Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging.

OBJECTIVE: The contamination of muscle and eye artifacts during an ictal period of the EEG significantly distorts source estimation algorithms. Recent blind source separation (BSS) techniques based on canonical correlation (BSS-CCA) and independent component analysis with spatial constraints (SCICA) have shown much promise in the removal of these artifacts. In this study we want to use BSS-CCA and SCICA as a preprocessing step before the source estimation during the ictal period. METHODS: Both the contaminated and cleaned ictal EEG were subjected to the RAP-MUSIC algorithm. This is a multiple dipole source estimation technique based on the separation of the EEG in signal and noise subspace. The source estimates were compared with the subtracted ictal SPECT (iSPECT) coregistered to magnetic resonance imaging (SISCOM) by means of the euclidean distance between the iSPECT activations and the dipole location estimates. SISCOM results in an image denoting the ictal onset zone with a propagation. RESULTS: We applied the artifact removal and the source estimation on 8 patients. Qualitatively, we can see that 5 out of 8 patients show an improvement of the dipoles. The dipoles are nearer to or have tighter clusters near the iSPECT activation. From the median of the distance measure, we could appreciate that 5 out of 8 patients show improvement. CONCLUSIONS: The results show that BSS-CCA and SCICA can be applied to remove artifacts, but the results should be interpreted with care. The results of the source estimation can be misleading due to excessive noise or modeling errors. Therefore, the accuracy of the source estimation can be increased by preprocessing the ictal EEG segment by BSS-CCA and SCICA. SIGNIFICANCE: This is a pilot study where EEG source localization in the presurgical evaluation can be made more reliable, if preprocessing techniques such as BSS-CCA and SCICA are used prior to EEG source analysis on ictal episodes.

Detection of spike and wave discharges in the cortical EEG of genetic absence epilepsy rats from Strasbourg.

Genetic absence epilepsy rats from Strasbourg (GAERS) are a strain of Wistar rats in which all animals present spontaneous occurrence of spike and wave discharges (SWD) in the cortical electroencephalogram (EEG). In this paper, we present a method for the detection of SWD, based on the key observation that SWD are quasi-periodic signals. A spectral-comb based analysis method is used to extract the fundamental frequency and the percentage of energy explained by the harmonic spectral components is subsequently used as a detection parameter. It is shown that a maximum sensitivity and specificity of up to 96 per cent can be achieved. We also compared the performance of this method with the methods presented in the literature and conclude that the surplus value of the novel detection method lies in the higher specificity that can be obtained in the analysis of long-term EEG fragments, which are contaminated by artefacts and contain large portions of slow-wave sleep.