Within-Subject Joint Independent Component Analysis of Simultaneous fMRI/ERP in an Auditory Oddball Paradigm

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. This research aimed to determine the sensitivity and limitations of applying joint independent component analysis (jICA) within-subjects, for ERP and fMRI data collected simultaneously in a parametric auditory frequency oddball paradigm. In a group of 20 subjects, an increase in ERP peak amplitude ranging 1–8 μV in the time window of the P300 (350–700 ms), and a correlated increase in fMRI signal in a network of regions including the right superior temporal and supramarginal gyri, was observed with the increase in deviant frequency difference. JICA of the same ERP and fMRI group data revealed activity in a similar network, albeit with stronger amplitude and larger extent. In addition, activity in the left pre- and post-central gyri, likely associated with right hand somato-motor response, was observed only with the jICA approach. Within-subject, the jICA approach revealed significantly stronger and more extensive activity in the brain regions associated with the auditory P300 than the P300 linear regression analysis. The results suggest that with the incorporation of spatial and temporal information from both imaging modalities, jICA may be a more sensitive method for extracting common sources of activity between ERP and fMRI

Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the application of cortically coupled computer vision to rapid image search. In RSVP, images are presented to participants in a rapid serial sequence which can evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram (EEG). The contemporary approach to this problem involves supervised spatial filtering techniques which are applied for the purposes of enhancing the discriminative information in the EEG data. In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three pipelines without spatial filtering are used as baseline comparison. The Area Under Curve (AUC) is used as an evaluation metric in this paper. The results reveal that MTWLB and xDAWN spatial filtering techniques enhance the classification performance of the pipeline but CSP does not. The results also support the conclusion that LR can be effective for RSVP based BCI if discriminative features are available

A new method to detect event-related potentials based on Pearson\u2019s correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience. Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP\u2019s waveform, these waveforms being time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson\u2019s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase\u2014in consonance with the stimuli\u2014in EEG signal correlation over all channels. This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs. These hidden components seem to be caused by variations (between each successive stimulus) of the ERP\u2019s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology. The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language

Adaptive techniques for the detection and localization of event related potentials from EEGs using reference signals

In this thesis we show the methods we developed for the detection and localisation of P300 signals from the electroencephalogram. We utilised signal processing theory in order to enhance the current methodology. The work done can be applied both to EEG averages and single trial EEG data. We developed a variety of methods dealing with the extraction of the P300 and its subcomponents using independent component analysis and least squares. Moreover, we developed novel localisation methods that localise the desired P300 subcomponent from EEG data. Throughout the thesis the main idea was the use of reference signals, which describe the prior information we have about the sources of interest. The main objective of this thesis is to utilize adaptive techniques, namely blind source separation (BSS), least squares (LS) and spatial filtering, in order to extract the P300 subcomponents from the electroencephalogram (EEG) with greater accuracy than the traditional methods. The first topic of research, is the development of constrained BSS and blind signal extraction (BSE) algorithms, to enhance the estimation of the conventional BSS and BSE algorithms. In these methods we use reference signals as prior information, obtained from real EEG data, to aid BSS and BSE in the extraction of the P300 subcomponents. Although, this method exhibits very good behaviour in terms of EEG averaged data, its performance degrades when applied to single trial data, which is the response of the brain after one single stimulus. The second topic deals with single trial EEG data and is based on least squares. Again, we use reference signals to describe the prior knowledge of the P300 subcomponents. In contrast to the first method, the reference signals are Gaussian spike templates with variable latency and width. The target of this algorithm is to measure the properties of the extracted P300 subcomponents and obtain features that can be used in the classification of schizophrenic patients and healthy subjects. Finally, the idea of spatial filtering combined with the use of a reference signal for localisation is introduced for the first time. The designed algorithm localises our desired source from within a mixture of sources where the propagation model of the sources is available. It performs well in the presence of noise and correlated sources. The research presented in this thesis paves the path in introducing adaptive techniques based on reference signals into ERP estimation. The results have been very promising and provide a big step in establishing a foundation for future research