Single Shot Reversible GAN for BCG artifact removal in simultaneous EEG-fMRI

Simultaneous EEG-fMRI acquisition and analysis technology has been widely used in various research fields of brain science. However, how to remove the ballistocardiogram (BCG) artifacts in this scenario remains a huge challenge. Because it is impossible to obtain clean and BCG-contaminated EEG signals at the same time, BCG artifact removal is a typical unpaired signal-to-signal problem. To solve this problem, this paper proposed a new GAN training model - Single Shot Reversible GAN (SSRGAN). The model is allowing bidirectional input to better combine the characteristics of the two types of signals, instead of using two independent models for bidirectional conversion as in the past. Furthermore, the model is decomposed into multiple independent convolutional blocks with specific functions. Through additional training of the blocks, the local representation ability of the model is improved, thereby improving the overall model performance. Experimental results show that, compared with existing methods, the method proposed in this paper can remove BCG artifacts more effectively and retain the useful EEG information.Comment: 8 pages, 5 figures, 1 tabl

Advancing Multimodal Approaches to Study Human Brain: Improvements in Simultaneous EEG-fMRI Acquisition

The primary aim of the study detailed in this dissertation was improving the quality of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) experiments. Two common challenges to use concurrent EEG-fMRI tests are addressed herein. The first is the presence of EEG artifacts during simultaneous EEG-fMRI, which require more consideration than EEG data recorded outside the scanner. To mitigate this issue, a fully automated artifact correction pipeline was developed. In the proposed pipeline, magnetic resonance (MR) environmental (i.e., gradient and ballistocardiogram [BCG]) artifacts were reduced using optimal basis sets (OBS) and average artifact subtraction (AAS). Subsequently, independent component analysis (ICA) was leveraged for reducing physiological artifacts (e.g., eye blinks, saccade and muscle artifacts), in addition to residual BCG artifacts. To validate pipeline performance, both resting-state (time/frequency and frequency analysis) and task-based (event related potential [ERP]) EEG data from eight healthy participants were tested. This data was compared with the time/frequency and frequency results achieved by matching meticulously, manually corrected EEG data to the automatically corrected EEG data. No significant difference was found between results. A comparison between ERP results (e.g., amplitude measures and SNR) also showed no differences between manually corrected and fully automated EEG corrected data. The second challenge addressed in this work is the low experimental control over the subject's actual behavior during the eyes-open resting-state fMRI (rsfMRI). This technique has been widely used for studying the (presumably) awake and alert human brain using multimodal EEG-fMRI; however, objective and verified experimental measures to quantify the degree of alertness (e.g., vigilance) are not readily available. To this end, the study reported in this dissertation investigated whether simultaneous multimodal EEG, rsfMRI and eye-tracker experiments could be used to extract objective and robust biomarkers of vigilance in healthy human subjects (n = 10) during cross fixation. Frontal and occipital beta power (FOBP) were found to correlate (r = 0.306, p<0.001) with pupil size fluctuation, which is an indirect index for locus coeruleus activity implicated in vigilance regulation. Moreover, FOBP was also correlated with heart rate (r = 0.255, p<0.001) and several brain regions in an anti-correlated network, including the bilateral insula and inferior parietal lobule. Results support the conclusion that FOBP is an objective and robust biomarker of vigilance in healthy human subjects

Signal processing techniques for extracting signals with periodic structure : applications to biomedical signals

In this dissertation some advanced methods for extracting sources from single and multichannel data are developed and utilized in biomedical applications. It is assumed that the sources of interest have periodic structure and therefore, the periodicity is exploited in various forms. The proposed methods can even be used for the cases where the signals have hidden periodicities, i.e., the periodic behaviour is not detectable from their time representation or even Fourier transform of the signal. For the case of single channel recordings a method based on singular spectrum anal ysis (SSA) of the signal is proposed. The proposed method is utilized in localizing heart sounds in respiratory signals, which is an essential pre-processing step in most of the heart sound cancellation methods. Artificially mixed and real respiratory signals are used for evaluating the method. It is shown that the performance of the proposed method is superior to those of the other methods in terms of false detection. More over, the execution time is significantly lower than that of the method ranked second in performance. For multichannel data, the problem is tackled using two approaches. First, it is assumed that the sources are periodic and the statistical characteristics of periodic sources are exploited in developing a method to effectively choose the appropriate delays in which the diagonalization takes place. In the second approach it is assumed that the sources of interest are cyclostationary. Necessary and sufficient conditions for extractability of the sources are mathematically proved and the extraction algorithms are proposed. Ballistocardiogram (BCG) artifact is considered as the sum of a number of independent cyclostationary components having the same cycle frequency. The proposed method, called cyclostationary source extraction (CSE), is able to extract these components without much destructive effect on the background electroencephalogram (EEG

