Ms. Carmen Alicia Carabalí Carabalí Profile

Carmen Alicia Carabalí Carabalí

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Proceedings Article | 22 December 2015 Paper

EEG source analysis of data from paralysed subjects

Carmen A. Carabali, John Willoughby, Sean Fitzgibbon, Tyler Grummett, Trent Lewis, Dylan DeLosAngeles, Kenneth Pope

Proceedings Volume 9681, 968115 (2015) https://doi.org/10.1117/12.2209412

KEYWORDS: Statistical analysis, Electroencephalography, Analytical research, Brain, Signal processing, Fourier transforms, Electromyography, Linear filtering, Motion analysis, Neuroimaging

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One of the limitations of Encephalography (EEG) data is its quality, as it is usually contaminated with electric signal from muscle. This research intends to study results of two EEG source analysis methods applied to scalp recordings taken in paralysis and in normal conditions during the performance of a cognitive task. The aim is to determinate which types of analysis are appropriate for dealing with EEG data containing myogenic components. The data used are the scalp recordings of six subjects in normal conditions and during paralysis while performing different cognitive tasks including the oddball task which is the object of this research. The data were pre-processed by filtering it and correcting artefact, then, epochs of one second long for targets and distractors were extracted. Distributed source analysis was performed in BESA Research 6.0, using its results and information from the literature, 9 ideal locations for source dipoles were identified. The nine dipoles were used to perform discrete source analysis, fitting them to the averaged epochs for obtaining source waveforms. The results were statistically analysed comparing the outcomes before and after the subjects were paralysed. Finally, frequency analysis was performed for better explain the results. The findings were that distributed source analysis could produce confounded results for EEG contaminated with myogenic signals, conversely, statistical analysis of the results from discrete source analysis showed that this method could help for dealing with EEG data contaminated with muscle electrical signal.

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