DCCA multi cross-correlation analysis applied on EEG signals to study motor

We applied the DCCA multiple cross-correlation coefficient to analyze time series of EEG experiment, where 109 subjects performed four tasks involving real and imaginary motor activities. In this case, four specific channels were selected on the scalp: two on the frontal and two on parietal region. As a result, the DCCA multiple cross-correlation coefficient identified that there is a single signature for each subject. Globally, there is no significant difference between the real and the imaginary task. The frontal channels had greater multiple cross-correlation values than the parietal ones, especially for time scales around 0 . 42 s, and with smaller standard deviations. According to our results, the proposal to study multiple time series at the same time by using the DCCA multiple cross-correlation coefficient is feasible and robust for EEG analysis. Finally, the coefficient was applied in a substantial number of subjects, tasks, and experiments producing high-quality results (figures, movies, and tables), and this study will probably kick off of anew approach to analyzing multiple cross-correlations in EEG signals.