Extracting the Invisible: Mesial Temporal Source Detection in Simultaneous EEG and SEEG Recordings.

Epileptic source detection relies mainly on visual expertise of scalp EEG signals, but it is recognised that epileptic discharges can escape to this expertise due to a deep localization of the brain sources that induce a very low, even negative, signal to noise ratio. In this methodological study, we aimed to investigate the feasibility of extracting deep mesial temporal sources that were invisible in scalp EEG signals using blind source separation (BSS) methods (infomax ICA, extended infomax ICA, and JADE) combined with a statistical measure (kurtosis). We estimated the effect of different methodological and physiological parameters that could alter or improve the extraction. Using nine well-defined mesial epileptic networks (1949 spikes) obtained from seven patients and simultaneous EEG-SEEG recordings, the first independent component extracted from the scalp EEG signals was validated in mean from 46 to 80% according to the different parameters. The three BSS methods equally performed (no significant difference) and no influence of the number of scalp electrodes used was found. At the opposite, the number and amplitude of spikes included in the averaging before the extraction modified the performance. Anyway, despite their invisibility in scalp EEG signals, this study demonstrates that deep source extraction is feasible under certain conditions and with the use of common signal analysis toolboxes. This finding confirms the crucial need to continue the signal analysis of scalp EEG recordings which contains subcortical signals that escape to expert visual analysis but could be found by signal processing.

3D handheld laser scanner based approach for automatic identification and localization of EEG sensors.

This paper describes and assesses for the first time the use of a handheld 3D laser scanner for scalp EEG sensor localization and co-registration with magnetic resonance images. Study on five subjects showed that the scanner had an equivalent accuracy, a better repeatability, and was faster than the reference electromagnetic digitizer. According to electrical source imaging, somatosensory evoked potentials experiments validated its ability to give precise sensor localization. With our automatic labeling method, the data provided by the scanner could be directly introduced in the source localization studies.