (between each successive stimulus) of the ERP\u2019s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology. The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language

Topography-Time-Frequency Atomic Decomposition for Event-Related M/EEG Signals.

International audienceWe present a method for decomposing MEG or EEG data (channel x time x trials) into a set of atoms with fixed spatial and time-frequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a pre-processing step before ICA, which gives access to high frequency activity. The time-frequency part is obtained with a novel iterative procedure, which is an extension of the matching pursuit procedure. The method is evaluated on a simulated dataset presenting both low-frequency evoked potentials and high-frequency oscillatory activity. We show that the method is able to recover well both low-frequency and high-frequency simulated activities. There was however cross-talk across some recovered components due to the correlation introduced in the simulation

A Fast and Reliable Method for Simultaneous Waveform, Amplitude and Latency Estimation of Single-Trial EEG/MEG Data

The amplitude and latency of single-trial EEG/MEG signals may provide valuable information concerning human brain functioning. In this article we propose a new method to reliably estimate single-trial amplitude and latency of EEG/MEG signals. The advantages of the method are fourfold. First, no a-priori specified template function is required. Second, the method allows for multiple signals that may vary independently in amplitude and/or latency. Third, the method is less sensitive to noise as it models data with a parsimonious set of basis functions. Finally, the method is very fast since it is based on an iterative linear least squares algorithm. A simulation study shows that the method yields reliable estimates under different levels of latency variation and signal-to-noise ratioÕs. Furthermore, it shows that the existence of multiple signals can be correctly determined. An application to empirical data from a choice reaction time study indicates that the method describes these data accurately

Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification

Objective. The main goal of this work is to develop a model for multi-sensor signals such as MEG or EEG signals, that accounts for the inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI type experiments. Approach. The method involves linear mixed effects statistical model, wavelet transform and spatial filtering, and aims at the characterization of localized discriminant features in multi-sensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e. discriminant) and background noise, using a very simple Gaussian linear mixed model. Main results. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data, in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. Significance. The combination of linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves on earlier results on similar problems, and the three main ingredients all play an important role

Consensus Matching Pursuit of Multi-Trial Biosignals, with Application to Brain Signals

submittedTime-frequency representations are commonly used to analyze the oscillatory nature of bioelectromagnetic signals. There is a growing interest in sparse representations, where the data is described using few components. In this study, we adapt the Matching Pursuit of Mallat and Zhang for biosignals consisting of a series of variations around a similar pattern, with emphasis on multi-trial datasets encountered in MEG and EEG. The general principle of Matching Pursuit (MP) is to iteratively subtract from the signal its projection on the atom selected from a dictionary. The originality of our method is to select each atom using a voting technique that is robust to variability, and to subtract it by adapting the parameters to each trial. Because it is designed to handle inter-trial variability using a voting technique, the method is called Consensus Matching Pursuit (CMP). The method is validated on both simplified and realistic simulations, and on two real datasets (intracerebral EEG and scalp EEG ).We also compare our method to two other multi-trial MP algorithms: Multivariate MP (MMP) and Induced activity MP (IMP). CMP is shown to be able to sparsely reveal the structure present in the data, and to be robust to variability (jitter) across trials

Functional mixed-effect models for electrophysiological responses

In electro/psychophysiological experiments, linear mixed-effect modeling is an effective statistical technique for data repeatedly observed from the same experimental participants or stimulus items. This review describes the application of mixed-ef fect modeling to functional responses, in particular those observed in event-related EEG or MEG experiments, using a discrete wavelet transform. The technique is illustrated with a design with several covariates, and procedures for generating posterior samples and computing a Bayesian false discovery rate are described.Лінійне моделювання з урахуванням змішаних ефектів (mixed-effect modeling, MEM) є корисною статистичною методикою при інтерпретації даних, які повторно реєструються в умовах тестування одного й того самого учасника дослідження або використання ідентичного порядку стимулів у електро-/психофізіологічних експериментах. В огляді описано використання МЕМ при аналізі функціональних відповідей (зокрема, пов’язаних з подією феноменів, що спостерігалися в електро- або магнітоенцефалографічних дослідженнях) із застосуванням дискретного вейвлет-перетворення

Efficient Acquisition and Denoising of Full-Range Event-Related Potentials Following Transient Stimulation of the Auditory Pathway

