Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition
Wang, X., Liu, W., Toiviainen, P., Ristaniemi, T., & Cong, F. (2020). Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition. Journal of Neuroscience Methods, 330, Article 108502. https://doi.org/10.1016/j.jneumeth.2019.108502
Published inJournal of Neuroscience Methods
© 2019 The Author(s).
Background Ongoing EEG data are recorded as mixtures of stimulus-elicited EEG, spontaneous EEG and noises, which require advanced signal processing techniques for separation and analysis. Existing methods cannot simultaneously consider common and individual characteristics among/within subjects when extracting stimulus-elicited brain activities from ongoing EEG elicited by 512-s long modern tango music. New method Aiming to discover the commonly music-elicited brain activities among subjects, we provide a comprehensive framework based on fast double-coupled nonnegative tensor decomposition (FDC-NTD) algorithm. The proposed algorithm with a generalized model is capable of simultaneously decomposing EEG tensors into common and individual components. Results With the proposed framework, the brain activities can be effectively extracted and sorted into the clusters of interest. The proposed algorithm based on the generalized model achieved higher fittings and stronger robustness. In addition to the distribution of centro-parietal and occipito-parietal regions with theta and alpha oscillations, the music-elicited brain activities were also located in the frontal region and distributed in the 4–11 Hz band. Comparison with existing method(s) The present study, by providing a solution of how to separate common stimulus-elicited brain activities using coupled tensor decomposition, has shed new light on the processing and analysis of ongoing EEG data in multi-subject level. It can also reveal more links between brain responses and the continuous musical stimulus. Conclusions The proposed framework based on coupled tensor decomposition can be successfully applied to group analysis of ongoing EEG data, as it can be reliably inferred that those brain activities we obtained are associated with musical stimulus. ...
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Additional information about fundingThis work was supported by the National Natural Science Foundation of China (Grant Nos. 91748105 and 81471742), the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China and the scholarships from China Scholarship Council (Nos. 201706060262 and 201706060263).
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