https://www.cs.helsinki.fi/u/ahyvarin/teaching/niseminar/papers/EEG_ICA_artifacts.pdf

Extraction of' ocular artefacts from EEG using independent component analysis

Quantitative analysis of sleep EEG data can provide valuable additional information in sleep research. However, analysis of data contaminated by artifacts can lead to spurious results. Thus, the first

Artifact Processing in Computerized Analysis of Sleep EEG – A Review

This work characterizes the dynamics of sleep spindles, observed in electroencephalogram (EEG) recorded from humans during sleep, using both time and frequency domain methods which depend on higher order statistics and spectra. The time domain method combines the use of second- and third-order correlations to reveal information on the stationarity of periodic spindle rhythms to detect transitions between multiple activities. The frequency domain method, based on normalized spectrum and bispectrum, describes frequency interactions associated with nonlinearities occuring in the observed EEG.

/pdf/characterization-of-sleep-spindles-using-higher-order-508x9kkp6w.pdf

Characterization of sleep spindles using higher order statistics and spectra

Independence: a new criterion for the analysis of the electromagnetic fields in the global brain?

Robust removal of ocular artifacts by combining Independent Component Analysis and system identification

Work undertaken to investigate the effect on EEG responses of ocular artefact removal by proportional electro-oculogram subtraction using the least-squares method is reported. The ocular artefact model and the least-squares approach to estimation of the model parameters and hence of the EEG waveform, including the response, are described. Results are presented to show that the response shapes become modified if ocular artefact removal is implemented recursively. This happens because the response is incorrectly treated as part of the random background EEG. The solution is to incorporate a model of the response within the ocular artefact removal algorithm. The results of tests on simulated data confirm these conclusions. The results of the incorporation of response models in removing ocular artefacts from CNV recordings are presented. Even with modelling recursive ocular artefact removal is found to modify the shape of the CNV, and so reliable results can only be obtained if the removal is implemented nonrecursively. Evidence is given that it is advisable to remove DC levels from the recorded data.

Effect on EEG responses of removing ocular artefacts by proportional EOG subtraction.

The techniques used and the results obtained in a spectral analysis of two specific responses in the human electroencephalogram are presented in this paper. The purposes are to show how the techniques may be applied to the necessarily short lengths of EEG data and to illustrate these techniques and the useful results obtained by relevant examples. The necessary data-processing procedures and precautions for transforming from the time to frequency domain are presented in a tutorial fashion. The importance of augmenting zeros, choice of the most appropriate data window and pretransformation of the data to avoid the combined effects of energy loss and low frequency content biasing caused by windowing is explained. The pros and cons of the tapered-cosine (Tukey) and Kaiser-Bessel windows are illustrated. The usefulness of applying certain statistical tests, which are based on a physical model of the responses, to the harmonic components of the responses is demonstrated. Thus a comparison is made between the features of auditory evoked responses and of the contingent negative variation, and the usefulness of predictive statistical diagnosis in differentiating between subject groups is illustrated by application to normal subjects and Huntington's chorea patients.

M. Coelho

Papers

Effect on EEG responses of removing ocular artefacts by proportional EOG subtraction.

Spectral analysis of EEG responses.