This body of work relates to recent advances in the field of human auditory event-related potentials (ERP), specifically the fast, deconvolution-based ERP acquisition as well as single-response based preprocessing, denoising and subsequent analysis methods. Its goal is the contribution of a cohesive set of methods facilitating the fast, reliable acquisition of the whole electrophysiological response generated by the auditory pathway from the brainstem to the cortex following transient acoustical stimulation. The present manuscript is divided into three sequential areas of investigation : First, the general feasibility of simultaneously acquiring auditory brainstem, middle-latency and late ERP single responses is demonstrated using recordings from 15 normal hearing subjects. Favourable acquisition parameters (i.e., sampling rate, bandpass filter settings and interstimulus intervals) are established, followed by signal analysis of the resulting ERP in terms of their dominant intrinsic scales to determine the properties of an optimal signal representation with maximally reduced sample count by means of nonlinear resampling on a logarithmic timebase. This way, a compression ratio of 16.59 is achieved. Time-scale analysis of the linear-time and logarithmic-time ERP single responses is employed to demonstrate that no important information is lost during compressive resampling, which is additionally supported by a comparative evaluation of the resulting average waveforms - here, all prominent waves remain visible, with their characteristic latencies and amplitudes remaining essentially unaffected by the resampling process. The linear-time and resampled logarithmic-time signal representations are comparatively investigated regarding their susceptibility to the types of physiological and technical noise frequently contaminating ERP recordings. While in principle there already exists a plethora of well-investigated approaches towards the denoising of ERP single-response representations to improve signal quality and/or reduce necessary aquisition times, the substantially altered noise characteristics of the obtained, resampled logarithmic-time single response representations as opposed to their linear-time equivalent necessitates a reevaluation of the available methods on this type of data. Additionally, two novel, efficient denoising algorithms based on transform coefficient manipulation in the sinogram domain and on an analytic, discrete wavelet filterbank are proposed and subjected to a comparative performance evaluation together with two established denoising methods. To facilitate a thorough comparison, the real-world ERP dataset obtained in the first part of this work is employed alongside synthetic data generated using a phenomenological ERP model evaluated at different signal-to-noise ratios (SNR), with individual gains in multiple outcome metrics being used to objectively assess algorithm performances. Results suggest the proposed denoising algorithms to substantially outperform the state-of-the-art methods in terms of the employed outcome metrics as well as their respective processing times. Furthermore, an efficient stimulus sequence optimization method for use with deconvolution-based ERP acquisition methods is introduced, which achieves consistent noise attenuation within a broad designated frequency range. A novel stimulus presentation paradigm for the fast, interleaved acquisition of auditory brainstem, middle-latency and late responses featuring alternating periods of optimized, high-rate deconvolution sequences and subsequent low-rate stimulation is proposed and investigated in 20 normal hearing subjects. Deconvolved sequence responses containing early and middle-latency ERP components are fused with subsequent late responses using a time-frequency resolved weighted averaging method based on cross-trial regularity, yielding a uniform SNR of the full-range auditory ERP across investigated timescales. Obtained average ERP waveforms exhibit morphologies consistent with both literature values and the reference recordings obtained in the first part of this manuscript, with all prominent waves being visible in the grand average waveforms. The novel stimulation approach cuts acquisition time by a factor of 3.4 while at the same time yielding a substantial gain in the SNR of obtained ERP data. Results suggest the proposed interleaved stimulus presentation and associated postprocessing methodology to be suitable for the fast, reliable extraction of full-range neural correlates of auditory processing in future studies.Diese Arbeit steht im Zusammenhang mit aktuellen Entwicklungen auf dem Gebiet der ereigniskorrelierten Potentiale (EKP) des humanen auditorischen Systems, insbesondere der schnellen, entfaltungsbasierten EKP-Aufzeichnung sowie einzelantwortbasierten Vorverarbeitungs-, Entrauschungs- und nachgelagerten Analysemethoden. Ziel ist die Bereitstellung eines vollständigen Methodensatzes, der eine schnelle, zuverlässige Erfassung der gesamten elektrophysiologischen Aktivität entlang der Hörbahn vom Hirnstamm bis zum Cortex ermöglicht, die als Folge transienter akustischer Stimulation auftritt. Das vorliegende Manuskript gliedert sich in drei aufeinander aufbauende Untersuchungsbereiche : Zunächst wird die generelle Machbarkeit der gleichzeitigen Aufzeichnung von Einzelantworten der