A hybrid ICA-Hermite transform for removal of Ballistocardiogram from EEG

In this paper the problem of removing Ballistocardiogram (BCG) artifact from EEG signal is addressed. BCG removal is an important task in analysis of simultaneous EEG-fMRI data. We propose a new method by combining independent component analysis (ICA) and discrete Hermite transform (DHT) for this purpose. Discrete Hermite transform is a powerful technique which is able to model a signal with no assumption about its shape. This feature makes DHT an appropriate tool to be combined with ICA for removing the BCG artifact. We show that the proposed hybrid ICA-Hermite transform can compensate for the existing drawbacks of the two methods, when applied separately. A significant improvement over conventional methods is demonstrated with synthetic data, and supported by preliminary work with real EEG. © 2012 EURASIP

WOFEX 2021 : 19th annual workshop, Ostrava, 1th September 2021 : proceedings of papers

The workshop WOFEX 2021 (PhD workshop of Faculty of Electrical Engineer-ing and Computer Science) was held on September 1st September 2021 at the VSB – Technical University of Ostrava. The workshop offers an opportunity for students to meet and share their research experiences, to discover commonalities in research and studentship, and to foster a collaborative environment for joint problem solving. PhD students are encouraged to attend in order to ensure a broad, unconfined discussion. In that view, this workshop is intended for students and researchers of this faculty offering opportunities to meet new colleagues.Ostrav

Analysis of Simultaneously Recorded EEG-fMRI by Constrained Source Separation.

In this dissertation novel methods are proposed for analyzing EEG and fMRI. These include an advanced technique developed to integrate these modalities. Majority of the proposed methods in this thesis are based on blind source separation (BSS) concept. Artifact removal is an essential task to prepare EEG signal recorded simultaneously with fMRI for further processing. This is tackled using two new approaches. In the first method, a hybrid independent component analysis (ICA) plus discrete Hermite transform (DHT) is developed. The second method employs a new cost function to perform source extraction based on joint short-and-long term prediction. The main objective of this work is to incorporate the prior information about the temporal structure and periodicity of ballistocardiogram (BCG) artifact into separation procedure. The main objective in fMRI analysis is detection of blood oxygenation level dependent (BOLD). General linear model (GLM) is a widely used technique for this purpose relying only on stimuli onset times and predefined haemodynamic response function (HRF). In this thesis, several BSS methods mainly based on non-negative matrix factorization (NMF) are developed for fMRI analysis. The main superiority of these techniques over GLM is their model-free nature. However, we demonstrate that the performance of these techniques can be improved by exploiting some statistical and physiological prior information. This leads to an advanced approach that does not entirely rely on a predefined model (in contrast to GLM) while taking advantages of all existing information. An important step in our approach for EEG-fMRI fusion is through estimating the fMRI time course using the EEG signals. One approach is by detection of movement onset from brain event-related oscillations. Extracting these oscillations is challenging due to their non-phase-locked nature and inter-trial variability. In this research, a novel method based on linear prediction is proposed to extract rolandic beta rhythm from multi-channel EEG recording. This technique employs a spatio-temporal constraint to effectively extract beta rhythms to study post-movement beta rebound. The results are used to construct a regressor for fMRI analysis in a combined EEG-fMRI paradigm. The last contribution in this thesis is development of a novel technique for EEG-fMRI fusion. This method combines the reconstructed time course using the extracted beta rhythm and fMRI using a partially constrained algorithm. PARAFAC2 is used for this purpose. The obtained results identify the voxels which are involved in post-movement beta rebound due to performing a motor task

Analysis of Simultaneously Recorded EEG-fMRI by Constrained Source Separation.

In this dissertation novel methods are proposed for analyzing EEG and fMRI. These include an advanced technique developed to integrate these modalities. Majority of the proposed methods in this thesis are based on blind source separation (BSS) concept. Artifact removal is an essential task to prepare EEG signal recorded simultaneously with fMRI for further processing. This is tackled using two new approaches. In the first method, a hybrid independent component analysis (ICA) plus discrete Hermite transform (DHT) is developed. The second method employs a new cost function to perform source extraction based on joint short-and-long term prediction. The main objective of this work is to incorporate the prior information about the temporal structure and periodicity of ballistocardiogram (BCG) artifact into separation procedure. The main objective in fMRI analysis is detection of blood oxygenation level dependent (BOLD). General linear model (GLM) is a widely used technique for this purpose relying only on stimuli onset times and predefined haemodynamic response function (HRF). In this thesis, several BSS methods mainly based on non-negative matrix factorization (NMF) are developed for fMRI analysis. The main superiority of these techniques over GLM is their model-free nature. However, we demonstrate that the performance of these techniques can be improved by exploiting some statistical and physiological prior information. This leads to an advanced approach that does not entirely rely on a predefined model (in contrast to GLM) while taking advantages of all existing information. An important step in our approach for EEG-fMRI fusion is through estimating the fMRI time course using the EEG signals. One approach is by detection of movement onset from brain event-related oscillations. Extracting these oscillations is challenging due to their non-phase-locked nature and inter-trial variability. In this research, a novel method based on linear prediction is proposed to extract rolandic beta rhythm from multi-channel EEG recording. This technique employs a spatio-temporal constraint to effectively extract beta rhythms to study post-movement beta rebound. The results are used to construct a regressor for fMRI analysis in a combined EEG-fMRI paradigm. The last contribution in this thesis is development of a novel technique for EEG-fMRI fusion. This method combines the reconstructed time course using the extracted beta rhythm and fMRI using a partially constrained algorithm. PARAFAC2 is used for this purpose. The obtained results identify the voxels which are involved in post-movement beta rebound due to performing a motor task