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Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA.
Javed, Ehtasham; Faye, Ibrahima; Malik, Aamir Saeed; Abdullah, Jafri Malin.
Afiliación
  • Javed E; Center for Intelligent Signal and Imaging Research (CISIR) & Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia. Electronic address: rajaehti1@gmail.com.
  • Faye I; Center for Intelligent Signal and Imaging Research (CISIR) & Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia. Electronic address: ibrahima_faye@utp.edu.my.
  • Malik AS; Center for Intelligent Signal and Imaging Research (CISIR) & Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia. Electronic address: aamir_saeed@utp.edu.my.
  • Abdullah JM; Center for Neuroscience Services and Research (P3Neuro) Health Campus, Universiti Sains Malaysia 16150 Kubang Kerian, Kelantan. Electronic address: brainsciences@gmail.com.
J Neurosci Methods ; 291: 150-165, 2017 11 01.
Article en En | MEDLINE | ID: mdl-28842191
ABSTRACT

BACKGROUND:

Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.

METHODS:

We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.

RESULTS:

The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals. COMPARISON WITH EXISTING

METHODS:

Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.

CONCLUSIONS:

The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Imagen por Resonancia Magnética / Artefactos / Electroencefalografía / Imagen Multimodal Tipo de estudio: Qualitative_research Límite: Adult / Humans Idioma: En Revista: J Neurosci Methods Año: 2017 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Imagen por Resonancia Magnética / Artefactos / Electroencefalografía / Imagen Multimodal Tipo de estudio: Qualitative_research Límite: Adult / Humans Idioma: En Revista: J Neurosci Methods Año: 2017 Tipo del documento: Article