François-Benoît Vialatte

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG.

It is well known that EEG signals of Alzheimers disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as mild cognitive impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish MCI patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD.

Techniques for early detection of Alzheimer's disease using spontaneous EEG recordings

Alzheimers disease (AD) is a degenerative disease which causes serious cognitive decline. Studies suggest that effective treatments for AD may be aided by the detection of the disease in its early stages, prior to extensive neuronal degeneration. In this paper, we propose a set of novel techniques which could help to perform this task, and present the results of experiments conducted to evaluate these approaches. The challenge is to discriminate between spontaneous EEG recordings from two groups of subjects: one afflicted with mild cognitive impairment and eventual AD and the other an age-matched control group. The classification results obtained indicate that the proposed methods are promising additions to the existing tools for detection of AD, though further research and experimentation with larger datasets is required to verify their effectiveness.

EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts

EEG recordings are usually corrupted by spurious extra-cerebral artifacts, which should be rejected or cleaned up by the practitioner. Since manual screening of human EEGs is inherently error prone and might induce experimental bias, automatic artifact detection is an issue of importance. Automatic artifact detection is the best guarantee for objective and clean results. We present a new approach, based on the time-frequency shape of muscular artifacts, to achieve reliable and automatic scoring. The impact of muscular activity on the signal can be evaluated using this methodology by placing emphasis on the analysis of EEG activity. The method is used to discriminate evoked potentials from several types of recorded muscular artifacts-with a sensitivity of 98.8% and a specificity of 92.2%. Automatic cleaning of EEG data is then successfully realized using this method, combined with independent component analysis. The outcome of the automatic cleaning is then compared with the Slepian multitaper spectrum based technique introduced by Delorme et al (2007 Neuroimage 34 1443-9).

Split-test Bonferroni correction for QEEG statistical maps

With statistical testing, corrections for multiple comparisons, such as Bonferroni adjustments, have given rise to controversies in the scientific community, because of their negative impact on statistical power. This impact is especially problematic for high-multidimensional data, such as multi-electrode brain recordings. With brain imaging data, a reliable method is needed to assess statistical significance of the data without losing statistical power. Conjunction analysis allows the combination of significance and consistency of an effect. Through a balanced combination of information from retest experiments (multiple trials split testing), we present an intuitively appealing, novel approach for brain imaging conjunction. The method is then tested and validated on synthetic data followed by a real-world test on QEEG data from patients with Alzheimer’s disease. This latter application requires both reliable type-I error and type-II error rates, because of the poor signal-to-noise ratio inherent in EEG signals.

On the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review

In recent years, various computational approaches have been proposed to diagnose Alzheimer’s disease (AD) from EEG recordings. In this paper, we review some of those approaches, and discuss their limitations and potential.

Statistical modeling and analysis of laser-evoked potentials of electrocorticogram recordings from awake humans

This article is devoted to statistical modeling and analysis of electrocorticogram (ECoG) signals induced by painful cutaneous laser stimuli, which were recorded from implanted electrodes in awake humans. Specifically, with statistical tools of factor analysis and independent component analysis, the pain-induced laser-evoked potentials (LEPs) were extracted and investigated under different controlled conditions. With the help of wavelet analysis, quantitative and qualitative analyses were conducted regarding the LEPs attributes of power, amplitude, and latency, in both averaging and single-trial experiments. Statistical hypothesis tests were also applied in various experimental setups. Experimental results reported herein also confirm previous findings in the neurophysiology literature. In addition, single-trial analysis has also revealed many new observations that might be interesting to the neuroscientists or clinical neurophysiologists. These promising results show convincing validation that advanced signal processing and statistical analysis may open new avenues for future studies of such ECoG or other relevant biomedical recordings.

Quantifying statistical interdependence, part iii: N > 2 point processes

Stochastic event synchrony (SES) is a recently proposed family of similarity measures. First, “events” are extracted from the given signals; next, one tries to align events across the different time series. The better the alignment, the more similar the N time series are considered to be. The similarity measures quantify the reliability of the events (the fraction of “nonaligned” events) and the timing precision. So far, SES has been developed for pairs of one-dimensional (Part I) and multidimensional (Part II) point processes. In this letter (Part III), SES is extended from pairs of signals to N > 2 signals. The alignment and SES parameters are again determined through statistical inference, more specifically, by alternating two steps: (1) estimating the SES parameters from a given alignment and (2), with the resulting estimates, refining the alignment. The SES parameters are computed by maximum a posteriori (MAP) estimation (step 1), in analogy to the pairwise case. The alignment (stepxa02) is solved by linear integer programming. In order to test the robustness and reliability of the proposed N-variate SES method, it is first applied to synthetic data. We show that N-variate SES results in more reliable estimates than bivariate SES. Next N-variate SES is applied to two problems in neuroscience: to quantify the firing reliability of Morris-Lecar neurons and to detect anomalies in EEG synchrony of patients with mild cognitive impairment. Those problems were also considered in Parts I and II, respectively. In both cases, the N-variate SES approach yields a more detailed analysis.

Modeling Transient Oscillations in the EEG of Patients with Mild Cognitive Impairment

We explore the potential of transient local synchrony in EEG, as a marker for MCI (mild cognitive impairment). EEG signals of patients with MCI are transformed to a wavelet time-frequency representation, and afterwards a sparsification process (bump modeling) extracts time-frequency oscillatory bursts. We observed that organized oscillatory events contain stronger discriminative signatures than averaged spectral EEG statistics for patients in a probable early stage of Alzheimer’s disease. Specifically, bump modeling enhanced the difference between MCI patients and age-matched control subjects in the θ and high β frequency ranges. This effect is consistent with previous results obtained on other databases.

Analysis of EEG Time Series Recorded from Alzheimer Patients Based on Their Spectral Content

In this paper, EEG recordings on each channel are seen as non stationary time series. We assume a piecewise stationary model for the signal where the changes affect the power spectrum. Such a model is particularly relevant for analyzing Alzheimer disease (AD) dataset as the disease leads to variation of the frequency rhythms. Our method is devoted to the estimation of the partition. We define a criterion based on a regularized periodogram to estimate the spectrum and on the power spectrum on predefined frequency bands to estimate the change points. The method produces new markers designed to be used for the discrimination between control and AD patients.