fMRI BOLD Correlates of EEG Independent Components: Spatial Correspondence With the Default Mode Network

Goal: We aimed to identify electroencephalographic (EEG) signal fluctuations within independent components (ICs) that correlate to spontaneous blood oxygenation level dependent (BOLD) activity in regions of the default mode network (DMN) during eyes-closed resting state.Methods: We analyzed simultaneously acquired EEG and functional magnetic resonance imaging (fMRI) eyes-closed resting state data in a convenience sample of 30 participants. IC analysis (ICA) was used to decompose the EEG time-series and common ICs were identified using data-driven IC clustering across subjects. The IC time courses were filtered into seven frequency bands, convolved with a hemeodynamic response function (HRF) and used to model spontaneous fMRI signal fluctuations across the brain. In parallel, group ICA analysis was used to decompose the fMRI signal into ICs from which the DMN was identified. Frequency and IC cluster associated hemeodynamic correlation maps obtained from the regression analysis were spatially correlated with the DMN. To investigate the reliability of our findings, the analyses were repeated with data collected from the same subjects 1 year later.Results: Our results indicate a relationship between power fluctuations in the delta, theta, beta and gamma frequency range and the DMN in different EEG ICs in our sample as shown by small to moderate spatial correlations at the first measurement (0.234 &lt; |r| &lt; 0.346, p &lt; 0.0001). Furthermore, activity within an EEG component commonly identified as eye movements correlates with BOLD activity within regions of the DMN. In addition, we demonstrate that correlations between EEG ICs and the BOLD signal during rest are in part stable across time.Discussion: We show that ICA source separated EEG signals can be used to investigate electrophysiological correlates of the DMN. The relationship between the eye movement component and the DMN points to a behavioral association between DMN activity and the level of eye movement or the presence of neuronal activity in this component. Previous findings of an association between frontal midline theta activity and the DMN were replicated

Spatio-temporal feature representations of reactivated memories

How does the human brain recover memories of past events? The neural processes of memory retrieval are still not fully uncovered. This doctoral thesis is concerned with the spatio-temporal feature representations of reactivated episodic memories. Classical theories and empirical evidence suggest that the revival of memory representations in the brain is initiated in the hippocampus, before activity patterns in cortical regions reactivate to represent previously experienced events. The current doctoral project tests the assumption that the neural processing cascade during retrieval is reversed with respect to perception. This general framework predicts that semantic concepts and modality-independent information is reconstructed before modality-specific sensory details. This backward information flow is also assumed to affect the neural representations when memories are recalled repeatedly, enhancing the integration of new information into existing conceptual networks. The first two studies investigate the neural information flow during retrieval with respect to the reactivated mnemonic representations. First, simultaneous EEG-fMRI is used to track the presumed reversed reconstruction from abstract modality-independent to sensory-specific visual and auditory memory representations. The second EEG-fMRI project then zooms in on the recall of visual memories, testing whether the visual retrieval process propagates backwards along the ventral visual stream transferring from abstract conceptual to detailed perceptual representations. The reverse reconstruction framework predicts that conceptual information, due to its prioritisation, should benefit more from repeated recall than perceptual information. Hence, the last, behavioural study investigated whether retrieval strengthens conceptual representations over perceptual ones and thus promotes the semanticisation of episodic memories. Altogether, the findings offer novel insights into retrieval-related processing cascades, in terms of their temporal and spatial dynamics and the nature of the reactivated representations. The results also provide an understanding of memory transformations during the consolidation processes that are amplified through repeated retrieval