Abstract
Objective. Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which—depending on their extent—may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts. Approach. In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches. Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts. Significance. Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.
Highlights
Electroencephalography (EEG) is a technique that measures electrical potentials over the scalp, which vary over time as a result of neuronal activity in the brain
We propose a novel multi-step Blind source separation (BSS) approach for attenuating ocular, movement and myogenic artifacts, which are typically present in mobile high-density EEG (hdEEG) data (Urigüen and Garcia-Zapirain 2015, Nathan and Contreras-Vidal 2016, Richer et al 2020)
We mainly focused on the development of a method for the attenuation of ocular, movement and myogenic artifacts, which are the typical artifacts that can be found in mobile hdEEG data (Urigüen and Garcia-Zapirain 2015, Nathan and ContrerasVidal 2016, Richer et al 2020)
Summary
Electroencephalography (EEG) is a technique that measures electrical potentials over the scalp, which vary over time as a result of neuronal activity in the brain. Those methods first decompose the EEG data using wavelet coefficients, and reconstruct new signals having decreased artifactual coefficients (Zikov et al 2002, Krishnaveni et al 2006, Bajaj et al 2020) Their performance varies with the choice of mother wavelets, and the criteria used for artifactual coefficient attenuation. Another group of methods uses empirical mode decomposition (EMD) to decompose the EEG data into narrow-band intrinsic mode functions (IMFs), and to reconstruct signals by mixing the IMFs that are not deemed to be artifactual (Zeng et al 2013, Chen et al 2014, Sweeney-Reed et al 2018). The performance of EMD approaches may be limited by the mode mixing problem (Zheng et al 2014)
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