Panagiotis Doulgeris

Separation of blended data by iterative estimation and subtraction of blending interference noise

Seismic acquisition is a trade-off between economy and quality. In conventional acquisition the time intervals between successive records are large enough to avoid interference in time. To obtain an efficient survey, the spatial source sampling is therefore often (too) large. However, in blending, or simultaneous acquisition, temporal overlap between shot records is allowed. This additional degree of freedom in survey design significantly improves the quality or the economics or both. Deblending is the procedure of recovering the data as if they were acquired in the conventional, unblended way. A simple least-squares procedure, however, does not remove the interference due to other sources, or blending noise. Fortunately, the character of this noise is different in different domains, e.g., it is coherent in the common source domain, but incoherent in the common receiver domain. This property is used to obtain a considerable improvement. We propose to estimate the blending noise and subtract it from the blended data. The estimate does not need to be perfect because our procedure is iterative. Starting with the least-squares deblended data, the estimate of the blending noise is obtained via the following steps: sort the data to a domain where the blending noise is incoherent; apply a noise suppression filter; apply a threshold to remove the remaining noise, ending up with (part of) the signal; compute an estimate of the blending noise from this signal. At each iteration, the threshold can be lowered and more of the signal is recovered. Promising results were obtained with a simple implementation of this method for both impulsive and vibratory sources. Undoubtedly, in the future algorithms will be developed for the direct processing of blended data. However, currently a high-quality deblending procedure is an important step allowing the application of contemporary processing flows

Sensorimotor cortical response during motion reflecting audiovisual stimulation: evidence from fractal EEG analysis

Sensorimotor activity in response to motion reflecting audiovisual titillation is studied in this article. EEG recordings, and especially the Mu-rhythm over the sensorimotor cortex (C3, CZ, and C4 electrodes), were acquired and explored. An experiment was designed to provide auditory (Modest Mussorgsky’s “Promenade” theme) and visual (synchronized human figure walking) stimuli to advanced music students (AMS) and non-musicians (NM) as a control subject group. EEG signals were analyzed using fractal dimension (FD) estimation (Higuchi’s, Katz’s and Petrosian’s algorithms) and statistical methods. Experimental results from the midline electrode (CZ) based on the Higuchi method showed significant differences between the AMS and the NM groups, with the former displaying substantial sensorimotor response during auditory stimulation and stronger correlation with the acoustic stimulus than the latter. This observation was linked to mirror neuron system activity, a neurological mechanism that allows trained musicians to detect action-related meanings underlying the structural patterns in musical excerpts. Contrarily, the response of AMS and NM converged during audiovisual stimulation due to the dominant presence of human-like motion in the visual stimulus. These findings shed light upon music perception aspects, exhibiting the potential of FD to respond to different states of cortical activity.

Separation of blended data by iterative estimation and subtraction of interference noise

Conventional data acquisition practice dictates the existence of sufficient time intervals between the firing of successive sources in the field. However, much attention has been drawn recently to the possibility of shooting in an overlapping fashion. Numerous publications have addressed the issue from different scopes (denoising, compressing, blind signal separation etc.) while others have defined the theoretical background. The term ‘blending’ was introduced to describe this new trend in acquisition designs, the time-overlapping data acquisition. In turn, the term ‘deblending’ refers to an algorithm that recovers the data as if they were shot in the conventional way. Such an algorithm is presented in this chapter for application on both impulsive and vibrating sources. This algorithm is based on iterative interference estimation and subtraction and is applied to field data.

Monitoring of Musical ‘Motion’ in EEG Using Bispectral Analysis: A Mirror Neurons-based Approach

‘Motion’, as expressed through high-level features of music, combined with mirror neuron (MN) system activation is studied in this article. The mechanism of MN involved in the perception of musical structures is seen as a means for cueing the learner on ‘known’ factors that can be used for his/her knowledge scaffolding. To explore such relationships, EEG recordings, and especially the Mu-rhythm over the sensorimotor cortex that relates to the activation of MN, were acquired and explored. An experiment was designed to provide the auditory and visual stimuli to two groups of subjects, advanced music students and non-musicians as a control subject group. The musician group’s response to ‘motion’, implemented by Modest Mussorgsky’s ‘Promenade’ and a corresponding video clip, was monitored. The acquired signals, after appropriate averaging in the time domain, were analyzed in the bifrequency domain, using bispectral analysis. Experimental results showed that motion inherent in high-level features of music, could be associated with Mu-rhythm modulation. Such modulation provoked by the MNs could cause bispectral fluctuations, especially when visual stimulation is combined with an auditory one. These results pave the way for further exploitation of the role of MNs in music and, in general, knowledge perception.

Analysis of a Coherency-constrained Inversion for the Separation of Blended Data: Discovering the Leakage Subspace

So-called blended or simultaneous source techniques are interesting because they offer the potential for better acquisition sampling, improved illumination and greatly enhanced computational efficiency. The procedure of retrieving data as if they were acquired in the conventional way is called deblending. Several inversion techniques have been proposed for solving this ill-posed problem with the iterative estimation and subtraction algorithm being one of them. This particular method uses a dedicated iteration that integrates a coherency-pass filter. However, fundamental questions regarding the convergence and limitations of this method have not previously been addressed. The notion of the leakage subspace is hereby introduced to aid in this quest.

Music Perception as Reflected in Bispectral EEG Analysis under a Mirror Neurons-Based Approach

One important goal of many intelligent interactive systems is dynamic personalization and adaptivity to users. ‘Motion’ and intention that are involved in the individual perception of musical structure combined with mirror neuron (MN) system activation are studied in this article. The mechanism of MN involved in the perception of musical structures is seen as a means for cueing the learner on ‘known’ factors that can be used for his/her knowledge scaffolding. To explore such relationships, EEG recordings, and especially the Mu-rhythm in the premotor cortex that relates to the activation of MN, were acquired and explored. Three experiments were designed to provide the auditory and visual stimuli to a group of subjects, including both musicians and non-musicians. The acquired signals, after appropriate averaging in the time domain, were analysed in frequency and bifrequency domains, using spectral and bispectral analysis, respectively. Experimental results have shown that an intention-related activity shown in musicians could be associated with Mu-rhythm suppression. Moreover, an underlying ongoing function appearing in the transition from heard sound to imagined sound could be revealed in the bispectrum domain and a Mu-rhythm modulation provoked by the MNs could cause bispectral fluctuations, especially when visual stimulation is combined with an auditory one for the case of musicians. These results pave the way for transferring the research in the area of blind or visually impaired people, where hearing is the main information sensing tool.