EEG source analysis of data from paralysed subjects

Carmen A. Carabali; John O. Willoughby; Sean P. Fitzgibbon; Tyler Grummett; Trent Lewis; Dylan DeLosAngeles; Kenneth J. Pope

doi:10.1117/12.2209412

22 December 2015 EEG source analysis of data from paralysed subjects

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

Author Affiliations +

Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 968115 (2015) https://doi.org/10.1117/12.2209412
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador

Abstract

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.

Citation Download Citation

Carmen A. Carabali, John O. Willoughby, Sean P. Fitzgibbon, Tyler Grummett, Trent Lewis, Dylan DeLosAngeles, and Kenneth J. Pope "EEG source analysis of data from paralysed subjects", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 968115 (22 December 2015); https://doi.org/10.1117/12.2209412

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KEYWORDS

Statistical analysis

Electroencephalography

Analytical research

Brain

Signal processing

Fourier transforms

Electromyography

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