Cédric Duchêne

Adaptive tracking of EEG oscillations

Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.

Does eye tremor provide the hyperacuity phenomenon

This paper is devoted to a study of the role of the fluctuations that the eye is subject to, from the point of view of noise-enhanced processing. To this end, a basic model of the retina is considered, namely a regular sampler subject to space and time fluctuations that model the random sampling and the involuntary eye tremor respectively. The filtering that can be done by the photoreceptor is also taken into account and the study focuses on a stochastic model of a natural scene. To quantify the effect of the noise, a coefficient of correlation between the signal acquired by a given photoreceptor and a given point of the scene that the eye is looking at is considered. It is shown both for academic examples and for a more realistic case that the fluctuations which affect the retina can induce noise-enhanced processing effects. The observed effect is then interpreted as a stochastic control of the retina via the random tremor.

Adaptive Tracking of EEG Frequency Components

In this chapter, we propose a novel method for tracking oscillatory components in EEG signals by means of an adaptive filter bank. The specific utility of our tracking algorithm is to maximize the oscillatory behavior of its output rather than its spectral power, which shows interesting properties for the observation of neuronal oscillations. In addition, the structure of the filter bank allows for efficiently tracking multiple frequency components perturbed by noise, therefore providing a good framework for EEG spectral analysis. Moreover, our algorithm can be generalized to multivariate data analysis, allowing the simultaneous investigation of several EEG sensors. Thus, a more precise extraction of spectral information can be obtained from the EEG signal under study. After a short introduction, we present our algorithm as well as synthetic examples illustrating its potential. Then, the performance of the method on real EEG signals is presented for the tracking of both a single oscillatory component and multiple components. Finally, future lines of improvement as well as areas of applications are discussed.

Estimation of Atrial Multiple Reentrant Circuits from Surface ECG Signals Based on a Vectorcardiographic Approach

This paper presents a simulation study on the identifiability of multiple reentrant circuits on the basis of the vectorcardiogram. The methods involved include an advanced tracking of the basic frequencies of the dominant rotors and a supporting identification based on the observed loops of their vectorcardiogram. The vector cardiogram was derived from body surface potentials spatially sampled by different lead systems. The results indicate that up to three independent circuits can be identified reliably.

The Role of Atrial Modeling in the Development of ECG Processing Tools

The standard ECG remains the most common non-invasive tool for assessing atrial fibrillation. Specific signal processing techniques have been developed to improve the diagnosis. However, validation of such tools is challenging and comprehensive invasive data may not easily be obtained. To facilitate this task, we developed a computer model of the atria. In this electrophysiological model, atrial fibrillation was simulated and the manifestation of its electrical activity on the thorax was computed. The resulting realistic-looking synthetic ECG signals were used as benchmarks for testing, evaluating and comparing ECG processing techniques such as cancella-tion of the ventricular activity, vectorcardiography and domi-nant frequency analysis.