Comparison of single trial back-projected independent components with the averaged waveform for the extraction of biomarkers of auditory P300 EPs

The independent components analysis (ICA) of the auditory P300 evoked responses in the EEG of normal subjects is described. The purpose was to identify any features which might provide the basis for biomarkers for diseases, such as Alzheimer’s disease. Single trial P300s were analysed by ICA, the activations were back-projected to scalp electrodes, many artefactual components were removed automatically, and the back-projected independent components (BICs) were first clustered according to their amplitudes and latencies. Then these primary clusters were secondarily clustered according to the columns of their mixing matrices, which clusters together those BICs with the same scalp topographies and, therefore, source locations. The BICs comprising the P300s had simple shapes, approximating half-sinusoids. Trial- to-trial variations in the BICs were found, which explain why different averages have been reported. Both positive- and also negative-going BICs were identified, some associated with known peaks in the P300 waveform. Artefact-free, single trial P300 waveforms could be constructed from the BICs, but these are probably of less interest than the BICs themselves. The findings demonstrate that neither averaged P300s, nor single trial P300s, are reliable as biomarkers, but rather it will be necessary to investigate the BICs present in a number of single trial realizations.peer-reviewe

To extract the independent components of the evoked potentials in the EEG using ICA

The aim was to develop a reliable method of extracting the independent components of single trial evoked potential (EP) signals to derive features for the subject’s bioprofile, for diagnostic, prognostic, and monitoring purposes. Single trials are of interest, because conventional averaging conceals trial-to-trial variability and hence information. Independent Components Analysis (ICA) is a technique for Blind Source Separation (BSS) to recover N temporally independent source signals s = {s1(t), ... sN(t)} from N linear mixtures (the observations), x = {x1(t), ... xN(t)} obtained by multiplying the matrix of unknown sources s by an unknown mixing matrix A, (x = A.s). ICA seeks a square unmixing matrix W such that s = W.x. Difficulties arise for short duration, relatively low amplitude EPs, which have sparse ICs. The effectiveness of different algorithms was compared. Problems associated with more sources than measurement electrodes and with the generation by the algorithms of artefactual components were investigated. Ways of extracting the true EP components were considered. Component grouping was applied to obtain reliable groups, which could be explored for any clinical interpretations. Here we describe the recommended approach as developed by our virtual research group.peer-reviewe

Digital Signal processing a practical approach

xxiii.760 : 24 cm

Simulating microprocessor systems using occam and a network of transputers

The simulation at component level of microprocessor systems provides a precise technique for evaluating the design of a system with regards to its requirements specification. The paper describes the use of occam to simulate individual microprocessor components, and presents a method of modelling the behaviour of microprocessor bus systems. The possibility of utilising transputers to provide real-time simulation is discussed

Signal processing of the contingent negative variation in schizophrenia using multilayer perceptrons and predictive statistical diagnosis

An event related potential known as the contingent negative variation (CNV) was recorded from two sites from the brains of 20 medicated schizophrenics and 20 normal control subjects. The aim was to apply signal processing, artificial neural networks and statistical techniques to the CNV waveform to improve the understanding of schizophrenia and to develop a neurophysiological technique for its identification and monitoring. CNV recording sites were the vertex and from a point midline approximately 30mm anterior to the vertex (frontal). Three-layer multilayer perceptrons (MLPs) were used to discriminate between the CNV waveforms of the schizophrenics and normal subjects. Although the MLP technique was successful in discrimination, it did not provide a quantitative measure for the analysis. Furthermore, during the test phase it always classified the subjects into one of the two categories and did not provide an output for either type (unknown type). To improve the clinical diagnosis a discrimination technique based on predictive statistical diagnosis (PSD) was developed. The input parameters to the PSD were a time domain feature and three features obtained from the energy spectrum of the CNV waveform. The PSD output indicated the probability and the atypicality index of each subject belonging to one of the two groups. Discrimination accuracy of the PSD was 100% for normal subjects. Three schizophrenics could not be classified into either type, but the rest were identified correctly. T-tests carried out on the recorded CNV waveforms showed that the CNV waveform recorded from the vertex site in normal subjects is significantly different from that recorded from the frontal site; however this was not the case for schizophrenics

The independent components of auditory P300 and CNV evoked potentials derived from single-trial recordings.

The back-projected independent components (BICs) of single-trial, auditory P300 and contingent negative variation (CNV) evoked potentials (EPs) were derived using independent component analysis (ICA) and cluster analysis. The method was tested in simulation including a study of the electric dipole equivalents of the signal sources. P300 data were obtained from healthy and Alzheimer's disease (AD) subjects. The BICs were of approximately 100 ms duration and approximated positive- and negative-going half-sinusoids. Some positively and negatively peaking BICs constituting the P300 coincided with known peaks in the averaged P300. However, there were trial-to-trial differences in their occurrences, particularly where a positive or a negative BIC could occur with the same latency in different trials, a fact which would be obscured by averaging them. These variations resulted in marked differences in the shapes of the reconstructed, artefact-free, single-trial P300s. The latencies of the BIC associated with the P3b peak differed between healthy and AD subjects (p < 0.01). More reliable evidence than that obtainable from single-trial or averaged P300s is likely to be found by studying the properties of the BICs over a number of trials. For the CNV, BICs corresponding to both the orienting and the expectancy components were found

The independent components of auditory P300 and CNV evoked potentials derived from single-trial recordings

Summarization: The back-projected independent components (BICs) of single-trial, auditory P300 and contingent negative variation (CNV) evoked potentials (EPs) were derived using independent component analysis (ICA) and cluster analysis. The method was tested in simulation including a study of the electric dipole equivalents of the signal sources. P300 data were obtained from healthy and Alzheimer's disease (AD) subjects. The BICs were of approximately 100 ms duration and approximated positive- and negative-going half-sinusoids. Some positively and negatively peaking BICs constituting the P300 coincided with known peaks in the averaged P300. However, there were trial-to-trial differences in their occurrences, particularly where a positive or a negative BIC could occur with the same latency in different trials, a fact which would be obscured by averaging them. These variations resulted in marked differences in the shapes of the reconstructed, artefact-free, single-trial P300s. The latencies of the BIC associated with the P3b peak differed between healthy and AD subjects (p < 0.01). More reliable evidence than that obtainable from single-trial or averaged P300s is likely to be found by studying the properties of the BICs over a number of trials. For the CNV, BICs corresponding to both the orienting and the expectancy components were found.Παρουσιάστηκε στο: Physiological Measuremen

To extract the independent components of the evoked potentials in the EEG using ICA

Summarization: The aim was to develop a reliable method of extracting the independent components of single trial evoked potential (EP) signals to derive features for the subject’s bioprofile, for diagnostic, prognostic, and monitoring purposes. Single trials are of interest, because conventional averaging conceals trial-to-trial variability and hence information. Independent Components Analysis (ICA) is a technique for Blind Source Separation (BSS) to recover N temporally independent source signals s = {s1(t), ... sN(t)} from N linear mixtures (the observations), x = {x (t), ... x (t)} 1N 2. obtained by multiplying the matrix of unknown sources s by an unknown mixing matrix A, (x = A.s). ICA seeks a square unmixing matrix W such that s = W.x. Difficulties arise for short duration, relatively low amplitude EPs, which have sparse ICs. The effectiveness of different algorithms was compared. Problems associated with more sources than measurement electrodes and with the generation by the algorithms of artefactual components were investigated. Ways of extracting the true EP components were considered. Component grouping was applied to obtain reliable groups, which could be explored for any clinical interpretations. Here we describe the recommended approach as developed by our virtual research group.Presented on