Influence of signal preprocessing on ICA-based EEG decomposition

Independent Component Analysis (ICA) has been widely used for analysis of EEG data and separating brain and non-brain sources from the EEG mixture. In this study, we compared decomposition results of the most commonly applied ICA algorithms: AMICA, Extended-Infomax, Infomax and FastICA. We examined 12 conditions of EEG data pre-processing, and assessed the independence and physiological plausibility of the recovered components. The results demonstrate that, in general, there were no significant differences in the decomposition results, while data pre-processing choices had a much more pronounced effect. In conclusion the efficiency of the ICA decompositions is highly dependent on the pre-processing steps applied to the EEG data submitted to ICA, rather than type of ICA applied

Conscious perception of natural images is constrained by category-related visual features

Conscious perception is crucial for adaptive behaviour yet access to consciousness varies for different types of objects. The visual system comprises regions with widely distributed category information and exemplar-level representations that cluster according to category. Does this categorical organisation in the brain provide insight into object-specific access to consciousness? We address this question using the Attentional Blink approach with visual objects as targets. We find large differences across categories in the attentional blink. We then employ activation patterns extracted from a deep convolutional neural network to reveal that these differences depend on mid- to high-level, rather than low-level, visual features. We further show that these visual features can be used to explain variance in performance across trials. Taken together, our results suggest that the specific organisation of the higher-tier visual system underlies important functions relevant for conscious perception of differing natural images

Removal of the ballistocardiographic artifact from EEG-fMRI data: a canonical correlation approach

The simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) can give new insights into how the brain functions. However, the strong electromagnetic field of the MR scanner generates artifacts that obscure the EEG and diminish its readability. Among them, the ballistocardiographic artifact (BCGa) that appears on the EEG is believed to be related to blood flow in scalp arteries leading to electrode movements. Average artifact subtraction (AAS) techniques, used to remove the BCGa, assume a deterministic nature of the artifact. This assumption may be too strong, considering the blood flow related nature of the phenomenon. In this work we propose a new method, based on canonical correlation analysis (CCA) and blind source separation (BSS) techniques, to reduce the BCGa from simultaneously recorded EEG–fMRI. We optimized the method to reduce the user’s interaction to a minimum. When tested on six subjects, recorded in 1.5 T or 3 T, the average artifact extracted with BSS–CCA and AAS did not show significant differences, proving the absence of systematic errors. On the other hand, when compared on the basis of intra-subject variability, we found significant differences and better performance of the proposed method with respect to AAS.We demonstrated that our method deals with the intrinsic subject variability specific to the artifact that may cause averaging techniques to fail.status: publishe

Automated identification of ERP peaks through Dynamic Time Warping: an application to developmental dyslexia

Objective: This article proposes a method to automatically identify and label event-related potential (ERP) components with high accuracy and precision. Methods: We present a framework, referred to as peak-picking Dynamic Time Warping (ppDTW), where a priori knowledge about the ERPs under investigation is used to define a reference signal. We developed a combination of peak-picking and Dynamic Time Warping (DTW) that makes the temporal intervals for peak-picking adaptive on the basis of the morphology of the data. We tested the procedure on experimental data recorded from a control group and from children diagnosed with developmental dyslexia. Results: We compared our results with the traditional peak-picking. We demonstrated that our method achieves better performance than peak-picking, with an overall precision, recall and F-score of 93%, 86% and 89%, respectively, versus 93%, 80% and 85% achieved by peak-picking. Conclusion: We showed that our hybrid method outperforms peak-picking, when dealing with data involving several peaks of interest. Significance: The proposed method can reliably identify and label ERP components in challenging event-related recordings, thus assisting the clinician in an objective assessment of amplitudes and latencies of peaks of clinical interest