E. M. Allen

A Fundamental Investigation of the Composition of Auditory Evoked Potentials

An investigation, carried out to establish whether auditory evoked potentials (AEPs) are due to phase reordering of the background electroencephalogram or to an additive signal, is described. New phase reordered and additive models of the AEP were introduced and used in conjunction with the techniques of angular statistics. It was established that all of the AEPs studied contained additive energy in at least one harmonic component.

The removal of ocular artefacts from the electroencephalogram: a review

The causes of ocular artefacts (OAs) in the human electroencephalogram (EEG) are explained and methods for their removal and their effectiveness are discussed. Recommendations for the best procedures to adopt are given together with suggestions for future research. Analogue subtraction techniques are found to be inferior to time domain techniques based on parameter estimation using the method of least squares applied to a linear function of the electro-occulograms (EOGs). Ways of assessing the effectiveness of different models for time domain removal of OAs are discussed. It is concluded that autoregressive modelling of the error terms, or else differenced data, must be used to reduce the effects of correlation in the background EEG. The most generally suitable model for the removal of random eye and blink artefacts should contain terms proportional to the right vertical EOG and the two horizontal EOGs. The EOGs should be linearly filtered to remove noise frequency components in excess of 8 Hz. Adaptive methods are preferred as on line OA removal would be desirable but for the fact that this may result in distortion of stimulus-related responses present. A number of difficulties remain and there are some suggestions for future research.

New online method for removing ocular artefacts from EEG signals

A method for online removal of ocular artefacts from the human electroencephalogram (EEG) is described. It uses numerically stable algorithms based on the efficient recursive least-squares algorithm. The method is shown to give similar results to its offline equivalents from which it has been developed. Compared with the present online methods our approach is superior, requiring no subjective manual adjustment and processing all signals digitally. An automatic online microcomputer-based ocular artefact remover has been built and successfully tested.

The Application of Pattern Recognition Techniques to the Contingent Negative Variation for the Differentiation of Subject Categories

Pattern recognition techniques and the statistics of directional data have been applied to a repetitive waveform to differentiate between subject categories. Normal subjects and Huntingtons Chorea patients were distinguished by comparing the patterns in the summarized results of statistical tests applied to the Fourier harmonics of their contingent negative variation (CNV) responses, by the consecutive phase angle variations of the first harmonic of their CNVs, and by their different averaged CNVs. A logic algorithm which may provide the basis of computerized diagnosis, or even prediction of the condition, is described.

Investigation and comparison of some models for removing ocular artefacts from EEG signals

An investigation of ocular artefacts (OAs) in the human electroencephalogram (EEG) to quantify the effectiveness of OA removal and to find the most effective model for removing OAs online is described. In Part 1, the models used in the investigation are described and the data analysed. The analysis showed that the ‘true’ EEG exhibited a high degree of serial correlation and so the ordinary least-squares (OLS) method employed to remove OA was inefficient. Efficient alternative methods based on autoregressive models of the ‘true’ EEG are discussed. It is also shown that the EOGs are linearly dependent making some of them redundant. In Part 2, the models are compared.

Expert system approach to electroencephalogram signal processing

The human electroencephalogram (EEG) is often corrupted by ocular artefacts (OAs) caused by the movement of the eyes and/or the eyelids, making the recognition of abnormal EEG signals more difficult. The removal of OAs using conventional signal processing is complicated by the similarity between abnormal EEGs and OAs, which can lead to corruption of the EEG signal. The paper describes the development of a novel approach that uses expert system techniques to differentiate OAs from genuine EEG signals, enabling OA removal to be applied only where appropriate, and ensuring that clinically relevant EEG information is left unaffected.

Pilot study of computerised differentiation of Huntington's disease, schizophrenic, and Parkinson's disease patients using the contingent negative variation.

In this study a potential known as the contingent negative variation was used to differentiate between schizophrenic, Parkinsons disease (PD), Huntingtons disease (HD) patients and normal control subjects. The aim was to assist diagnosis and the avoidance of false diagnosis. 20 schizophrenic, 16 PD, 11 HD and 43 normal control subjects were enrolled for this study. The discriminatory variables were generated by applying spectral analysis to pre- and post-stimulus sections of the CNV responses. The patient differentiation was achieved by using the measured variables in a discriminant analysis program. It was possible to accurately differentiate between HD, schizophrenic, PD patients and normal control subjects. It was also attempted to differentiate between HD and schizophrenic patients, HD and PD patients, and schizophrenic and PD patients. The test results indicated that the method is useful in differentiating between these patients. This study had a number of limitations. It was based on a limited number of individuals, and an analysis of medication effects on the test results and the test-retest reliability assessment could not be carried out.

Investigation and comparison of some models for removing ocular artefacts from EEG signals. Part 2. Quantitative and pictorial comparison of models.

An investigation of ocular artefacts (OAs) in the human electroencephalogram (EEG) to quantify the effectiveness of OA removal, and to find the most effective model for removing OAs online is described. It was found unnecessary to use the vertical and horizontal EOGs of both eyes, although more than one EOG signal is required for adequate OA removal. The model using the vertical right EOG and the two horizontal EOGs was the best overall, but in most cases the use of only the vertical and horizontal right EOGs was sufficient. OAs were not completely removed by any of the models investigated, suggesting that more complex models are necessary.