IEEE J Biomed Health Inform - A three-step multimodal analysis framework for modeling corticomuscular activity with application to Parkinson's disease.

Tópicos

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Resumo

Corticomuscular coupling analysis based on multiple datasets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. A popular conventional method to assess corticomuscular coupling has been the pair-wise magnitude-squared coherence (MSC) between EEG and concomitant EMG recordings. However, there are certain limitations associated with the MSC, including the difficulty in robustly assessing group inference, only dealing with two types of datasets simultaneously and the biologically implausible assumption of pair-wise interactions. To overcome such limitations, in this paper, we propose assessing corticomuscular coupling by combining multiset canonical correlation analysis (M-CCA) and joint independent component analysis (jICA). The proposed method takes advantage of the M-CCA and jICA to ensure that the extracted components are maximally correlated across multiple datasets and meanwhile statistically independent within each dataset. Simulations were performed to illustrate the performance of the proposed method. We also applied the proposed method to concurrent EEG, EMG, and behavior data collected in a Parkinson's disease (PD) study. The results reveal highly correlated temporal patterns among the three types of signals and corresponding spatial activation patterns. In addition to the expected motor areas, the corresponding spatial activation patterns demonstrate enhanced occipital connectivity in the PD subjects, consistent with previous medical findings.

Resumo Limpo

corticomuscular coupl analysi base multipl dataset electroencephalographi eeg electromyographi emg signal provid use tool understand human motor control system popular convent method assess corticomuscular coupl pairwis magnitudesquar coher msc eeg concomit emg record howev certain limit associ msc includ difficulti robust assess group infer deal two type dataset simultan biolog implaus assumpt pairwis interact overcom limit paper propos assess corticomuscular coupl combin multiset canon correl analysi mcca joint independ compon analysi jica propos method take advantag mcca jica ensur extract compon maxim correl across multipl dataset meanwhil statist independ within dataset simul perform illustr perform propos method also appli propos method concurr eeg emg behavior data collect parkinson diseas pd studi result reveal high correl tempor pattern among three type signal correspond spatial activ pattern addit expect motor area correspond spatial activ pattern demonstr enhanc occipit connect pd subject consist previous medic find

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