The identification of predominant auditory steady‐state response brain sources in electroencephalography using denoising source separation

• Published in: The European journal of neuroscience Vol. 53; no. 11; pp. 3688 - 3709
• Main Authors: Granados Barbero, Raúl, De Vos, Astrid, Wouters, Jan
• Format: Journal Article
• Language: English
• Published: France Wiley Subscription Services, Inc 01.06.2021
• Subjects: EEG; Electrodes; Electroencephalography; head model; ICA; Information processing; PCA; source analysis; source analysis; head model; ICA; PCA
• ISSN: 0953-816X; 1460-9568; 1460-9568
• DOI: 10.1111/ejn.15219

Different approaches have been used to extract auditory steady‐state responses (ASSRs) from electroencephalography (EEG) recordings, including region‐related electrode configurations (electrode level) and the manual placement of equivalent current dipoles (source level). Inherent limitations of these approaches are the assumption of the anatomical origin and the omission of activity generated by secondary sources. Data‐driven methods such as independent component analysis (ICA) seem to avoid these limitations but only to face new others such as the presence of ASSRs with similar properties in different components and the manual selection protocol to select and classify the most relevant components carrying ASSRs. We propose the novel approach of applying a spatial filter to these components in order to extract the most relevant information. We aimed to develop a method based on the reproducibility across trials that performs reliably in low‐signal‐to‐noise ratio (SNR) scenarios using denoising source separation (DSS). DSS combined with ICA successfully reduced the number of components and extracted the most relevant ASSR at 4, 10 and 20 Hz stimulation in group and individual level studies of EEG adolescent data. The anatomical brain location for these low stimulation frequencies showed results in cortical areas with relatively small dispersion. However, for 40 and 80 Hz, results with regard to the number of components and the anatomical origin were less clear. At all stimulation frequencies the outcome measures were consistent with literature, and the partial rejection of inter‐subject variability led to more accurate results and higher SNRs. These findings are promising for future applications in group comparison involving pathologies. We developed a method based on the reproducibility across trials that performs reliably in low‐SNR scenarios using Denoising Source Separation (DSS). DSS combined with Independent Component Analysis successfully reduced the number of components and extracted the most relevant ASSR at 4‐Hz, 10‐Hz and 20‐Hz stimulation in a group and individual level studies of EEG adolescent data. This algorithm improved the detection thresholds by enhancing SNR and facilitating the component selection procedure.