auditorischen Hirnstammpotentiale zusammen mit mittelspäten und späten EKP anhand von Referenzmessungen an 15 normalhörenden Probanden demonstriert. Es werden hierzu geeignete Erfassungsparameter (Abtastrate, Bandpassfiltereinstellungen und Interstimulusintervalle) ermittelt, gefolgt von einer Signalanalyse der resultierenden EKP im Hinblick auf deren dominante intrinsische Skalen, um auf dieser Grundlage die Eigenschaften einer optimalen Signaldarstellung mit maximal reduzierter Anzahl an Abtastpunkten zu bestimmen, die durch nichtlineare Neuabtastung auf eine logarithmische Zeitbasis realisiert wird. Hierbei wird ein Kompressionsverhältnis von 16.59 erzielt. Zeit-Skalen-Analysen der uniform und logarithmisch abgetasteten EKP-Einzelantworten zeigen, dass bei der kompressiven Neuabtastung keine relevante Information verloren geht, was durch eine vergleichende Auswertung der resultierenden, gemittelten Wellenformen zusätzlich gestützt wird - alle prominenten Wellen bleiben sichtbar und sind hinsichtlich ihrer charakteristischen Latenzen und Amplituden von der Neuabtastung weitgehend unbeeinflusst. Die uniforme und logarithmische Signalrepräsentation werden hinsichtlich ihrer Anfälligkeit für die üblicherweise bei der EKP-Aufzeichnung auftretenden physiologischen und technischen Störquellen vergleichend untersucht. Obwohl bereits eine Fülle von gut etablierten Ansätzen für die Entrauschung von EKP-Einzelantwortdarstellungen zur Verbesserung der Signalqualität und/oder zur Reduktion der benötigten Erfassungszeiten existiert, erfordern die wesentlich veränderten Störeigenschaften der vorliegenden, logarithmisch abgetasteten Einzelantwortdarstellungen im Gegensatz zu ihrem uniformen Äquivalent eine Neubewertung der verfügbaren Methoden für diese Art von Daten. Darüber hinaus werden zwei neuartige, effiziente Entrauschungsalgorithmen geboten, die auf der Koeffizientenmanipulation einer Sinogramm-Repräsentation bzw. einer analytischen, diskreten Wavelet-Zerlegung der Einzelantworten basieren und gemeinsam mit zwei etablierten Entrauschungsmethoden einer vergleichenden Leistungsbewertung unterzogen werden. Um einen umfassenden Vergleich zu ermöglichen, werden der im ersten Teil dieser Arbeit erhaltene EKP-Messdatensatz sowie synthetischen Daten eingesetzt, die mithilfe eines phänomenologischen EKP-Modells bei verschiedenen Signal-Rausch-Abständen (SRA) erzeugt wurden, wobei die individuellen Anstiege in mehreren Zielmetriken zur objektiven Bewertung der Performanz herangezogen werden. Die erhaltenen Ergebnisse deuten darauf hin, dass die vorgeschlagenen Entrauschungsalgorithmen die etablierten Methoden sowohl in den eingesetzten Zielmetriken als auch mit Blick auf die Laufzeiten deutlich übertreffen. Weiterhin wird ein effizientes Reizsequenzoptimierungsverfahren für den Einsatz mit entfaltungsbasierten EKP-Aufzeichnungsmethoden vorgestellt, das eine konsistente Rauschunterdrückung innerhalb eines breiten Frequenzbands erreicht. Ein neuartiges Stimulus-Präsentationsparadigma für die schnelle, verschachtelte Erfassung auditorischer Hirnstammpotentiale, mittlelspäter und später Antworten durch alternierende Darbietung von optimierten, dichter Stimulussequenzen und nachgelagerter, langsamer Einzelstimulation wird eingeführt und in 20 normalhörenden Probanden evaluiert. Entfaltete Sequenzantworten, die frühe und mittlere EKP enthalten, werden mit den nachfolgenden späten Antworten fusioniert, wobei eine Zeit-Frequenz-aufgelöste, gewichtete Mittelung unter Berücksichtigung von Regularität über Einzelantworten hinweg zum Einsatz kommt. Diese erreicht einheitliche SRA der resultierenden EKP-Signale über alle untersuchten Zeitskalen hinweg. Die erhaltenen, gemittelten EKP-Wellenformen weisen Morphologien auf, die sowohl mit einschlägigen Literaturwerten als auch mit den im ersten Teil dieses Manuskripts erhaltenen Referenzaufnahmen konsistent sind, wobei alle markanten Wellen deutlich in den Gesamtmittelwerten sichtbar sind. Das neuartige Stimulationsparadigma verkürzt die Erfassungszeit um den Faktor 3.4 und vergrößert gleichzeitig den erreichten SRA erheblich. Die Ergebnisse deuten darauf hin, dass die vorgeschlagene verschachtelte Stimuluspräsentation und die nachgelagerte EKP-Verarbeitungsmethodik zur schnellen, zuverlässigen Extraktion neuronaler Korrelate der gesamten auditorischen Verarbeitung im Rahmen zukünftiger Studien geeignet sind.Bundesministerium für Bildung und Forschung | Bimodal Fusion - Eine neurotechnologische Optimierungsarchitektur für integrierte bimodale Hörsysteme | 2016-201

Single-trial evoked potential estimation using wavelets

In this paper we present conventional and translation-invariant (TI) wavelet-based approaches for single-trial evoked potential estimation based on intracortical recordings. We demonstrate that the wavelet-based approaches outperform several existing methods including the Wiener filter, least mean square (LMS), and recursive least squares (RLS), and that the TI wavelet-based estimates have higher SNR and lower RMSE than the conventional wavelet-based estimates. We also show that multichannel averaging significantly improves the evoked potential estimation, especially for the wavelet-based approaches. The excellent performances of the wavelet-based approaches for extracting evoked potentials are demonstrated via examples using simulated and experimental data